Patent Publication Number: US-2022222862-A1

Title: Three-dimensional data decoding method and three-dimensional data decoding device

Description:
CROSS REFERENCE TO RELATED APPLICATIONS 
     This application is a U.S. continuation application of PCT International Patent Application Number PCT/JP2020/039049 filed on Oct. 16, 2020, claiming the benefit of priority of U.S. Provisional Patent Application No. 62/923,016 filed on Oct. 18, 2019, the entire contents of which are hereby incorporated by reference. 
    
    
     BACKGROUND 
     1. Technical Field 
     The present disclosure relates to a three-dimensional data decoding method and a three-dimensional data decoding device. 
     2. Description of the Related Art 
     Devices or services utilizing three-dimensional data are expected to find their widespread use in a wide range of fields, such as computer vision that enables autonomous operations of cars or robots, map information, monitoring, infrastructure inspection, and video distribution. Three-dimensional data is obtained through various means including a distance sensor such as a rangefinder, as well as a stereo camera and a combination of a plurality of monocular cameras. 
     Methods of representing three-dimensional data include a method known as a point cloud scheme that represents the shape of a three-dimensional structure by a point cloud in a three-dimensional space. In the point cloud scheme, the positions and colors of a point cloud are stored. While point cloud is expected to be a mainstream method of representing three-dimensional data, a massive amount of data of a point cloud necessitates compression of the amount of three-dimensional data by encoding for accumulation and transmission, as in the case of a two-dimensional moving picture (examples include Moving Picture Experts Group-4 Advanced Video Coding (MPEG-4 AVC) and High Efficiency Video Coding (HEVC) standardized by MPEG). 
     Meanwhile, point cloud compression is partially supported by, for example, an open-source library (Point Cloud Library) for point cloud-related processing. 
     Furthermore, a technique for searching for and displaying a facility located in the surroundings of the vehicle by using three-dimensional map data is known (for example, see Patent Literature (PTL) 1). 
     CITATION LIST 
     Patent Literature 
     PTL 1: International Publication WO 2014/020663 
     SUMMARY 
     In encoded three-dimensional data (a plurality of three-dimensional points), it is desirable that a plurality of desired encoded three-dimensional points can be appropriately selected from the plurality of encoded three-dimensional points and decoded. 
     The present disclosure provides a three-dimensional data decoding method or a three-dimensional data decoding device that can appropriately select a plurality of desired encoded three-dimensional points from a plurality of encoded three-dimensional points and decode the selected encoded three-dimensional points. 
     A three-dimensional data decoding method according to an aspect of the present disclosure includes: obtaining three-dimensional space information including encoded three-dimensional points; calculating an angle formed by a line segment connecting a predetermined position in the three-dimensional space information and a first base point and a line segment connecting the first base point and a second base point different from the first base point; determining whether the angle calculated satisfies a predetermined condition; and decoding the encoded three-dimensional points included in the three-dimensional space information when the angle is determined to satisfy the predetermined condition, and not decoding the encoded three-dimensional points included in the three-dimensional space information when the angle is determined not to satisfy the predetermined condition. 
     The present disclosure can provide a three-dimensional data decoding method or a three-dimensional data decoding device that can appropriately select a plurality of desired encoded three-dimensional points from a plurality of encoded three-dimensional points and decode the selected encoded three-dimensional points. 
    
    
     
       BRIEF DESCRIPTION OF DRAWINGS 
       These and other objects, advantages and features of the disclosure will become apparent from the following description thereof taken in conjunction with the accompanying drawings that illustrate a specific embodiment of the present disclosure. 
         FIG. 1  is a diagram illustrating a configuration of a three-dimensional data encoding and decoding system according to Embodiment 1; 
         FIG. 2  is a diagram illustrating a structure example of point cloud data according to Embodiment 1;  FIG. 3  is a diagram illustrating a structure example of a data file indicating the point cloud data according to Embodiment 1; 
         FIG. 4  is a diagram illustrating types of the point cloud data according to Embodiment 1; 
         FIG. 5  is a diagram illustrating a structure of a first encoder according to Embodiment 1; 
         FIG. 6  is a block diagram illustrating the first encoder according to Embodiment 1; 
         FIG. 7  is a diagram illustrating a structure of a first decoder according to Embodiment 1; 
         FIG. 8  is a block diagram illustrating the first decoder according to Embodiment 1; 
         FIG. 9  is a block diagram of a three-dimensional data encoding device according to Embodiment 1; 
         FIG. 10  is a diagram showing an example of geometry information according to Embodiment 1; 
         FIG. 11  is a diagram showing an example of an octree representation of geometry information according to Embodiment 1; 
         FIG. 12  is a block diagram of a three-dimensional data decoding device according to Embodiment 1; 
         FIG. 13  is a block diagram of an attribute information encoder according to Embodiment 1; 
         FIG. 14  is a block diagram of an attribute information decoder according to Embodiment 1; 
         FIG. 15  is a block diagram showing a configuration of the attribute information encoder according to the variation of Embodiment 1; 
         FIG. 16  is a block diagram of the attribute information encoder according to Embodiment 1; 
         FIG. 17  is a block diagram showing a configuration of the attribute information decoder according to the variation of Embodiment 1; 
         FIG. 18  is a block diagram of the attribute information decoder according to Embodiment 1; 
         FIG. 19  is a diagram illustrating a structure of a second encoder according to Embodiment 1; 
         FIG. 20  is a block diagram illustrating the second encoder according to Embodiment 1; 
         FIG. 21  is a diagram illustrating a structure of a second decoder according to Embodiment 1; 
         FIG. 22  is a block diagram illustrating the second decoder according to Embodiment 1; 
         FIG. 23  is a diagram illustrating a protocol stack related to PCC encoded data according to Embodiment 1; 
         FIG. 24  is a diagram illustrating structures of an encoder and a multiplexer according to Embodiment 2; 
         FIG. 25  is a diagram illustrating a structure example of encoded data according to Embodiment 2; 
         FIG. 26  is a diagram illustrating a structure example of encoded data and a NAL unit according to Embodiment 2; 
         FIG. 27  is a diagram illustrating a semantics example of pcc_nal_unit_type according to Embodiment 2; 
         FIG. 28  is a block diagram of a first encoder according to Embodiment 3; 
         FIG. 29  is a block diagram of a first decoder according to Embodiment 3; 
         FIG. 30  is a block diagram of a divider according to Embodiment 3; 
         FIG. 31  is a diagram illustrating an example of dividing slices and tiles according to Embodiment 3; 
         FIG. 32  is a diagram illustrating dividing pattern examples of slices and tiles according to Embodiment 3; 
         FIG. 33  is a diagram illustrating an example of dependency according to Embodiment 3; 
         FIG. 34  is a diagram illustrating a data decoding order according to Embodiment 3; 
         FIG. 35  is a flowchart of encoding processing according to Embodiment 3; 
         FIG. 36  is a block diagram of a combiner according to Embodiment 3; 
         FIG. 37  is a diagram illustrating a structure example of encoded data and a NAL unit according to Embodiment 3; 
         FIG. 38  is a flowchart of encoding processing according to Embodiment 3; 
         FIG. 39  is a flowchart of decoding processing according to Embodiment 3; 
         FIG. 40  is a diagram illustrating an example of syntax of tile additional information according to Embodiment 4; 
         FIG. 41  is a block diagram of an encoding and decoding system according to Embodiment 4; 
         FIG. 42  is a diagram illustrating an example of syntax of slice additional information according to Embodiment 4; 
         FIG. 43  is a flowchart of an encoding process according to Embodiment 4; 
         FIG. 44  is a flowchart of a decoding process according to Embodiment 4;  FIG. 45  is a diagram illustrating examples of a division method according to Embodiment 5; 
         FIG. 46  is a diagram illustrating an example of dividing point cloud data according to Embodiment 5; 
         FIG. 47  is a diagram illustrating an example of syntax of tile additional information according to Embodiment 5; 
         FIG. 48  is a diagram illustrating an example of index information according to Embodiment 5; 
         FIG. 49  is a diagram illustrating an example of dependency relationships according to Embodiment 5; 
         FIG. 50  is a diagram illustrating an example of transmitted data according to Embodiment 5; 
         FIG. 51  is a diagram illustrating a structural example of NAL units according to Embodiment 5; 
         FIG. 52  is a diagram illustrating an example of dependency relationships according to Embodiment 5; 
         FIG. 53  is a diagram illustrating an example of decoding order of data according to Embodiment 5; 
         FIG. 54  is a diagram illustrating an example of dependency relationships according to Embodiment 5; 
         FIG. 55  is a diagram illustrating an example of decoding order of data according to Embodiment 5; 
         FIG. 56  is a flowchart of an encoding process according to Embodiment 5; 
         FIG. 57  is a flowchart of a decoding process according to Embodiment 5; 
         FIG. 58  is a flowchart of an encoding process according to Embodiment 5; 
         FIG. 59  is a flowchart of an encoding process according to Embodiment 5; 
         FIG. 60  is a diagram illustrating an example of transmitted data and an example of received data according to Embodiment 5; 
         FIG. 61  is a flowchart of a decoding process according to Embodiment 5; 
         FIG. 62  is a diagram illustrating an example of transmitted data and an example of received data according to Embodiment 5; 
         FIG. 63  is a flowchart of a decoding process according to Embodiment 5; 
         FIG. 64  is a flowchart of an encoding process according to Embodiment 5; 
         FIG. 65  is a diagram illustrating an example of index information according to Embodiment 5; 
         FIG. 66  is a diagram illustrating an example of dependency relationships according to Embodiment 5; 
         FIG. 67  is a diagram illustrating an example of transmitted data according to Embodiment 5; 
         FIG. 68  is a diagram illustrating an example of transmitted data and an example of received data according to Embodiment 5; 
         FIG. 69  is a flowchart of a decoding process according to Embodiment 5; 
         FIG. 70  is a diagram showing a syntax example of GPS according to Embodiment 6; 
         FIG. 71  is a flowchart of a three-dimensional data decoding process according to Embodiment 6; 
         FIG. 72  is a diagram showing an example of an application according to Embodiment 6; 
         FIG. 73  is a diagram showing an example of tile division and slice division according to Embodiment 6; 
         FIG. 74  is a flowchart showing a process performed by a system according to Embodiment 6; 
         FIG. 75  is a flowchart showing a process performed by the system according to Embodiment 6; 
         FIG. 76  is a block diagram of a three-dimensional data encoding device according to Embodiment 7; 
         FIG. 77  is a block diagram of a three-dimensional data decoding device according to Embodiment 7; 
         FIG. 78  is a block diagram of a three-dimensional data encoding device according to Embodiment 7; 
         FIG. 79  is a block diagram showing a configuration of a three-dimensional data decoding device according to Embodiment 7; 
         FIG. 80  is a diagram showing an example of point cloud data according to Embodiment 7; 
         FIG. 81  is a diagram showing an example of a normal vector for each point according to Embodiment 7; 
         FIG. 82  is a diagram showing a syntax example of a normal vector according to Embodiment 7; 
         FIG. 83  is a flowchart of a three-dimensional data encoding process according to Embodiment 7; 
         FIG. 84  is a flowchart of a three-dimensional data decoding process according to Embodiment 7; 
         FIG. 85  is a diagram showing an example configuration of a bitstream according to Embodiment 7; 
         FIG. 86  is a diagram showing an example of point cloud information according to Embodiment 7; 
         FIG. 87  is a flowchart of a three-dimensional data encoding process according to Embodiment 7; 
         FIG. 88  is a flowchart of a three-dimensional data decoding process according to Embodiment 7; 
         FIG. 89  is a diagram showing an example of normal vector division according to Embodiment 7; 
         FIG. 90  is a diagram showing an example of normal vector division according to Embodiment 7; 
         FIG. 91  is a diagram showing an example of point cloud data according to Embodiment 7; 
         FIG. 92  is a diagram showing an example of normal vectors according to Embodiment 7; 
         FIG. 93  is a diagram showing an example of normal vector information according to Embodiment 7; 
         FIG. 94  is a diagram showing an example of a cube according to Embodiment 7; 
         FIG. 95  is a diagram showing an example of faces of the cube according to Embodiment 7; 
         FIG. 96  is a diagram showing an example of faces of the cube according to Embodiment 7; 
         FIG. 97  is a diagram showing an example of faces of the cube according to Embodiment 7; 
         FIG. 98  is a diagram showing an example of the visibility of a slice according to Embodiment 7; 
         FIG. 99  is a diagram showing an example configuration of a bitstream according to Embodiment 7; 
         FIG. 100  is a diagram showing a syntax example of a slice header of geometry information according to Embodiment 7; 
         FIG. 101  is a diagram showing a syntax example of a slice header of geometry information according to Embodiment 7; 
         FIG. 102  is a flowchart of a three-dimensional data encoding process according to Embodiment 7; 
         FIG. 103  is a flowchart of a three-dimensional data decoding process according to Embodiment 7; 
         FIG. 104  is a flowchart of a three-dimensional data decoding process according to Embodiment 7; 
         FIG. 105  is a diagram showing an example configuration of a bitstream according to Embodiment 7; 
         FIG. 106  is a diagram showing a syntax example of slice information according to Embodiment 7; 
         FIG. 107  is a diagram showing a syntax example of slice information according to Embodiment 7; 
         FIG. 108  is a flowchart of a three-dimensional data decoding process according to Embodiment 7; 
         FIG. 109  is a diagram showing an example of a partial decoding process according to Embodiment 7; 
         FIG. 110  is a diagram showing an example configuration of a three-dimensional data decoding device according to Embodiment 7; 
         FIG. 111  is a diagram showing an example process performed by a random access controller according to Embodiment 7; 
         FIG. 112  is a diagram showing an example process performed by the random access controller according to Embodiment 7; 
         FIG. 113  is a diagram showing an example of a relationship between distance and resolution according to Embodiment 7; 
         FIG. 114  is a diagram showing an example of bricks and normal vectors according to Embodiment 7; 
         FIG. 115  is a diagram showing an example of levels according to Embodiment 7; 
         FIG. 116  is a diagram showing an example of an octree structure according to Embodiment 7; 
         FIG. 117  is a flowchart of a three-dimensional data decoding process according to Embodiment 7; 
         FIG. 118  is a flowchart of a three-dimensional data decoding process according to Embodiment 7; 
         FIG. 119  is a diagram showing an example of a brick to be decoded according to Embodiment 7; 
         FIG. 120  is a diagram showing an example of levels to be decoded according to Embodiment 7; 
         FIG. 121  is a diagram showing a syntax example of a slice header of geometry information according to Embodiment 7; 
         FIG. 122  is a flowchart of a three-dimensional data encoding process according to Embodiment 7; 
         FIG. 123  is a flowchart of a three-dimensional data decoding process according to Embodiment 7; 
         FIG. 124  is a diagram showing an example of point cloud data according to Embodiment 7; 
         FIG. 125  is a diagram showing an example of point cloud data according to Embodiment 7; 
         FIG. 126  is a diagram showing an example configuration of a system according to Embodiment 7; 
         FIG. 127  is a diagram showing an example configuration of a system according to Embodiment 7; 
         FIG. 128  is a diagram showing an example configuration of a system according to Embodiment 7; 
         FIG. 129  is a diagram showing an example configuration of a system according to Embodiment 7; 
         FIG. 130  is a diagram showing an example configuration of a bitstream according to Embodiment 7; 
         FIG. 131  is a diagram showing an example configuration of a three-dimensional data encoding device according to Embodiment 7; 
         FIG. 132  is a diagram showing an example configuration of a three-dimensional data decoding device according to Embodiment 7; 
         FIG. 133  is a diagram showing a basic structure of ISOBMFF according to Embodiment 7; 
         FIG. 134  is a diagram showing a protocol stack in a case where a common PCC codec NAL unit is stored in ISOBMFF according to Embodiment 7; 
         FIG. 135  is a diagram showing an example of a transform of a bitstream into a file format according to Embodiment 7; 
         FIG. 136  is a diagram showing a syntax example of slice information according to Embodiment 7; 
         FIG. 137  is a diagram showing a syntax example of a PCC random access table according to Embodiment 7; 
         FIG. 138  is a diagram showing a syntax example of the PCC random access table according to Embodiment 7; 
         FIG. 139  is a diagram showing a syntax example of the PCC random access table according to Embodiment 7; 
         FIG. 140  is a flowchart of a three-dimensional data encoding process according to embodiment 7; 
         FIG. 141  is a flowchart of a three-dimensional data decoding process according to embodiment 7; 
         FIG. 142  is a flowchart of a three-dimensional data encoding process according to embodiment 7; 
         FIG. 143  is a flowchart of a three-dimensional data decoding process according to embodiment 7; 
         FIG. 144  is a block diagram of a three-dimensional data creation device according to Embodiment 8; 
         FIG. 145  is a flowchart of a three-dimensional data creation method according to Embodiment 8; 
         FIG. 146  is a diagram showing a structure of a system according to Embodiment 8; 
         FIG. 147  is a block diagram of a client device according to Embodiment 8; 
         FIG. 148  is a block diagram of a server according to Embodiment 8; 
         FIG. 149  is a flowchart of a three-dimensional data creation process performed by the client device according to Embodiment 8; 
         FIG. 150  is a flowchart of a sensor information transmission process performed by the client device according to Embodiment 8; 
         FIG. 151  is a flowchart of a three-dimensional data creation process performed by the server according to Embodiment 8; 
         FIG. 152  is a flowchart of a three-dimensional map transmission process performed by the server according to Embodiment 8; 
         FIG. 153  is a diagram showing a structure of a variation of the system according to Embodiment 8; 
         FIG. 154  is a diagram showing a structure of the server and client devices according to Embodiment 8; 
         FIG. 155  is a diagram illustrating a configuration of a server and a client device according to Embodiment 8; 
         FIG. 156  is a flowchart of a process performed by the client device according to Embodiment 8; 
         FIG. 157  is a diagram illustrating a configuration of a sensor information collection system according to Embodiment 8; 
         FIG. 158  is a diagram illustrating an example of a system according to Embodiment 8; 
         FIG. 159  is a diagram illustrating a variation of the system according to Embodiment 8; 
         FIG. 160  is a flowchart illustrating an example of an application process according to Embodiment 8; 
         FIG. 161  is a diagram illustrating the sensor range of various sensors according to Embodiment 8; 
         FIG. 162  is a diagram illustrating a configuration example of an automated driving system according to Embodiment 8; 
         FIG. 163  is a diagram illustrating a configuration example of a bitstream according to Embodiment 8; 
         FIG. 164  is a flowchart of a point cloud selection process according to Embodiment 8; 
         FIG. 165  is a diagram illustrating a screen example for point cloud selection process according to Embodiment 8; 
         FIG. 166  is a diagram illustrating a screen example of the point cloud selection process according to Embodiment 8; 
         FIG. 167  is a diagram illustrating a screen example of the point cloud selection process according to Embodiment 8; 
         FIG. 168  is a diagram for describing the center position of a tile according to Embodiment 9; 
         FIG. 169  is a block diagram for describing a process of decoding an encoded three-dimensional point cloud according to Embodiment 9; 
         FIG. 170  is a diagram for describing a process of calculating angle information used for determination of visibility by a three-dimensional data decoding device according to Embodiment 9; 
         FIG. 171  is a block diagram illustrating the configuration for specifying a decoding target according to Embodiment 9; 
         FIG. 172  is a flowchart illustrating a process of determining the decoding target by the three-dimensional data decoding device according to Embodiment 9; 
         FIG. 173  is a diagram for describing a first example of a decoding process by the three-dimensional data decoding device according to Embodiment 9; 
         FIG. 174  is a diagram for describing a second example of the decoding process by the three-dimensional data decoding device according to Embodiment 9; 
         FIG. 175  is a diagram for describing a third example of the decoding process by the three-dimensional data decoding device according to Embodiment 9; 
         FIG. 176  is a diagram for describing a fourth example of the decoding process by the three-dimensional data decoding device according to Embodiment 9; 
         FIG. 177  is a diagram for describing the decoding process by a three-dimensional data decoding device according to Variation 1 of Embodiment 9; 
         FIG. 178  is a diagram for describing a process of calculating angle information used for determination of visibility by the three-dimensional data decoding device according to Variation 1 of Embodiment 9; 
         FIG. 179  is a flowchart illustrating a process of determining resolution based on the angle information by a three-dimensional data decoding device according to Variation 2 of Embodiment 9; and 
         FIG. 180  is a flowchart illustrating the decoding process by the three-dimensional data decoding devices according to Embodiment 9 and the Variations. 
     
    
    
     DETAILED DESCRIPTION OF THE EMBODIMENTS 
     A three-dimensional data decoding method according to an aspect of the present disclosure includes: obtaining a tile (three-dimensional space information) including encoded three-dimensional points; calculating an angle formed by a line segment connecting a predetermined position in the tile and a first base point and a line segment connecting the first base point and a second base point different from the first base point; determining whether the angle calculated satisfies a predetermined condition; and decoding the encoded three-dimensional points included in the tile when the angle is determined to satisfy the predetermined condition, and not decoding the encoded three-dimensional points included in the tile when the angle is determined not to satisfy the predetermined condition. 
     For example, the three-dimensional data decoding device sequentially decodes encoded three-dimensional points in the order of the data included in a bitstream (encoded data) including three-dimensional points encoded by a three-dimensional data encoding device. Here, in the case where three-dimensional data represents a three-dimensional map, for example, when a user consults the map, the user often wants to see a map of a particular direction from the current position of the user. In such a case, if the three-dimensional data decoding device sequentially decodes the encoded three-dimensional points in the order of the data included in the bitstream, and sequentially displays images representing three-dimensional points on a display device or the like in the order of decoding, it may take a long time to display the part that the user wants to check on the display device. In addition, depending on the current position of the user, there may be three-dimensional points that need not be decoded. In view of this, in the three-dimensional data decoding method according to the present disclosure, the line of sight direction of a camera, a user, or the like, is assumed using the positional relationship between a tile and any two points such as a position of the camera, the user, or the like, and a focal point position of the camera, the user, or the like, and an encoded tile located in the line of sight direction is decoded. Accordingly, an encoded tile located in a region that is likely to be desired by the user can be decoded. Specifically, the three-dimensional data decoding method according to the present disclosure can appropriately select and decode a desired encoded tile (i.e., three-dimensional points) among a plurality of encoded tiles. 
     Furthermore, for example, the determining of whether the angle calculated satisfies the predetermined condition includes determining whether the angle is less than a predetermined angle. The decoding of the encoded three-dimensional points included in the tile includes: decoding the encoded three-dimensional points included in the tile when the angle is less than the predetermined angle; and not decoding the encoded three-dimensional points included in the tile when the angle is greater than or equal to the predetermined angle. 
     Accordingly, a tile located in the range determined by the user can be appropriately decoded according to the view angle of the camera, or the like, by appropriately setting the predetermined angle. 
     Furthermore, for example, the decoding of the encoded three-dimensional points included in the tile includes: determining a resolution of the encoded three-dimensional points based on the angle; and decoding the encoded three-dimensional points according to the resolution determined. 
     For example, there is a high possibility that the center portion of the user&#39;s field of view is more important for the user than the outer edge portion of the user&#39;s field of view. Therefore, for example, according to the calculated angle, tiles are decoded such that the resolution of a tile located in the center portion of the field of view of the camera, the user, or the like is increased, and the resolution of a tile located in the outer edge portion of the field of view of the camera, the user, or the like is decreased. Accordingly, the processing amount can be reduced by decreasing the resolution of a tile that is highly likely to be relatively unimportant for the user, while increasing the resolution of a tile that can be relatively important for the user. 
     Furthermore, for example, the first base point indicates a position of a camera, and the second base point indicates a focal point position of the camera. 
     Alternatively, for example, the second base point indicates a position of a camera, and the first base point indicates a focal point position of the camera. 
     Accordingly, for example, it is possible to decode a tile in a position determined by the position of a camera and the shooting direction of the camera in the case of actual shooting by the camera. For this reason, for example, it is possible to appropriately select and decode a tile for generating a three-dimensional image (three-dimensional points) according to an image obtained from the camera. 
     Furthermore, for example, the calculating of the angle includes calculating the angle by calculating an inner product of a vector from the first base point to the predetermined position and a vector from the first base point to the second base point. 
     Accordingly, the angle can be calculated using a simple process. 
     Furthermore, for example, the predetermined position is a position identified by smallest coordinate values in the tile in a coordinate space in which the tile is located, a center position of the tile, or a center of gravity of the tile. 
     Specifically, for the position of the tile, it is desirable to adopt a position that is included in the tile and can be easily calculated. 
     Furthermore, a three-dimensional data decoding device according to an aspect of the present disclosure includes a processor and memory. Using the memory, the processor: obtains a tile including encoded three-dimensional points; calculates an angle formed by a line segment connecting a predetermined position in the tile and a first base point and a line segment connecting the first base point and a second base point different from the first base point; determines whether the angle calculated satisfies a predetermined condition; and decodes the encoded three-dimensional points included in the tile when the angle is determined to satisfy the predetermined condition, and does not decode the encoded three-dimensional points included in the tile when the angle is determined not to satisfy the predetermined condition. 
     For example, the three-dimensional data decoding device sequentially decodes encoded three-dimensional points in the order of the data included in a bitstream (encoded data) including three-dimensional points encoded by a three-dimensional data encoding device. Here, in the case where three-dimensional data represents a three-dimensional map, for example, when a user consults the map, the user often wants to see a map of a particular direction from the current position of the user. In such a case, if the three-dimensional data decoding device sequentially decodes the encoded three-dimensional points in the order of the data included in the bitstream, and sequentially displays images representing three-dimensional points on a display device or the like in the order of decoding, it may take a long time to display the part that the user wants to check on the display device. In addition, depending on the current position of the user, there may be three-dimensional points that need not be decoded. In view of this, in the three-dimensional data decoding device according to the present disclosure, the line of sight direction of a camera, a user, or the like, is assumed using the positional relationship between a tile and any two points such as a position of the camera, the user, or the like, and a focal point position of the camera, the user, or the like, and an encoded tile located in the line of sight direction is decoded. Accordingly, the three-dimensional data decoding device can decode an encoded tile located in a region that is likely to be desired by the user. Specifically, the three-dimensional data decoding device according to the present disclosure can appropriately select and decode a desired encoded tile (i.e., three-dimensional points) among a plurality of encoded tiles. 
     It is to be noted that these general or specific aspects may be implemented as a system, a method, an integrated circuit, a computer program, or a computer-readable recording medium such as a CD-ROM, or may be implemented as any combination of a system, a method, an integrated circuit, a computer program, and a recording medium. 
     Hereinafter, embodiments will be specifically described with reference to the drawings. It is to be noted that each of the following embodiments indicate a specific example of the present disclosure. The numerical values, shapes, materials, constituent elements, the arrangement and connection of the constituent elements, steps, the processing order of the steps, etc., indicated in the following embodiments are mere examples, and thus are not intended to limit the present disclosure. Among the constituent elements described in the following embodiments, constituent elements not recited in any one of the independent claims that indicate the broadest concepts will be described as optional constituent elements. 
     Embodiment 1 
     When using encoded data of a point cloud in a device or for a service in practice, required information for the application is desirably transmitted and received in order to reduce the network bandwidth. However, conventional encoding structures for three-dimensional data have no such a function, and there is also no encoding method for such a function. 
     Embodiment 1 described below relates to a three-dimensional data encoding method and a three-dimensional data encoding device for encoded data of a three-dimensional point cloud that provides a function of transmitting and receiving required information for an application, a three-dimensional data decoding method and a three-dimensional data decoding device for decoding the encoded data, a three-dimensional data multiplexing method for multiplexing the encoded data, and a three-dimensional data transmission method for transmitting the encoded data. 
     In particular, at present, a first encoding method and a second encoding method are under investigation as encoding methods (encoding schemes) for point cloud data. However, there is no method defined for storing the configuration of encoded data and the encoded data in a system format. Thus, there is a problem that an encoder cannot perform an MUX process (multiplexing), transmission, or accumulation of data. 
     In addition, there is no method for supporting a format that involves two codecs, the first encoding method and the second encoding method, such as point cloud compression (PCC). 
     With regard to this embodiment, a configuration of PCC-encoded data that involves two codecs, a first encoding method and a second encoding method, and a method of storing the encoded data in a system format will be described. 
     A configuration of a three-dimensional data (point cloud data) encoding and decoding system according to this embodiment will be first described.  FIG. 1  is a diagram showing an example of a configuration of the three-dimensional data encoding and decoding system according to this embodiment. As shown in  FIG. 1 , the three-dimensional data encoding and decoding system includes three-dimensional data encoding system  4601 , three-dimensional data decoding system  4602 , sensor terminal  4603 , and external connector  4604 . 
     Three-dimensional data encoding system  4601  generates encoded data or multiplexed data by encoding point cloud data, which is three-dimensional data. Three-dimensional data encoding system  4601  may be a three-dimensional data encoding device implemented by a single device or a system implemented by a plurality of devices. The three-dimensional data encoding device may include a part of a plurality of processors included in three-dimensional data encoding system  4601 . 
     Three-dimensional data encoding system  4601  includes point cloud data generation system  4611 , presenter  4612 , encoder  4613 , multiplexer  4614 , input/output unit  4615 , and controller  4616 . Point cloud data generation system  4611  includes sensor information obtainer  4617 , and point cloud data generator  4618 . 
     Sensor information obtainer  4617  obtains sensor information from sensor terminal  4603 , and outputs the sensor information to point cloud data generator  4618 . Point cloud data generator  4618  generates point cloud data from the sensor information, and outputs the point cloud data to encoder  4613 . 
     Presenter  4612  presents the sensor information or point cloud data to a user. For example, presenter  4612  displays information or an image based on the sensor information or point cloud data. 
     Encoder  4613  encodes (compresses) the point cloud data, and outputs the resulting encoded data, control information (signaling information) obtained in the course of the encoding, and other additional information to multiplexer  4614 . The additional information includes the sensor information, for example. 
     Multiplexer  4614  generates multiplexed data by multiplexing the encoded data, the control information, and the additional information input thereto from encoder  4613 . A format of the multiplexed data is a file format for accumulation or a packet format for transmission, for example. 
     Input/output unit  4615  (a communication unit or interface, for example) outputs the multiplexed data to the outside. Alternatively, the multiplexed data may be accumulated in an accumulator, such as an internal memory. Controller  4616  (or an application executor) controls each processor. That is, controller  4616  controls the encoding, the multiplexing, or other processing. 
     Note that the sensor information may be input to encoder  4613  or multiplexer  4614 . Alternatively, input/output unit  4615  may output the point cloud data or encoded data to the outside as it is. 
     A transmission signal (multiplexed data) output from three-dimensional data encoding system  4601  is input to three-dimensional data decoding system  4602  via external connector  4604 . 
     Three-dimensional data decoding system  4602  generates point cloud data, which is three-dimensional data, by decoding the encoded data or multiplexed data. Note that three-dimensional data decoding system  4602  may be a three-dimensional data decoding device implemented by a single device or a system implemented by a plurality of devices. The three-dimensional data decoding device may include a part of a plurality of processors included in three-dimensional data decoding system  4602 . 
     Three-dimensional data decoding system  4602  includes sensor information obtainer  4621 , input/output unit  4622 , demultiplexer  4623 , decoder  4624 , presenter  4625 , user interface  4626 , and controller  4627 . 
     Sensor information obtainer  4621  obtains sensor information from sensor terminal  4603 . 
     Input/output unit  4622  obtains the transmission signal, decodes the transmission signal into the multiplexed data (file format or packet), and outputs the multiplexed data to demultiplexer  4623 . 
     Demultiplexer  4623  obtains the encoded data, the control information, and the additional information from the multiplexed data, and outputs the encoded data, the control information, and the additional information to decoder  4624 . 
     Decoder  4624  reconstructs the point cloud data by decoding the encoded data. 
     Presenter  4625  presents the point cloud data to a user. For example, presenter  4625  displays information or an image based on the point cloud data. User interface  4626  obtains an indication based on a manipulation by the user. Controller  4627  (or an application executor) controls each processor. That is, controller  4627  controls the demultiplexing, the decoding, the presentation, or other processing. 
     Note that input/output unit  4622  may obtain the point cloud data or encoded data as it is from the outside. Presenter  4625  may obtain additional information, such as sensor information, and present information based on the additional information. Presenter  4625  may perform a presentation based on an indication from a user obtained on user interface  4626 . 
     Sensor terminal  4603  generates sensor information, which is information obtained by a sensor. Sensor terminal  4603  is a terminal provided with a sensor or a camera. For example, sensor terminal  4603  is a mobile body, such as an automobile, a flying object, such as an aircraft, a mobile terminal, or a camera. 
     Sensor information that can be generated by sensor terminal  4603  includes (1) the distance between sensor terminal  4603  and an object or the reflectance of the object obtained by LiDAR, a millimeter wave radar, or an infrared sensor or (2) the distance between a camera and an object or the reflectance of the object obtained by a plurality of monocular camera images or a stereo-camera image, for example. The sensor information may include the posture, orientation, gyro (angular velocity), position (GPS information or altitude), velocity, or acceleration of the sensor, for example. The sensor information may include air temperature, air pressure, air humidity, or magnetism, for example. 
     External connector  4604  is implemented by an integrated circuit (LSI or IC), an external accumulator, communication with a cloud server via the Internet, or broadcasting, for example. 
     Next, point cloud data will be described.  FIG. 2  is a diagram showing a configuration of point cloud data.  FIG. 3  is a diagram showing a configuration example of a data file describing information of the point cloud data. 
     Point cloud data includes data on a plurality of points. Data on each point includes geometry information (three-dimensional coordinates) and attribute information associated with the geometry information. A set of a plurality of such points is referred to as a point cloud. For example, a point cloud indicates a three-dimensional shape of an object. 
     Geometry information (position), such as three-dimensional coordinates, may be referred to as geometry. Data on each point may include attribute information (attribute) on a plurality of types of attributes. A type of attribute is color or reflectance, for example. 
     One item of attribute information (in other words, a piece of attribute information or an attribute information item) may be associated with one item of geometry information (in other words, a piece of geometry information or a geometry information item), or attribute information on a plurality of different types of attributes may be associated with one item of geometry information. Alternatively, items of attribute information on the same type of attribute may be associated with one item of geometry information. 
     The configuration example of a data file shown in  FIG. 3  is an example in which geometry information and attribute information are associated with each other in a one-to-one relationship, and geometry information and attribute information on N points forming point cloud data are shown. 
     The geometry information is information on three axes, specifically, an x-axis, a y-axis, and a z-axis, for example. The attribute information is RGB color information, for example. A representative data file is ply file, for example. 
     Next, types of point cloud data will be described.  FIG. 4  is a diagram showing types of point cloud data. As shown in  FIG. 4 , point cloud data includes a static object and a dynamic object. 
     The static object is three-dimensional point cloud data at an arbitrary time (a time point). The dynamic object is three-dimensional point cloud data that varies with time. In the following, three-dimensional point cloud data associated with a time point will be referred to as a PCC frame or a frame. 
     The object may be a point cloud whose range is limited to some extent, such as ordinary video data, or may be a large point cloud whose range is not limited, such as map information. 
     There are point cloud data having varying densities. There may be sparse point cloud data and dense point cloud data. 
     In the following, each processor will be described in detail. Sensor information is obtained by various means, including a distance sensor such as LiDAR or a range finder, a stereo camera, or a combination of a plurality of monocular cameras. Point cloud data generator  4618  generates point cloud data based on the sensor information obtained by sensor information obtainer  4617 . Point cloud data generator  4618  generates geometry information as point cloud data, and adds attribute information associated with the geometry information to the geometry information. 
     When generating geometry information or adding attribute information, point cloud data generator  4618  may process the point cloud data. For example, point cloud data generator  4618  may reduce the data amount by omitting a point cloud whose position coincides with the position of another point cloud. Point cloud data generator  4618  may also convert the geometry information (such as shifting, rotating or normalizing the position) or render the attribute information. 
     Note that, although  FIG. 1  shows point cloud data generation system  4611  as being included in three-dimensional data encoding system  4601 , point cloud data generation system  4611  may be independently provided outside three-dimensional data encoding system  4601 . 
     Encoder  4613  generates encoded data by encoding point cloud data according to an encoding method previously defined. In general, there are the two types of encoding methods described below. One is an encoding method using geometry information, which will be referred to as a first encoding method, hereinafter. The other is an encoding method using a video codec, which will be referred to as a second encoding method, hereinafter. 
     Decoder  4624  decodes the encoded data into the point cloud data using the encoding method previously defined. 
     Multiplexer  4614  generates multiplexed data by multiplexing the encoded data in an existing multiplexing method. The generated multiplexed data is transmitted or accumulated. Multiplexer  4614  multiplexes not only the PCC-encoded data but also another medium, such as a video, an audio, subtitles, an application, or a file, or reference time information. Multiplexer  4614  may further multiplex attribute information associated with sensor information or point cloud data. 
     Multiplexing schemes or file formats include ISOBMFF, MPEG-DASH, which is a transmission scheme based on ISOBMFF, MMT, MPEG-2 TS Systems, or RMP, for example. 
     Demultiplexer  4623  extracts PCC-encoded data, other media, time information and the like from the multiplexed data. 
     Input/output unit  4615  transmits the multiplexed data in a method suitable for the transmission medium or accumulation medium, such as broadcasting or communication. Input/output unit  4615  may communicate with another device over the Internet or communicate with an accumulator, such as a cloud server. 
     As a communication protocol, http, ftp, TCP, UDP or the like is used. The pull communication scheme or the push communication scheme can be used. 
     A wired transmission or a wireless transmission can be used. For the wired transmission, Ethernet (registered trademark), USB, RS-232C, HDMI (registered trademark), or a coaxial cable is used, for example. For the wireless transmission, wireless LAN, Wi-Fi (registered trademark), Bluetooth (registered trademark), or a millimeter wave is used, for example. 
     As a broadcasting scheme, DVB-T2, DVB-S2, DVB-C2, ATSC3.0, or ISDB-S3 is used, for example. 
       FIG. 5  is a diagram showing a configuration of first encoder  4630 , which is an example of encoder  4613  that performs encoding in the first encoding method.  FIG. 6  is a block diagram showing first encoder  4630 . First encoder  4630  generates encoded data (encoded stream) by encoding point cloud data in the first encoding method. First encoder  4630  includes geometry information encoder  4631 , attribute information encoder  4632 , additional information encoder  4633 , and multiplexer  4634 . 
     First encoder  4630  is characterized by performing encoding by keeping a three-dimensional structure in mind. First encoder  4630  is further characterized in that attribute information encoder  4632  performs encoding using information obtained from geometry information encoder  4631 . The first encoding method is referred to also as geometry-based PCC (GPCC). 
     Point cloud data is PCC point cloud data like a PLY file or PCC point cloud data generated from sensor information, and includes geometry information (position), attribute information (attribute), and other additional information (metadata). The geometry information is input to geometry information encoder  4631 , the attribute information is input to attribute information encoder  4632 , and the additional information is input to additional information encoder  4633 . 
     Geometry information encoder  4631  generates encoded geometry information (compressed geometry), which is encoded data, by encoding geometry information. For example, geometry information encoder  4631  encodes geometry information using an N-ary tree structure, such as an octree. Specifically, in the case of an octree, a current space (target space) is divided into eight nodes (subspaces), 8-bit information (occupancy code) that indicates whether each node includes a point cloud or not is generated. A node including a point cloud is further divided into eight nodes, and 8-bit information that indicates whether each of the eight nodes includes a point cloud or not is generated. This process is repeated until a predetermined level is reached or the number of the point clouds included in each node becomes equal to or less than a threshold. 
     Attribute information encoder  4632  generates encoded attribute information (compressed attribute), which is encoded data, by encoding attribute information using configuration information generated by geometry information encoder  4631 . For example, attribute information encoder  4632  determines a reference point (reference node) that is to be referred to in encoding a current point (in other words, a current node or a target node) to be processed based on the octree structure generated by geometry information encoder  4631 . For example, attribute information encoder  4632  refers to a node whose parent node in the octree is the same as the parent node of the current node, of peripheral nodes or neighboring nodes. Note that the method of determining a reference relationship is not limited to this method. 
     The process of encoding attribute information may include at least one of a quantization process, a prediction process, and an arithmetic encoding process. In this case, “refer to” means using a reference node for calculating a predicted value of attribute information or using a state of a reference node (occupancy information that indicates whether a reference node includes a point cloud or not, for example) for determining a parameter of encoding. For example, the parameter of encoding is a quantization parameter in the quantization process or a context or the like in the arithmetic encoding. 
     Additional information encoder  4633  generates encoded additional information (compressed metadata), which is encoded data, by encoding compressible data of additional information. 
     Multiplexer  4634  generates encoded stream (compressed stream), which is encoded data, by multiplexing encoded geometry information, encoded attribute information, encoded additional information, and other additional information. The generated encoded stream is output to a processor in a system layer (not shown). 
     Next, first decoder  4640 , which is an example of decoder  4624  that performs decoding in the first encoding method, will be described.  FIG. 7  is a diagram showing a configuration of first decoder  4640 .  FIG. 8  is a block diagram showing first decoder  4640 . First decoder  4640  generates point cloud data by decoding encoded data (encoded stream) encoded in the first encoding method in the first encoding method. First decoder  4640  includes demultiplexer  4641 , geometry information decoder  4642 , attribute information decoder  4643 , and additional information decoder  4644 . 
     An encoded stream (compressed stream), which is encoded data, is input to first decoder  4640  from a processor in a system layer (not shown). 
     Demultiplexer  4641  separates encoded geometry information (compressed geometry), encoded attribute information (compressed attribute), encoded additional information (compressed metadata), and other additional information from the encoded data. 
     Geometry information decoder  4642  generates geometry information by decoding the encoded geometry information. For example, geometry information decoder  4642  restores the geometry information on a point cloud represented by three-dimensional coordinates from encoded geometry information represented by an N-ary structure, such as an octree. 
     Attribute information decoder  4643  decodes the encoded attribute information based on configuration information generated by geometry information decoder  4642 . For example, attribute information decoder  4643  determines a reference point (reference node) that is to be referred to in decoding a current point (current node) to be processed based on the octree structure generated by geometry information decoder  4642 . For example, attribute information decoder  4643  refers to a node whose parent node in the octree is the same as the parent node of the current node, of peripheral nodes or neighboring nodes. Note that the method of determining a reference relationship is not limited to this method. 
     The process of decoding attribute information may include at least one of an inverse quantization process, a prediction process, and an arithmetic decoding process. In this case, “refer to” means using a reference node for calculating a predicted value of attribute information or using a state of a reference node (occupancy information that indicates whether a reference node includes a point cloud or not, for example) for determining a parameter of decoding. For example, the parameter of decoding is a quantization parameter in the inverse quantization process or a context or the like in the arithmetic decoding. 
     Additional information decoder  4644  generates additional information by decoding the encoded additional information. First decoder  4640  uses additional information required for the decoding process for the geometry information and the attribute information in the decoding, and outputs additional information required for an application to the outside. 
     Next, an example configuration of a geometry information encoder will be described.  FIG. 9  is a block diagram of geometry information encoder  2700  according to this embodiment. Geometry information encoder  2700  includes octree generator  2701 , geometry information calculator  2702 , encoding table selector  2703 , and entropy encoder  2704 . 
     Octree generator  2701  generates an octree, for example, from input position information, and generates an occupancy code of each node of the octree. Geometry information calculator  2702  obtains information that indicates whether a neighboring node of a current node (target node) is an occupied node or not. For example, geometry information calculator  2702  calculates occupancy information on a neighboring node from an occupancy code of a parent node to which a current node belongs (information that indicates whether a neighboring node is an occupied node or not). Geometry information calculator  2702  may save an encoded node in a list and search the list for a neighboring node. Note that geometry information calculator  2702  may change neighboring nodes in accordance with the position of the current node in the parent node. 
     Encoding table selector  2703  selects an encoding table used for entropy encoding of the current node based on the occupancy information on the neighboring node calculated by geometry information calculator  2702 . For example, encoding table selector  2703  may generate a bit sequence based on the occupancy information on the neighboring node and select an encoding table of an index number generated from the bit sequence. 
     Entropy encoder  2704  generates encoded geometry information and metadata by entropy-encoding the occupancy code of the current node using the encoding table of the selected index number. Entropy encoder may add, to the encoded geometry information, information that indicates the selected encoding table. 
     In the following, an octree representation and a scan order for geometry information will be described. Geometry information (geometry data) is transformed into an octree structure (octree transform) and then encoded. The octree structure includes nodes and leaves. Each node has eight nodes or leaves, and each leaf has voxel (VXL) information.  FIG. 10  is a diagram showing an example structure of geometry information including a plurality of voxels.  FIG. 11  is a diagram showing an example in which the geometry information shown in  FIG. 10  is transformed into an octree structure. Here, of leaves shown in  FIG. 11 , leaves  1 ,  2 , and  3  represent voxels VXL 1 , VXL 2 , and VXL 3  shown in  FIG. 10 , respectively, and each represent VXL containing a point cloud (referred to as a valid VXL, hereinafter). 
     Specifically, node  1  corresponds to the entire space comprising the geometry information in  FIG. 10 . The entire space corresponding to node  1  is divided into eight nodes, and among the eight nodes, a node containing valid VXL is further divided into eight nodes or leaves. This process is repeated for every layer of the tree structure. Here, each node corresponds to a subspace, and has information (occupancy code) that indicates where the next node or leaf is located after division as node information. A block in the bottom layer is designated as a leaf and retains the number of the points contained in the leaf as leaf information. 
     Next, an example configuration of a geometry information decoder will be described.  FIG. 12  is a block diagram of geometry information decoder  2710  according to this embodiment. Geometry information decoder  2710  includes octree generator  2711 , geometry information calculator  2712 , encoding table selector  2713 , and entropy decoder  2714 . 
     Octree generator  2711  generates an octree of a space (node) based on header information, metadata or the like of a bitstream. For example, octree generator  2711  generates an octree by generating a large space (root node) based on the sizes of a space in an x-axis direction, a y-axis direction, and a z-axis direction added to the header information and dividing the space into two parts in the x-axis direction, the y-axis direction, and the z-axis direction to generate eight small spaces A (nodes A 0  to A 7 ). Nodes A 0  to A 7  are sequentially designated as a current node. 
     Geometry information calculator  2712  obtains occupancy information that indicates whether a neighboring node of a current node is an occupied node or not. For example, geometry information calculator  2712  calculates occupancy information on a neighboring node from an occupancy code of a parent node to which a current node belongs. Geometry information calculator  2712  may save a decoded node in a list and search the list for a neighboring node. Note that geometry information calculator  2712  may change neighboring nodes in accordance with the position of the current node in the parent node. 
     Encoding table selector  2713  selects an encoding table (decoding table) used for entropy decoding of the current node based on the occupancy information on the neighboring node calculated by geometry information calculator  2712 . For example, encoding table selector  2713  may generate a bit sequence based on the occupancy information on the neighboring node and select an encoding table of an index number generated from the bit sequence. 
     Entropy decoder  2714  generates position information by entropy-decoding the occupancy code of the current node using the selected encoding table. Note that entropy decoder  2714  may obtain information on the selected encoding table by decoding the bitstream, and entropy-decode the occupancy code of the current node using the encoding table indicated by the information. 
     In the following, configurations of an attribute information encoder and an attribute information decoder will be described.  FIG. 13  is a block diagram showing an example configuration of attribute information encoder A 100 . The attribute information encoder may include a plurality of encoders that perform different encoding methods. For example, the attribute information encoder may selectively use any of the two methods described below in accordance with the use case. 
     Attribute information encoder A 100  includes LoD attribute information encoder A 101  and transformed-attribute-information encoder A 102 . LoD attribute information encoder A 101  classifies three-dimensional points into a plurality of layers based on geometry information on the three-dimensional points, predicts attribute information on three-dimensional points belonging to each layer, and encodes a prediction residual therefor. Here, each layer into which a three-dimensional point is classified is referred to as a level of detail (LoD). 
     Transformed-attribute-information encoder A 102  encodes attribute information using region adaptive hierarchical transform (RAHT). Specifically, transformed-attribute-information encoder A 102  generates a high frequency component and a low frequency component for each layer by applying RAHT or Haar transform to each item of attribute information based on the geometry information on three-dimensional points, and encodes the values by quantization, entropy encoding or the like. 
       FIG. 14  is a block diagram showing an example configuration of attribute information decoder A 110 . The attribute information decoder may include a plurality of decoders that perform different decoding methods. For example, the attribute information decoder may selectively use any of the two methods described below for decoding based on the information included in the header or metadata. 
     Attribute information decoder A 110  includes LoD attribute information decoder A 111  and transformed-attribute-information decoder A 112 . LoD attribute information decoder A 111  classifies three-dimensional points into a plurality of layers based on the geometry information on the three-dimensional points, predicts attribute information on three-dimensional points belonging to each layer, and decodes attribute values thereof. 
     Transformed-attribute-information decoder A 112  decodes attribute information using region adaptive hierarchical transform (RAHT). Specifically, transformed-attribute-information decoder A 112  decodes each attribute value by applying inverse RAHT or inverse Haar transform to the high frequency component and the low frequency component of the attribute value based on the geometry information on the three-dimensional point. 
       FIG. 15  is a block diagram showing a configuration of attribute information encoder  3140  that is an example of LoD attribute information encoder A 101 . 
     Attribute information encoder  3140  includes LoD generator  3141 , periphery searcher  3142 , predictor  3143 , prediction residual calculator  3144 , quantizer  3145 , arithmetic encoder  3146 , inverse quantizer  3147 , decoded value generator  3148 , and memory  3149 . 
     LoD generator  3141  generates an LoD using geometry information on a three-dimensional point. 
     Periphery searcher  3142  searches for a neighboring three-dimensional point neighboring each three-dimensional point using a result of LoD generation by LoD generator  3141  and distance information indicating distances between three-dimensional points. 
     Predictor  3143  generates a predicted value of an item of attribute information on a current (target) three-dimensional point to be encoded. 
     Prediction residual calculator  3144  calculates (generates) a prediction residual of the predicted value of the item of the attribute information generated by predictor  3143 . 
     Quantizer  3145  quantizes the prediction residual of the item of attribute information calculated by prediction residual calculator  3144 . 
     Arithmetic encoder  3146  arithmetically encodes the prediction residual quantized by quantizer  3145 . Arithmetic encoder  3146  outputs a bitstream including the arithmetically encoded prediction residual to the three-dimensional data decoding device, for example. 
     The prediction residual may be binarized by quantizer  3145  before being arithmetically encoded by arithmetic encoder  3146 . 
     Arithmetic encoder  3146  may initialize the encoding table used for the arithmetic encoding before performing the arithmetic encoding. Arithmetic encoder  3146  may initialize the encoding table used for the arithmetic encoding for each layer. Arithmetic encoder  3146  may output a bitstream including information that indicates the position of the layer at which the encoding table is initialized. 
     Inverse quantizer  3147  inverse-quantizes the prediction residual quantized by quantizer  3145 . 
     Decoded value generator  3148  generates a decoded value by adding the predicted value of the item of attribute information generated by predictor  3143  and the prediction residual inverse-quantized by inverse quantizer  3147  together. 
     Memory  3149  is a memory that stores a decoded value of an item of attribute information on each three-dimensional point decoded by decoded value generator  3148 . For example, when generating a predicted value of a three-dimensional point yet to be encoded, predictor  3143  may generate the predicted value using a decoded value of an item of attribute information on each three-dimensional point stored in memory  3149 . 
       FIG. 16  is a block diagram of attribute information encoder  6600  that is an example of transformation attribute information encoder A 102 . Attribute information encoder  6600  includes sorter  6601 , Haar transformer  6602 , quantizer  6603 , inverse quantizer  6604 , inverse Haar transformer  6605 , memory  6606 , and arithmetic encoder  6607 . 
     Sorter  6601  generates the Morton codes by using the geometry information of three-dimensional points, and sorts the plurality of three-dimensional points in the order of the Morton codes. Haar transformer  6602  generates the coding coefficient by applying the Haar transform to the attribute information. Quantizer  6603  quantizes the coding coefficient of the attribute information. 
     Inverse quantizer  6604  inverse quantizes the coding coefficient after the quantization. Inverse Haar transformer  6605  applies the inverse Haar transform to the coding coefficient. Memory  6606  stores the values of items of attribute information of a plurality of decoded three-dimensional points. For example, the attribute information of the decoded three-dimensional points stored in memory  6606  may be utilized for prediction and the like of an unencoded three-dimensional point. 
     Arithmetic encoder  6607  calculates ZeroCnt from the coding coefficient after the quantization, and arithmetically encodes ZeroCnt. Additionally, arithmetic encoder  6607  arithmetically encodes the non-zero coding coefficient after the quantization. Arithmetic encoder  6607  may binarize the coding coefficient before the arithmetic encoding. In addition, arithmetic encoder  6607  may generate and encode various kinds of header information. 
       FIG. 17  is a block diagram showing a configuration of attribute information decoder  3150  that is an example of LoD attribute information decoder A 111 . 
     Attribute information decoder  3150  includes LoD generator  3151 , periphery searcher  3152 , predictor  3153 , arithmetic decoder  3154 , inverse quantizer  3155 , decoded value generator  3156 , and memory  3157 . 
     LoD generator  3151  generates an LoD using geometry information on a three-dimensional point decoded by the geometry information decoder (not shown in  FIG. 17 ). 
     Periphery searcher  3152  searches for a neighboring three-dimensional point neighboring each three-dimensional point using a result of LoD generation by LoD generator  3151  and distance information indicating distances between three-dimensional points. 
     Predictor  3153  generates a predicted value of attribute information item on a current three-dimensional point to be decoded. 
     Arithmetic decoder  3154  arithmetically decodes the prediction residual in the bitstream obtained from attribute information encoder  3140  shown in  FIG. 15 . Note that arithmetic decoder  3154  may initialize the decoding table used for the arithmetic decoding. Arithmetic decoder  3154  initializes the decoding table used for the arithmetic decoding for the layer for which the encoding process has been performed by arithmetic encoder  3146  shown in  FIG. 15 . Arithmetic decoder  3154  may initialize the decoding table used for the arithmetic decoding for each layer. Arithmetic decoder  3154  may initialize the decoding table based on the information included in the bitstream that indicates the position of the layer for which the encoding table has been initialized. 
     Inverse quantizer  3155  inverse-quantizes the prediction residual arithmetically decoded by arithmetic decoder  3154 . 
     Decoded value generator  3156  generates a decoded value by adding the predicted value generated by predictor  3153  and the prediction residual inverse-quantized by inverse quantizer  3155  together. Decoded value generator  3156  outputs the decoded attribute information data to another device. 
     Memory  3157  is a memory that stores a decoded value of an item of attribute information on each three-dimensional point decoded by decoded value generator  3156 . For example, when generating a predicted value of a three-dimensional point yet to be decoded, predictor  3153  generates the predicted value using a decoded value of an item of attribute information on each three-dimensional point stored in memory  3157 . 
       FIG. 18  is a block diagram of attribute information decoder  6610  that is an example of transformation attribute information decoder A 112 . Attribute information decoder  6610  includes arithmetic decoder  6611 , inverse quantizer  6612 , inverse Haar transformer  6613 , and memory  6614 . 
     Arithmetic decoder  6611  arithmetically decodes ZeroCnt and the coding coefficient included in a bitstream. Note that arithmetic decoder  6611  may decode various kinds of header information. 
     Inverse quantizer  6612  inverse quantizes the arithmetically decoded coding coefficient. Inverse Haar transformer  6613  applies the inverse Haar transform to the coding coefficient after the inverse quantization. Memory  6614  stores the values of items of attribute information of a plurality of decoded three-dimensional points. For example, the attribute information of the decoded three-dimensional points stored in memory  6614  may be utilized for prediction of an undecoded three-dimensional point. 
     Next, second encoder  4650 , which is an example of encoder  4613  that performs encoding in the second encoding method, will be described.  FIG. 19  is a diagram showing a configuration of second encoder  4650 .  FIG. 20  is a block diagram showing second encoder  4650 . 
     Second encoder  4650  generates encoded data (encoded stream) by encoding point cloud data in the second encoding method. Second encoder  4650  includes additional information generator  4651 , geometry image generator  4652 , attribute image generator  4653 , video encoder  4654 , additional information encoder  4655 , and multiplexer  4656 . 
     Second encoder  4650  is characterized by generating a geometry image and an attribute image by projecting a three-dimensional structure onto a two-dimensional image, and encoding the generated geometry image and attribute image in an existing video encoding scheme. The second encoding method is referred to as video-based PCC (VPCC). 
     Point cloud data is PCC point cloud data like a PLY file or PCC point cloud data generated from sensor information, and includes geometry information (position), attribute information (attribute), and other additional information (metadata). 
     Additional information generator  4651  generates map information on a plurality of two-dimensional images by projecting a three-dimensional structure onto a two-dimensional image. 
     Geometry image generator  4652  generates a geometry image based on the geometry information and the map information generated by additional information generator  4651 . The geometry image is a distance image in which distance (depth) is indicated as a pixel value, for example. The distance image may be an image of a plurality of point clouds viewed from one point of view (an image of a plurality of point clouds projected onto one two-dimensional plane), a plurality of images of a plurality of point clouds viewed from a plurality of points of view, or a single image integrating the plurality of images. 
     Attribute image generator  4653  generates an attribute image based on the attribute information and the map information generated by additional information generator  4651 . The attribute image is an image in which attribute information (color (RGB), for example) is indicated as a pixel value, for example. The image may be an image of a plurality of point clouds viewed from one point of view (an image of a plurality of point clouds projected onto one two-dimensional plane), a plurality of images of a plurality of point clouds viewed from a plurality of points of view, or a single image integrating the plurality of images. 
     Video encoder  4654  generates an encoded geometry image (compressed geometry image) and an encoded attribute image (compressed attribute image), which are encoded data, by encoding the geometry image and the attribute image in a video encoding scheme. Note that, as the video encoding scheme, any well-known encoding method can be used. For example, the video encoding scheme is AVC or HEVC. 
     Additional information encoder  4655  generates encoded additional information (compressed metadata) by encoding the additional information, the map information and the like included in the point cloud data. 
     Multiplexer  4656  generates an encoded stream (compressed stream), which is encoded data, by multiplexing the encoded geometry image, the encoded attribute image, the encoded additional information, and other additional information. The generated encoded stream is output to a processor in a system layer (not shown). 
     Next, second decoder  4660 , which is an example of decoder  4624  that performs decoding in the second encoding method, will be described.  FIG. 21  is a diagram showing a configuration of second decoder  4660 .  FIG. 22  is a block diagram showing second decoder  4660 . Second decoder  4660  generates point cloud data by decoding encoded data (encoded stream) encoded in the second encoding method in the second encoding method. Second decoder  4660  includes demultiplexer  4661 , video decoder  4662 , additional information decoder  4663 , geometry information generator  4664 , and attribute information generator  4665 . 
     An encoded stream (compressed stream), which is encoded data, is input to second decoder  4660  from a processor in a system layer (not shown). 
     Demultiplexer  4661  separates an encoded geometry image (compressed geometry image), an encoded attribute image (compressed attribute image), an encoded additional information (compressed metadata), and other additional information from the encoded data. 
     Video decoder  4662  generates a geometry image and an attribute image by decoding the encoded geometry image and the encoded attribute image in a video encoding scheme. Note that, as the video encoding scheme, any well-known encoding method can be used. For example, the video encoding scheme is AVC or HEVC. 
     Additional information decoder  4663  generates additional information including map information or the like by decoding the encoded additional information. 
     Geometry information generator  4664  generates geometry information from the geometry image and the map information. Attribute information generator  4665  generates attribute information from the attribute image and the map information. 
     Second decoder  4660  uses additional information required for decoding in the decoding, and outputs additional information required for an application to the outside. 
     In the following, a problem with the PCC encoding scheme will be described.  FIG. 23  is a diagram showing a protocol stack relating to PCC-encoded data.  FIG. 23  shows an example in which PCC-encoded data is multiplexed with other medium data, such as a video (HEVC, for example) or an audio, and transmitted or accumulated. 
     A multiplexing scheme and a file format have a function of multiplexing various encoded data and transmitting or accumulating the data. To transmit or accumulate encoded data, the encoded data has to be converted into a format for the multiplexing scheme. For example, with HEVC, a technique for storing encoded data in a data structure referred to as a NAL unit and storing the NAL unit in ISOBMFF is prescribed. 
     At present, a first encoding method (Codec1) and a second encoding method (Codec2) are under investigation as encoding methods for point cloud data. However, there is no method defined for storing the configuration of encoded data and the encoded data in a system format. Thus, there is a problem that an encoder cannot perform an MUX process (multiplexing), transmission, or accumulation of data. 
     Note that, in the following, the term “encoding method” means any of the first encoding method and the second encoding method unless a particular encoding method is specified. 
     Embodiment 2 
     In this embodiment, types of the encoded data (geometry information (geometry), attribute information (attribute), and additional information (metadata)) generated by first encoder  4630  or second encoder  4650  described above, a method of generating additional information (metadata), and a multiplexing process in the multiplexer will be described. The additional information (metadata) may be referred to as a parameter set or control information (signaling information). 
     In this embodiment, the dynamic object (three-dimensional point cloud data that varies with time) described above with reference to  FIG. 4  will be described, for example. However, the same method can also be used for the static object (three-dimensional point cloud data associated with an arbitrary time point). 
       FIG. 24  is a diagram showing configurations of encoder  4801  and multiplexer  4802  in a three-dimensional data encoding device according to this embodiment. Encoder  4801  corresponds to first encoder  4630  or second encoder  4650  described above, for example. Multiplexer  4802  corresponds to multiplexer  4634  or  4656  described above. 
     Encoder  4801  encodes a plurality of PCC (point cloud compression) frames of point cloud data to generate a plurality of pieces of encoded data (multiple compressed data) of geometry information, attribute information, and additional information. 
     Multiplexer  4802  integrates a plurality of types of data (geometry information, attribute information, and additional information) into a NAL unit, thereby converting the data into a data configuration that takes data access in the decoding device into consideration. 
       FIG. 25  is a diagram showing a configuration example of the encoded data generated by encoder  4801 . Arrows in the drawing indicate a dependence involved in decoding of the encoded data. The source of an arrow depends on data of the destination of the arrow. That is, the decoding device decodes the data of the destination of an arrow, and decodes the data of the source of the arrow using the decoded data. In other words, “a first entity depends on a second entity” means that data of the second entity is referred to (used) in processing (encoding, decoding, or the like) of data of the first entity. 
     First, a process of generating encoded data of geometry information will be described. Encoder  4801  encodes geometry information of each frame to generate encoded geometry data (compressed geometry data) for each frame. The encoded geometry data is denoted by G(i). i denotes a frame number or a time point of a frame, for example. 
     Furthermore, encoder  4801  generates a geometry parameter set (GPS(i)) for each frame. The geometry parameter set includes a parameter that can be used for decoding of the encoded geometry data. The encoded geometry data for each frame depends on an associated geometry parameter set. 
     The encoded geometry data formed by a plurality of frames is defined as a geometry sequence. Encoder  4801  generates a geometry sequence parameter set (referred to also as geometry sequence PS or geometry SPS) that stores a parameter commonly used for a decoding process for the plurality of frames in the geometry sequence. The geometry sequence depends on the geometry SPS. 
     Next, a process of generating encoded data of attribute information will be described. Encoder  4801  encodes attribute information of each frame to generate encoded attribute data (compressed attribute data) for each frame. The encoded attribute data is denoted by A(i).  FIG. 25  shows an example in which there are attribute X and attribute Y. and encoded attribute data for attribute X is denoted by AX(i), and encoded attribute data for attribute Y is denoted by AY(i). 
     Furthermore, encoder  4801  generates an attribute parameter set (APS(i)) for each frame. The attribute parameter set for attribute X is denoted by AXPS(i), and the attribute parameter set for attribute Y is denoted by AYPS(i). The attribute parameter set includes a parameter that can be used for decoding of the encoded attribute information. The encoded attribute data depends on an associated attribute parameter set. 
     The encoded attribute data formed by a plurality of frames is defined as an attribute sequence. Encoder  4801  generates an attribute sequence parameter set (referred to also as attribute sequence PS or attribute SPS) that stores a parameter commonly used for a decoding process for the plurality of frames in the attribute sequence. The attribute sequence depends on the attribute SPS. 
     In the first encoding method, the encoded attribute data depends on the encoded geometry data. 
       FIG. 25  shows an example in which there are two types of attribute information (attribute X and attribute Y). When there are two types of attribute information, for example, two encoders generate data and metadata for the two types of attribute information. For example, an attribute sequence is defined for each type of attribute information, and an attribute SPS is generated for each type of attribute information. 
     Note that, although  FIG. 25  shows an example in which there is one type of geometry information, and there are two types of attribute information, the present invention is not limited thereto. There may be one type of attribute information or three or more types of attribute information. In such cases, encoded data can be generated in the same manner. If the point cloud data has no attribute information, there may be no attribute information. In such a case, encoder  4801  does not have to generate a parameter set associated with attribute information. 
     Next, a process of generating encoded data of additional information (metadata) will be described. Encoder  4801  generates a PCC stream PS (referred to also as PCC stream PS or stream PS), which is a parameter set for the entire PCC stream. Encoder  4801  stores a parameter that can be commonly used for a decoding process for one or more geometry sequences and one or more attribute sequences in the stream PS. For example, the stream PS includes identification information indicating the codec for the point cloud data and information indicating an algorithm used for the encoding, for example. The geometry sequence and the attribute sequence depend on the stream PS. 
     Next, an access unit and a GOF will be described. In this embodiment, concepts of access unit (AU) and group of frames (GOF) are newly introduced. 
     An access unit is a basic unit for accessing data in decoding, and is formed by one or more pieces of data and one or more pieces of metadata. For example, an access unit is formed by geometry information and one or more pieces of attribute information associated with a same time point. A GOF is a random access unit, and is formed by one or more access units. 
     Encoder  4801  generates an access unit header (AU header) as identification information indicating the top of an access unit. Encoder  4801  stores a parameter relating to the access unit in the access unit header. For example, the access unit header includes a configuration of or information on the encoded data included in the access unit. The access unit header further includes a parameter commonly used for the data included in the access unit, such as a parameter relating to decoding of the encoded data. 
     Note that encoder  4801  may generate an access unit delimiter that includes no parameter relating to the access unit, instead of the access unit header. The access unit delimiter is used as identification information indicating the top of the access unit. The decoding device identifies the top of the access unit by detecting the access unit header or the access unit delimiter. 
     Next, generation of identification information for the top of a GOF will be described. As identification information indicating the top of a GOF, encoder  4801  generates a GOF header. Encoder  4801  stores a parameter relating to the GOF in the GOF header. For example, the GOF header includes a configuration of or information on the encoded data included in the GOF. The GOF header further includes a parameter commonly used for the data included in the GOF, such as a parameter relating to decoding of the encoded data. 
     Note that encoder  4801  may generate a GOF delimiter that includes no parameter relating to the GOF, instead of the GOF header. The GOF delimiter is used as identification information indicating the top of the GOF. The decoding device identifies the top of the GOF by detecting the GOF header or the GOF delimiter. 
     In the PCC-encoded data, the access unit is defined as a PCC frame unit, for example. The decoding device accesses a PCC frame based on the identification information for the top of the access unit. 
     For example, the GOF is defined as one random access unit. The decoding device accesses a random access unit based on the identification information for the top of the GOF. For example, if PCC frames are independent from each other and can be separately decoded, a PCC frame can be defined as a random access unit. 
     Note that two or more PCC frames may be assigned to one access unit, and a plurality of random access units may be assigned to one GOF. 
     Encoder  4801  may define and generate a parameter set or metadata other than those described above. For example, encoder  4801  may generate supplemental enhancement information (SEI) that stores a parameter (an optional parameter) that is not always used for decoding. 
     Next, a configuration of encoded data and a method of storing encoded data in a NAL unit will be described. 
     For example, a data format is defined for each type of encoded data.  FIG. 26  is a diagram showing an example of encoded data and a NAL unit. 
     For example, as shown in  FIG. 26 , encoded data includes a header and a payload. The encoded data may include length information indicating the length (data amount) of the encoded data, the header, or the payload. The encoded data may include no header. 
     The header includes identification information for identifying the data, for example. The identification information indicates a data type or a frame number, for example. 
     The header includes identification information indicating a reference relationship, for example. The identification information is stored in the header when there is a dependence relationship between data, for example, and allows an entity to refer to another entity. For example, the header of the entity to be referred to includes identification information for identifying the data. The header of the referring entity includes identification information indicating the entity to be referred to. 
     Note that, when the entity to be referred to or the referring entity can be identified or determined from other information, the identification information for identifying the data or identification information indicating the reference relationship can be omitted. 
     Multiplexer  4802  stores the encoded data in the payload of the NAL unit. The NAL unit header includes pec_nal_unit_type, which is identification information for the encoded data.  FIG. 27  is a diagram showing a semantics example of pcc_nal_unit_type. 
     As shown in  FIG. 27 , when pec_codec_type is codec 1 (Codec1: first encoding method), values 0 to 10 of pcc_nal_unit_type are assigned to encoded geometry data (Geometry), encoded attribute X data (AttributeX), encoded attribute Y data (AttributeY), geometry PS (Geom. PS), attribute XPS (AttrX. S), attribute YPS (AttrY. PS), geometry SPS (Geometry Sequence PS), attribute X SPS (AttributeX Sequence PS), attribute Y SPS (AttributeY Sequence PS). AU header (AU Header), and GOF header (GOF Header) in codec 1. Values of 11 and greater are reserved in codec 1. 
     When pcc_codec_type is codec 2 (Codec2: second encoding method), values of 0 to 2 of pec_nal_unit_type are assigned to data A (DataA), metadata A (MetaDataA), and metadata B (MetaDataB) in the codec. Values of 3 and greater are reserved in codec 2. 
     Embodiment 3 
     Although there are tools for data dividing, such as the slice or the tile, in HEVC encoding in order to make parallel processing in a decoding device possible, there are no such tools yet in PCC (Point Cloud Compression) encoding. 
     In PCC, various data dividing methods can be considered according to parallel processing, compression efficiency, and compression algorithms. Here, the definitions of slice and tile, the data structure, and the transmission/reception methods will be described. 
       FIG. 28  is a block diagram illustrating the configuration of first encoder  4910  included in a three-dimensional data encoding device according to the present embodiment. First encoder  4910  generates encoded data (an encoded stream) by encoding point cloud data with a first encoding method (GPCC (Geometry based PCC)). First encoder  4910  includes divider  4911 , a plurality of geometry information encoders  4912 , a plurality of attribute information encoders  4913 , additional information encoder  4914 , and multiplexer  4915 . 
     Divider  4911  generates a plurality of divided data by dividing point cloud data. Specifically, divider  4911  generates a plurality of divided data by dividing the space of point cloud data into a plurality of subspaces. Here, the subspaces are one of tiles and slices, or a combination of tiles and slices. More specifically, point cloud data includes geometry information, attribute information, and additional information. Divider  4911  divides geometry information into a plurality of divided geometry information, and divides attribute information into a plurality of divided attribute information. Also, divider  4911  generates additional information about division. 
     A plurality of geometry information encoders  4912  generate a plurality of encoded geometry information by encoding the plurality of divided geometry information. For example, the plurality of geometry information encoders  4912  process the plurality of divided geometry information in parallel. 
     The plurality of attribute information encoders  4913  generate a plurality of encoded attribute information by encoding the plurality of divided attribute information. For example, the plurality of attribute information encoders  4913  process the plurality of divided attribute information in parallel. 
     Additional information encoder  4914  generates encoded additional information by encoding the additional information included in point cloud data, and the additional information about data dividing generated by divider  4911  at the time of division. 
     Multiplexer  4915  generates encoded data (an encoded stream) by multiplexing the plurality of encoded geometry information, the plurality of encoded attribute information, and the encoded additional information, and transmits the generated encoded data. Furthermore, the encoded additional information is used at the time of decoding. 
     Note that, although  FIG. 28  illustrates the example in which the respective numbers of geometry information encoders  4912  and attribute information encoders  4913  are two, the respective numbers of geometry information encoders  4912  and attribute information encoders  4913  may be one, or may be three or more. Furthermore, the plurality of divided data may be processed in parallel in the same chip, such as a plurality of cores in a CPU, may be processed in parallel by the respective cores of a plurality of chips, or may be processed in parallel by the plurality of cores of a plurality of chips. 
       FIG. 29  is a block diagram illustrating the configuration of first decoder  4920 . First decoder  4920  restores point cloud data by decoding the encoded data (encoded stream) generated by encoding the point cloud data with the first encoding method (GPCC). First decoder  4920  includes demultiplexer  4921 , a plurality of geometry information decoders  4922 , a plurality of attribute information decoders  4923 , additional information decoder  4924 , and combiner  4925 . 
     Demultiplexer  4921  generates a plurality of encoded geometry information, a plurality of encoded attribute information, and encoded additional information by demultiplexing the encoded data (encoded stream). 
     The plurality of geometry information decoders  4922  generate a plurality of divided geometry information by decoding the plurality of encoded geometry information. For example, the plurality of geometry information decoders  4922  process the plurality of encoded geometry information in parallel. 
     The plurality of attribute information decoders  4923  generate a plurality of divided attribute information by decoding the plurality of encoded attribute information. For example, the plurality of attribute information decoders  4923  process the plurality of encoded attribute information in parallel. 
     Additional information decoder  4924  generates additional information by decoding the encoded additional information. 
     Combiner  4925  generates geometry information by combining the plurality of divided geometry information by using the additional information. Combiner  4925  generates attribute information by combining the plurality of divided attribute information by using the additional information. 
     Note that, although  FIG. 29  illustrates the example in which the respective numbers of geometry information decoders  4922  and attribute information decoders  4923  are two, the respective numbers of geometry information decoders  4922  and attribute information decoders  4923  may be one, or may be three or more. Furthermore, the plurality of divided data may be processed in parallel in the same chip, such as a plurality of cores in a CPU, may be processed in parallel by the respective cores of a plurality of chips, or may be processed in parallel by the plurality of cores of a plurality of chips. 
     Next, the configuration of divider  4911  will be described.  FIG. 30  is a block diagram of divider  4911 . Divider  4911  includes slice divider  4931 , geometry information tile divider (geometry tile divider)  4932 , and attribute information tile divider (attribute tile divider)  4933 . 
     Slice divider  4931  generates a plurality of slice geometry information by dividing geometry information (position or geometry) into slices. Also, slice divider  4931  generates a plurality of slice attribute information by dividing attribute information (attribute) into slices. Furthermore, slice divider  4931  outputs slice additional information (SliceMetaData) including the information related to slice dividing and the information generated in the slice dividing. 
     Geometry information tile divider  4932  generates a plurality of divided geometry information (a plurality of tile geometry information) by dividing the plurality of slice geometry information into tiles. Also, geometry information tile divider  4932  outputs geometry tile additional information (geometry tile metadata) including the information related to tile dividing of geometry information, and the information generated in the tile dividing of the geometry information. 
     Attribute information tile divider  4933  generates a plurality of divided attribute information (a plurality of tile attribute information) by dividing the plurality of slice attribute information into tiles. Also, attribute information tile divider  4933  outputs attribute tile additional information (attribute tile metadata) including the information related to tile dividing of attribute information, and the information generated in the tile dividing of the attribute information. 
     Note that the number of slices or tiles to be divided is one or more. That is, slice or tile dividing may not be performed. 
     Note that, although the example in which tile dividing is performed after slice dividing has been illustrated here, slice dividing may be performed after tile dividing. Furthermore, a new division type may be defined in addition to the slice and the tile, and dividing may be performed with three or more division types. 
     Hereinafter, the dividing method for point cloud data will be described.  FIG. 31  is a diagram illustrating an example of slice and tile dividing. 
     First, the method for slice dividing will be described. Divider  4911  divides three-dimensional point cloud data into arbitrary point clouds on a slice-by-slice basis. In slice dividing, divider  4911  does not divide the geometry information and the attribute information constituting points, but collectively divides the geometry information and the attribute information. That is, divider  4911  performs slice dividing so that the geometry information and the attribute information of an arbitrary point belong to the same slice. Note that, as long as these are followed, the number of divisions and the dividing method may be any number and any method. Furthermore, the minimum unit of division is a point. For example, the numbers of divisions of geometry information and attribute information are the same. For example, a three-dimensional point corresponding to geometry information after slice dividing, and a three-dimensional point corresponding to attribute information are included in the same slice. 
     Also, divider  4911  generates slice additional information, which is additional information related to the number of divisions and the dividing method at the time of slice dividing. The slice additional information is the same for geometry information and attribute information. For example, the slice additional information includes the information indicating the reference coordinate position, size, or side length of a bounding box after division. Also, the slice additional information includes the information indicating the number of divisions, the division type, etc. 
     Next, the method for tile dividing will be described. Divider  4911  divides the data divided into slices into slice geometry information (G slice) and slice attribute information (A slice), and divides each of the slice geometry information and the slice attribute information on a tile-by-tile basis. 
     Note that, although  FIG. 31  illustrates the example in which division is performed with an octree structure, the number of divisions and the dividing method may be any number and any method. 
     Also, divider  4911  may divide geometry information and attribute information with different dividing methods, or may divide geometry information and attribute information with the same dividing method. Additionally, divider  4911  may divide a plurality of slices into tiles with different dividing methods, or may divide a plurality of slices into tiles with the same dividing method. 
     Furthermore, divider  4911  generates tile additional information related to the number of divisions and the dividing method at the time of tile dividing. The tile additional information (geometry tile additional information and attribute tile additional information) is separate for geometry information and attribute information. For example, the tile additional information includes the information indicating the reference coordinate position, size, or side length of a bounding box after division. Additionally, the tile additional information includes the information indicating the number of divisions, the division type, etc. 
     Next, an example of the method of dividing point cloud data into slices or tiles will be described. As the method for slice or tile dividing, divider  4911  may use a predetermined method, or may adaptively switch methods to be used according to point cloud data. 
     At the time of slice dividing, divider  4911  divides a three-dimensional space by collectively handling geometry information and attribute information. For example, divider  4911  determines the shape of an object, and divides a three-dimensional space into slices according to the shape of the object. For example, divider  4911  extracts objects such as trees or buildings, and performs division on an object-by-object basis. For example, divider  4911  performs slice dividing so that the entirety of one or a plurality of objects are included in one slice. Alternatively, divider  4911  divides one object into a plurality of slices. 
     In this case, the encoding device may change the encoding method for each slice, for example. For example, the encoding device may use a high-quality compression method for a specific object or a specific part of the object. In this case, the encoding device may store the information indicating the encoding method for each slice in additional information (metadata). 
     Also, divider  4911  may perform slice dividing so that each slice corresponds to a predetermined coordinate space based on map information or geometry information. 
     At the time of tile dividing, divider  4911  separately divides geometry information and attribute information. For example, divider  4911  divides slices into tiles according to the data amount or the processing amount. For example, divider  4911  determines whether the data amount of a slice (for example, the number of three-dimensional points included in a slice) is greater than a predetermined threshold value. When the data amount of the slice is greater than the threshold value, divider  4911  divides slices into tiles. When the data amount of the slice is less than the threshold value, divider  4911  does not divide slices into tiles. 
     For example, divider  4911  divides slices into tiles so that the processing amount or processing time in the decoding device is within a certain range (equal to or less than a predetermined value). Accordingly, the processing amount per tile in the decoding device becomes constant, and distributed processing in the decoding device becomes easy. 
     Additionally, when the processing amount is different between geometry information and attribute information, for example, when the processing amount of geometry information is greater than the processing amount of attribute information, divider  4911  makes the number of divisions of geometry information larger than the number of divisions of attribute information. 
     Furthermore, for example, when geometry information may be decoded and displayed earlier, and attribute information may be slowly decoded and displayed later in the decoding device according to contents, divider  4911  may make the number of divisions of geometry information larger than the number of divisions of attribute information. Accordingly, since the decoding device can increase the parallel number of geometry information, it is possible to make the processing of geometry information faster than the processing of attribute information. 
     Note that the decoding device does not necessarily have to process sliced or tiled data in parallel, and may determine whether or not to process them in parallel according to the number or capability of decoding processors. 
     By performing division with the method as described above, it is possible to achieve adaptive encoding according to contents or objects. Also, parallel processing in decoding processing can be achieved. Accordingly, the flexibility of a point cloud encoding system or a point cloud decoding system is improved. 
       FIG. 32  is a diagram illustrating dividing pattern examples of slices and tiles. DU in the diagram is a data unit (DataUnit), and indicates the data of a tile or a slice. Additionally, each DU includes a slice index (SliceIndex) and a tile index (TileIndex). The top right numerical value of a DU in the diagram indicates the slice index, and the bottom left numerical value of the DU indicates the tile index. 
     In Pattern 1, in slice dividing, the number of divisions and the dividing method are the same for G slice and A slice. In tile dividing, the number of divisions and the dividing method for G slice are different from the number of divisions and the dividing method for A slice. Additionally, the same number of divisions and dividing method are used among a plurality of G slices. The same number of divisions and dividing method are used among a plurality of A slices. 
     In Pattern 2, in slice dividing, the number of divisions and the dividing method are the same for G slice and A slice. In tile dividing, the number of divisions and the dividing method for G slice are different from the number of divisions and the dividing method for A slice. Additionally, the number of divisions and the dividing method are different among a plurality of G slices. The number of divisions and the dividing method are different among a plurality of A slices. 
     Next, the encoding method for divided data will be described. The three-dimensional data encoding device (first encoder  4910 ) encodes each of divided data. When encoding attribute information, the three-dimensional data encoding device generates, as additional information, dependency information indicating based on which configuration information (geometry information, additional information, or other attribute information) encoding has been performed. That is, the dependency information indicates, for example, the configuration information of a reference destination (dependence destination). In this case, the three-dimensional data encoding device generates the dependency information based on the configuration information corresponding to the divided shape of attribute information. Note that the three-dimensional data encoding device may generate the dependency information based on the configuration information corresponding to a plurality of divided shapes. 
     Dependency information may be generated by the three-dimensional data encoding device, and the generated dependency information may be transmitted to the three-dimensional data decoding device. Alternatively, the three-dimensional data decoding device may generate dependency information, and the three-dimensional data encoding device may not transmit the dependency information. Furthermore, the dependency used by the three-dimensional data encoding device may be defined in advance, and the three-dimensional data encoding device may not transmit the dependency information. 
       FIG. 33  is a diagram illustrating an example of dependency of each data. The heads of arrows in the diagram indicate dependence destinations, and the origins of the arrows indicate dependence sources. The three-dimensional data decoding device decodes data in the order of a dependence destination to a dependence source. Additionally, the data indicated by solid lines in the diagram is data that is actually transmitted, and the data indicated by dotted lines is data that is not transmitted. 
     Furthermore, in the diagram, G indicates geometry information, and A indicates attribute information. Gs 1  indicates the geometry information of slice number 1, and Gs 2  indicates the geometry information of slice number 2. Gs 1   t   1  indicates the geometry information of slice number 1 and tile number 1, Gs 1   t   2  indicates the geometry information of slice number 1 and tile number 2, Gs 2   t   1  indicates the geometry information of slice number 2 and tile number 1, and Gs 2   t   2  indicates the geometry information of slice number 2 and tile number 2. Similarly, As 1  indicates the attribute information of slice number 1, and As 2  indicates the attribute information of slice number 2. As 1   t   1  indicates the attribute information of slice number 1 and tile number 1, As 1   t   2  indicates the attribute information of slice number 1 and tile number 2, As 2   t   1  indicates the attribute information of slice number 2 and tile number 1, and As 2   t   2  indicates the attribute information of slice number 2 and tile number 2. 
     Mslice indicates slice additional information, MGtile indicates geometry tile additional information, and MAtile indicates attribute tile additional information. Ds 1   t   1  indicates the dependency information of attribute information As 1   t   1 , and Ds 2   t   1  indicates the dependency information of attribute information As 2   t   1 . 
     Additionally, the three-dimensional data encoding device may rearrange data in a decoding order, so that it is unnecessary to rearrange data in the three-dimensional data decoding device. Note that data may be rearranged in the three-dimensional data decoding device, or data may be rearranged in both the three-dimensional data encoding device and the three-dimensional data decoding device. 
       FIG. 34  is a diagram illustrating an example of the data decoding order. In the example of  FIG. 34 , decoding is sequentially performed from the data on the left. For those data in dependency, the three-dimensional data decoding device decodes the data of a dependence destination first. For example, the three-dimensional data encoding device rearranges data in advance to be in this order, and transmits the data. Note that, as long as it is the order in which the data of dependence destinations become first, it may be any kind of order. Additionally, the three-dimensional data encoding device may transmit additional information and dependency information before data. 
       FIG. 35  is a flowchart illustrating the flow of processing by the three-dimensional data encoding device. First, the three-dimensional data encoding device encodes the data of a plurality of slices or tiles as described above (S 4901 ). Next, as illustrated in  FIG. 34 , the three-dimensional data encoding device rearranges the data so that the data of dependence destinations become first (S 4902 ). Next, the three-dimensional data encoding device multiplexes the rearranged data (forms the rearranged data into a NAL unit) (S 4903 ). 
     Next, the configuration of combiner  4925  included in first decoder  4920  will be described.  FIG. 36  is a block diagram illustrating the configuration of combiner  4925 . Combiner  4925  includes geometry information tile combiner (geometry tile combiner)  4941 , attribute information tile combiner (attribute tile combiner)  4942 , and a slice combiner. 
     Geometry information tile combiner  4941  generates a plurality of slice geometry information by combining a plurality of divided geometry information by using geometry tile additional information. Attribute information tile combiner  4942  generates a plurality of slice attribute information by combining a plurality of divided attribute information by using attribute tile additional information. 
     Slice combiner  4943  generates geometry information by combining the plurality of slice geometry information by using slice additional information. Additionally, slice combiner  4943  generates attribute information by combining the plurality of slice attribute information by using slice additional information. 
     Note that the number of slices or tiles to be divided is one or more. That is, slice or tile dividing may not be performed. 
     Furthermore, although the example in which tile dividing is performed after slice dividing has been illustrated here, slice dividing may be performed after tile dividing. Furthermore, a new division type may be defined in addition to the slice and the tile, and dividing may be performed with three or more division types. 
     Next, the configuration of encoded data divided into slices or divided into tiles, and the storing method (multiplexing method) of the encoded data into a NAL unit will be described.  FIG. 37  is a diagram illustrating the configuration of encoded data, and the storing method of the encoded data into a NAL unit. 
     Encoded data (divided geometry information and divided attribute information) is stored in the payload of a NAL unit. 
     Encoded data includes a header and a payload. The header includes identification information for specifying the data included in the payload. This identification information includes, for example, the type of slice dividing or tile dividing (slice_type, tile_type), the index information for specifying slices or tiles (slice_idx, tile_idx), the geometry information of data (slices or tiles), or the address of data, etc. The index information for specifying slices is also written as the slice index (SliceIndex). The index information for specifying tiles is also written as the tile index (TileIndex). Additionally, the type of division is, for example, the technique based on an object shape as described above, the technique based on map information or geometry information, or the technique based on the data amount or processing amount, etc. 
     Note that all or a part of the above-described information may be stored in one of the header of divided geometry information and the header of divided attribute information, and may not be stored in the other. For example, when the same dividing method is used for geometry information and attribute information, the type of division (slice_type, tile_type) and the index information (slice_idx, tile_idx) for the geometry information and the attribute information are the same. Therefore, these information may be included in the header of one of the geometry information and the attribute information. For example, when attribute information depends on geometry information, the geometry information is processed first. Therefore, these information may be included in the header of the geometry information, and these information may not be included in the header of the attribute information. In this case, the three-dimensional data decoding device determines that, for example, the attribute information of a dependence source belongs to the same slice or tile as a slice or tile of the geometry information of a dependence destination. 
     Furthermore, additional information (slice additional information, geometry tile additional information, or attribute tile additional information) related to slice dividing or tile dividing, and dependency information indicating dependency, etc. may be stored and transmitted in an existing parameter set (GPS, APS, geometry SPS, or attribute SPS). When the dividing method is changed for each frame, the information indicating the dividing method may be stored in the parameter set (GPS or APS) for each frame. When the dividing method is not changed within a sequence, the information indicating the dividing method may be stored in the parameter set (geometry SPS or attribute SPS) for each sequence. Furthermore, when the same dividing method is used for geometry information and attribute information, the information indicating the dividing method may be stored in the parameter set of a PCC stream (stream PS). 
     Also, the above-described information may be stored in any of the above-described parameter sets, or may be stored in a plurality of the parameter sets. Additionally, a parameter set for tile dividing or slice dividing may be defined, and the above-described information may be stored in the parameter set. Furthermore, these information may be stored in the header of encoded data. 
     Also, the header of encoded data includes the identification information indicating dependency. That is, when there is dependency between data, the header includes the identification information for referring to a dependence destination from a dependence source. For example, the header of data of a dependence destination includes the identification information for specifying the data. The identification information indicating the dependence destination is included in the header of the data of a dependence source. Note that, when the identification information for specifying data, the additional information related to slice dividing or tile dividing, and the identification information indicating dependency can be identified or derived from other information, these information may be omitted. 
     Next, the flows of encoding processing and decoding processing of point cloud data according to the present embodiment will be described.  FIG. 38  is a flowchart of the encoding processing of point cloud data according to the present embodiment. 
     First, the three-dimensional data encoding device determines the dividing method to be used (S 4911 ). This dividing method includes whether or not to perform slice dividing, and whether or not to perform tile dividing. Also, the dividing method may include the number of divisions and the type of division, etc. in the case of performing slice dividing or tile dividing. The type of division is the technique based on an object shape as described above, the technique based on map information or geometry information, or the technique based on the data amount or processing amount, etc. Note that the dividing method may be defined in advance. 
     When slice dividing is performed (Yes in S 4912 ), the three-dimensional data encoding device generates a plurality of slice geometry information and a plurality of slice attribute information by collectively dividing geometry information and attribute information (S 4913 ). Also, the three-dimensional data encoding device generates slice additional information related to slice dividing. Note that the three-dimensional data encoding device may separately divide geometry information and attribute information. 
     When tile dividing is performed (Yes in S 4914 ), the three-dimensional data encoding device generates a plurality of divided geometry information and a plurality of divided attribute information by separately dividing the plurality of slice geometry information and the plurality of slice attribute information (or geometry information and attribute information) (S 4915 ). Additionally, the three-dimensional data encoding device generates geometry tile additional information and attribute tile additional information related to tile dividing. Note that the three-dimensional data encoding device may collectively divide slice geometry information and slice attribute information. 
     Next, the three-dimensional data encoding device generates a plurality of encoded geometry information and a plurality of encoded attribute information by encoding each of the plurality of divided geometry information and the plurality of divided attribute information (S 4916 ). Also, the three-dimensional data encoding device generates dependency information. 
     Next, the three-dimensional data encoding device generates encoded data (an encoded stream) by forming (multiplexing) the plurality of encoded geometry information, the plurality of encoded attribute information, and additional information into a NAL unit (S 4917 ). Also, the three-dimensional data encoding device transmits the generated encoded data. 
       FIG. 39  is a flowchart of the decoding processing of point cloud data according to the present embodiment. First, the three-dimensional data decoding device determines the dividing method by analyzing additional information (slice additional information, geometry tile additional information, and attribute tile additional information) related to the dividing method included in the encoded data (encoded stream) (S 4921 ). This dividing method includes whether or not to perform slice dividing, and whether or not to perform tile dividing. Additionally, the dividing method may include the number of divisions and the type of division, etc. in the case of performing slice dividing or tile dividing. 
     Next, the three-dimensional data decoding device generates divided geometry information and divided attribute information by decoding a plurality of encoded geometry information and a plurality of encoded attribute information included in the encoded data by using dependency information included in the encoded data (S 4922 ). 
     When it is indicated by the additional information that tile dividing has been performed (Yes in S 4923 ), the three-dimensional data decoding device generates a plurality of slice geometry information and a plurality of slice attribute information by combining a plurality of divided geometry information and a plurality of divided attribute information with respective methods based on geometry tile additional information and attribute tile additional information (S 4924 ). Note that the three-dimensional data decoding device may combine the plurality of divided geometry information and the plurality of divided attribute information with the same method. 
     When it is indicated by the additional information that slice dividing has been performed (Yes in S 4925 ), the three-dimensional data decoding device generates geometry information and attribute information by combining the plurality of slice geometry information and the plurality of slice attribute information (the plurality of divided geometry information and the plurality of divided attribute information) with the same method based on slice additional information (S 4926 ). Note that the three-dimensional data decoding device may combine the plurality of slice geometry information and the plurality of slice attribute information with respective different methods. 
     It is to be noted that attribute information (an identifier, area information, address information, position information, etc.) of a tile or a slice may be stored in other control information instead of SEI. For example, the attribute information may be stored in control information indicating the overall structure of PCC data, or may be stored in control information for each tile or each slice. 
     In addition, when the three-dimensional data encoding device (three-dimensional data transmitting device) transmits the PCC data to another device, the three-dimensional data encoding device may convert control information such as SEI into control information unique to a protocol supported by the system and present the converted control information. 
     For example, when the three-dimensional data encoding device converts PCC data including attribute information into an ISO Base Media File Format (ISOBM), the three-dimensional data encoding device may store SEI in an “mdat box” together with the PCC data, or may store SEI in a “track box” in which control information related to a stream is described. In other words, the three-dimensional data encoding device may store the control information in a table for random access. In addition, when the three-dimensional data encoding device packetizes PCC data and transmits packets of PCC data, the three-dimensional data encoding device may store SEI in packet headers. In this way, attribute information can be obtained in a layer of the system, which makes it easier to access the attribute information, and the tile data or the slice data, and thus makes it possible to accelerate the access. 
     It is to be noted that, in the configuration of the three-dimensional data decoding device, memory manager may determine, in advance, whether information which is necessary for a decoding process is present in memory, and if the information necessary for the decoding process is absent, memory manager may obtain the information necessary for the decoding process from storage or via a network. 
     When the three-dimensional data decoding device obtains PCC data from storage or via a network using Pull in a protocol such as the MPEG-DASH, memory manager may identify attribute information of data necessary for a decoding process based on information obtained from localizer or the like, request the tile or the slice including the identified attribute information, and obtain the necessary data (PCC stream). A tile or a slice including attribute information may be identified by a storage or network side, or may be identified by memory manager. For example, memory manager may obtain SEI from all PCC data in advance, and identify a tile or a slice based on the information. 
     When all PCC data have been transmitted from the storage or via the network using Push in the UDP protocol, or the like, memory manager may obtain desired data by identifying the attribute information of data necessary for a decoding process and a tile or a slice, based on information obtained from localizer, or the like, and by filtering a plurality of tiles or slices to obtain a desired tile or a slice from the PCC data transmitted. 
     In addition, when obtaining data, the three-dimensional data encoding device may determine whether desired data is present, whether real-time processing is possible based on a data size, etc., or a communication state, etc. When the three-dimensional data encoding device determines that it is difficult to obtain the data based on the determination result, the three-dimensional data encoding device may select and obtain another slice or tile whose priority or data amount is different from that of the data. 
     In addition, the three-dimensional data decoding device may transmit information from localizer, or the like to a cloud server, and the cloud server may determine necessary information based on the information. 
     Embodiment 4 
     The following describes tile additional information. The three-dimensional data encoding device generates tile additional information that is metadata regarding a tile division method, and transmits the generated tile additional information to the three-dimensional data decoding device. 
       FIG. 40  is a diagram illustrating an example of syntax of tile additional information (TileMetaData). As shown in  FIG. 40 , for example, tile additional information includes division method information (type_of_divide), shape information (topview_shape), an overlap flag (tile_overlap_lag), overlap information (type_of_overlap), height information (tile_height), a tile number (tile_number), and tile position information (global_position, relativeposition). 
     Division method information (type_of_divide) indicates a tile division method. For example, division method information indicates whether a tile division method is division based on map information, that is, division based on top view (top_view) or another division (other). 
     Shape information (topview_shape) is included in tile additional information when a tile division method is, for example, division based on top view. Shape information indicates a shape in top view of a tile. Examples of the shape include a square and a circle. Moreover, the examples of the shape may include an ellipse, a rectangle, or a polygon other than a quadrangle, or may include a shape other than these. It should be noted that shape information may indicate not only a shape in top view of a tile but also a three-dimensional shape (e.g., a cube, a round column) of a tile. 
     An overlap flag (tile_overlap_flag) indicates whether tiles overlap each other. For example, an overlap flag is included in tile additional information when a tile division method is division based on top view. In this case, the overlap flag indicates whether tiles overlap each other in top view. It should be noted that an overlap flag may indicate whether tiles overlap each other in a three-dimensional space. 
     Overlap information (type_of_overlap) is included in tile additional information when, for example, tiles overlap each other. Overlap information indicates, for example, how tiles overlap each other. For example, overlap information indicates the size of an overlapping region. 
     Height information (tile_height) indicates the height of a tile. It should be noted that height information may include information indicating a tile shape. For example, when the shape of a tile in top view is a rectangle, the information may indicate the length of a side (a vertical length, a horizontal length) of the rectangle. When the shape of a tile in top view is a circle, the information may indicate the diameter or radius of the circle. 
     Moreover, height information may indicate the height of each tile or a height common to tiles. In addition, height types such as roads and overpasses may be set in advance, and height information may indicate the height of each of the height types and a height type of each tile. Alternatively, a height of each height type may be specified in advance, and height information may indicate a height type of each tile. In other words, height information need not indicate a height of each height type. 
     A tile number (tile_number) indicates the number of tiles. It should be noted that tile additional information may include information indicating an interval between tiles. 
     Tile position information (global_position, relative_position) is information for identifying the position of each tile. For example, tile position information indicates the absolute coordinates or relative coordinates of each tile. 
     It should be noted that part or all of the above-mentioned information may be provided for each tile or each group of tiles (e.g., for each frame or group of frames). 
     The three-dimensional data encoding device may include tile additional information in supplemental enhancement information (SEI) and transmit the SEI. Alternatively, the three-dimensional data encoding device may store tile additional information in an existing parameter set (PPS, GPS, or APS, etc.) and transmit the parameter set. 
     For example, when tile additional information changes for each frame, the tile additional information may be stored in a parameter set for each frame (GPS or APS etc.). When tile additional information does not change in a sequence, the tile additional information may be stored in a parameter set for sequence (geometry SPS or attribute SPS). Further, when the same tile division information is used for geometry information and attribute information, tile additional information may be stored in a parameter set for a PCC stream (a stream PS). 
     Moreover, tile additional information may be stored in any one of the above-mentioned parameter sets or in parameter sets. In addition, tile additional information may be stored in the header of encoded data. Additionally, tile additional information may be stored in the header of a NAL unit. 
     Furthermore, part or all of tile additional information may be stored in one of the header of divided geometry information and the header of divided attribute information, and need not be stored in the other. For example, when the same tile additional information is used for geometry information and attribute information, the tile additional information may be included in the header of one of the geometry information and the attribute information. For example, when attribute information depends on geometry information, the geometry information is processed first. For this reason, the tile additional information may be included in the header of the geometry information, and need not be included in the header of the attribute information. In this case, for example, the three-dimensional data decoding device determines that the attribute information of the depender belongs to the same tile as a tile having the geometry information of the dependee. 
     The three-dimensional data decoding device reconstructs point cloud data subjected to tile division, based on tile additional information. When there are pieces of overlapping point cloud data, the three-dimensional data decoding device specifies the pieces of overlapping point cloud data and selects one of the pieces of overlapping point cloud data or merges pieces of point cloud data. 
     Moreover, the three-dimensional data decoding device may perform decoding using tile additional information. For example, when tiles overlap each other, the three-dimensional data decoding device may perform decoding for each tile, perform processing (e.g., smoothing or filtering) using the pieces of decoded data, and generate point cloud data. This makes it possible to perform highly accurate decoding. 
       FIG. 41  is a diagram illustrating a configuration example of a system including the three-dimensional data encoding device and the three-dimensional data decoding device. Tile divider  5051  divides point cloud data including geometry information and attribute information into a first tile and a second tile. In addition, tile divider  5051  transmits tile additional information regarding tile division to decoder  5053  and tile combiner  5054 . 
     Encoder  5052  generates encoded data by encoding the first tile and the second tile. 
     Decoder  5053  restores the first tile and the second tile by decoding the encoded data generated by encoder  5052 . Tile combiner  5054  restores the point cloud data (the geometry information and the attribute information) by combining the first tile and the second tile using the tile additional information. 
     The following describes slice additional information. The three-dimensional data encoding device generates slice additional information that is metadata regarding a slice division method, and transmits the generated slice additional information to the three-dimensional data decoding device. 
       FIG. 42  is a diagram illustrating an example of syntax of slice additional information (SliceMetaData). As shown in  FIG. 42 , for example, slice additional information includes division method information (type_of_divide), an overlap flag (slice_overlap_flag), overlap information (type_of_overlap), a slice number (slice_number), slice position information (global_position, relative_position), and slice size information (slice_bounding_box_size). 
     Division method information (type_of_divide) indicates a slice division method. For example, division method information indicates whether a slice division method is division based on information about an object (object). It should be noted that slice additional information may include information indicating an object division method. For example, this information indicates whether one object is to be divided into slices or assigned to one slice. In addition, the information may indicate, for example, a division number when one object is divided into slices. 
     An overlap flag (slice_overlap_flag) indicates whether slices overlap each other. Overlap information (type_of_overlap) is included in slice additional information when, for example, slices overlap each other. Overlap information indicates, for example, how slices overlap each other. For example, overlap information indicates the size of an overlapping region. 
     A slice number (slice_number) indicates the number of slices. 
     Slice position information (global_position, relative_position) and slice size information (slice_bounding_box_size) are information about a region of a slice. Slice position information is information for identifying the position of each slice. For example, slice position information indicates the absolute coordinates or relative coordinates of each slice. Slice size information (slice_bounding_box_size) indicates the size of each slice. For example, slice size information indicates the size of a bounding box of each slice. 
     The three-dimensional data encoding device may include slice additional information in SEI and transmit the SEI. Alternatively, the three-dimensional data encoding device may store slice additional information in an existing parameter set (PPS, GPS, or APS, etc.) and transmit the parameter set. 
     For example, when slice additional information changes for each frame, the slice additional information may be stored in a parameter set for each frame (GPS or APS etc.). When slice additional information does not change in a sequence, the slice additional information may be stored in a parameter set for sequence (geometry SPS or attribute SPS). Further, when the same slice division information is used for geometry information and attribute information, slice additional information may be stored in a parameter set for a PCC stream (a stream PS). 
     Moreover, slice additional information may be stored in any one of the above-mentioned parameter sets or in parameter sets. In addition, slice additional information may be stored in the header of encoded data. Additionally, slice additional information may be stored in the header of a NAL unit. 
     Furthermore, part or all of slice additional information may be stored in one of the header of divided geometry information and the header of divided attribute information, and need not be stored in the other. For example, when the same slice additional information is used for geometry information and attribute information, the slice additional information may be included in the header of one of the geometry information and the attribute information. For example, when attribute information depends on geometry information, the geometry information is processed first. For this reason, the slice additional information may be included in the header of the geometry information, and need not be included in the header of the attribute information. In this case, for example, the three-dimensional data decoding device determines that the attribute information of the depender belongs to the same slice as a slice having the geometry information of the dependee. 
     The three-dimensional data decoding device reconstructs point cloud data subjected to slice division, based on slice additional information. When there are pieces of overlapping point cloud data, the three-dimensional data decoding device specifies the pieces of overlapping point cloud data and selects one of the pieces of overlapping point cloud data or merges pieces of point cloud data. 
     Moreover, the three-dimensional data decoding device may perform decoding using slice additional information. For example, when slices overlap each other, the three-dimensional data decoding device may perform decoding for each slice, perform processing (e.g., smoothing or filtering) using the pieces of decoded data, and generate point cloud data. This makes it possible to perform highly accurate decoding. 
       FIG. 43  is a flowchart of a three-dimensional data encoding process including a tile additional information generation process performed by the three-dimensional data encoding device according to the present embodiment. 
     First, the three-dimensional data encoding device determines a division method to be used (S 5031 ). Specifically, the three-dimensional data encoding device determines whether a division method based on top view (top_view) or another method (other) is to be used as a tile division method. In addition, the three-dimensional data encoding device determines a tile shape when the division method based on top view is used. Additionally, the three-dimensional data encoding device determines whether tiles overlap with other tiles. 
     When the tile division method determined in step S 5031  is the division method based on top view (YES in S 5032 ), the three-dimensional data encoding device includes a result of the determination that the tile division method is the division method based on top view (top_view), in tile additional information (S 5033 ). 
     On the other hand, when the tile division method determined in step S 5031  is a method other than the division method based on top view (NO in S 5032 ), the three-dimensional data encoding device includes a result of the determination that the tile division method is the method other than the division method based on top view (top_view), in tile additional information (S 5034 ). 
     Moreover, when a shape in top view of a tile determined in step S 5031  is a square (SQUARE in S 5035 ), the three-dimensional data encoding device includes a result of the determination that the shape in top view of the tile is the square, in the tile additional information (S 5036 ). In contrast, when a shape in top view of a tile determined in step S 5031  is a circle (CIRCLE in S 5035 ), the three-dimensional data encoding device includes a result of the determination that the shape in top view of the tile is the circle, in the tile additional information (S 5037 ). 
     Next, the three-dimensional data encoding device determines whether tiles overlap with other tiles (S 5038 ). When the tiles overlap with the other tiles (YES in S 5038 ), the three-dimensional data encoding device includes a result of the determination that the tiles overlap with the other tiles, in the tile additional information (S 5039 ). On the other hand, when the tiles do not overlap with other tiles (NO in S 5038 ), the three-dimensional data encoding device includes a result of the determination that the tiles do not overlap with the other tiles, in the tile additional information ( 55040 ). 
     Finally, the three-dimensional data encoding device divides the tiles based on the tile division method determined in step S 5031 , encodes each of the tiles, and transmits the generated encoded data and the tile additional information (S 5041 ). 
       FIG. 44  is a flowchart of a three-dimensional data decoding process performed by the three-dimensional data decoding device according to the present embodiment using tile additional information. 
     First, the three-dimensional data decoding device analyzes tile additional information included in a bitstream (S 5051 ). 
     When the tile additional information indicates that tiles do not overlap with other tiles (NO in S 5052 ), the three-dimensional data decoding device generates point cloud data of each tile by decoding the tile (S 5053 ). Finally, the three-dimensional data decoding device reconstructs point cloud data from the point cloud data of each tile, based on a tile division method and a tile shape indicated by the tile additional information (S 5054 ). 
     In contrast, when the tile additional information indicates that tiles overlap with other tiles (YES in S 5052 ), the three-dimensional data decoding device generates point cloud data of each tile by decoding the tile. In addition, the three-dimensional data decoding device identifies overlap portions of the tiles based on the tile additional information (S 5055 ). It should be noted that, regarding the overlap portions, the three-dimensional data decoding device may perform decoding using pieces of overlapping information. Finally, the three-dimensional data decoding device reconstructs point cloud data from the point cloud data of each tile, based on a tile division method, a tile shape, and overlap information indicated by the tile additional information (S 5056 ). 
     The following describes, for example, variations regarding slice. The three-dimensional data encoding device may transmit, as additional information, information indicating a type (a road, a building, a tree, etc.) or attribute (dynamic information, static information, etc.) of an object. Alternatively, a coding parameter may be predetermined according to an object, and the three-dimensional data encoding device may notify the coding parameter to the three-dimensional data decoding device by transmitting a type or attribute of the object. 
     The following methods may be used regarding slice data encoding order and transmitting order. For example, the three-dimensional data encoding device may encode slice data in decreasing order of ease of object recognition or clustering. Alternatively, the three-dimensional data encoding device may encode slice data in the order in which clustering is completed. Moreover, the three-dimensional data encoding device may transmit slice data in the order in which the slice data is encoded. Alternatively, the three-dimensional data encoding device may transmit slice data in decreasing order of priority for decoding in an application. For example, when dynamic information has high priority for decoding, the three-dimensional data encoding device may transmit slice data in the order in which slices are grouped using the dynamic information. 
     Furthermore, when encoded data order is different from the order of priority for decoding, the three-dimensional data encoding device may transmit encoded data after rearranging the encoded data. In addition, when storing encoded data, the three-dimensional data encoding device may store encoded data after rearranging the encoded data. 
     An application (the three-dimensional data decoding device) requests a server (the three-dimensional data encoding device) to transmit slices including desired data. The server may transmit slice data required by the application, and need not transmit slice data unnecessary for the application. 
     An application requests a server to transmit a tile including desired data. The server may transmit tile data required by the application, and need not transmit tile data unnecessary for the application. 
     Embodiment 5 
     The present embodiment describes processing of a division unit (e.g., a tile or a slice) including no points. First, a method of dividing point cloud data will be described. 
     In a video coding standard such as HEVC, since there are data for all the pixels of a two-dimensional image, even when a two-dimensional space is divided into data areas, all the data areas include data. On the other hand, in encoding of three-dimensional point cloud data, points themselves that are elements of point cloud data are data, and there is a possibility that data are not included in some of areas. 
     There are various methods of spatially dividing point cloud data, and such methods can be classified according to whether a division unit (e.g., a tile or a slice) that is a divided data unit always includes one or more point data. 
     A division method in which all division units each include one or more point data is referred to as a first division method. Examples of the first division method include a method of dividing point cloud data in consideration of processing time for encoding or the size of encoded data. In this case, each division unit has a substantially even number of points. 
       FIG. 45  is a diagram illustrating examples of a division method. For example, as shown in (a) in  FIG. 45 , a method of separating points belonging to an identical space into two identical spaces may be used as the first division method. In addition, as shown in (b) in  FIG. 45 , a space may be divided into subspaces (division units) so that each of the division units includes points. 
     Since these methods are division in consideration of points, all division units always include one or more points. 
     A division method in which division units are likely to include one or more division units including no point data is referred to as a second division method. For example, as shown in (c) in  FIG. 45 , a method of dividing a space equally may be used as the second division method. In this case, a division unit does not always include points. In short, a division unit may include no points. 
     When the three-dimensional data encoding device divides point cloud data, the three-dimensional data encoding device may include, in divided additional information (e.g., tile additional information or slice additional information), (i) whether a division method in which all division units include one or more point data has been used, (ii) whether a division method in which division units include one or more division units including no point data has been used, or (iii) whether a division method in which division units are likely to include one or more division units including no point data. Subsequently, the three-dimensional data encoding device may transmit the divided additional information. 
     It should be noted that the three-dimensional data encoding device may indicate the above information as a type of a division method. Additionally, the three-dimensional data encoding device may perform division using a predetermined division method, and need not transmit divided additional information. In this case, the three-dimensional data encoding device clearly specifies whether the division method is the first division method or the second division method in advance. 
     The following describes the second division method and an example of generating and transmitting encoded data. It should be noted that although tile division will be exemplified as a method of dividing a three-dimensional space below, the present embodiment is not limited to tile division, and the following procedure is applicable to a division method using division units other than tiles. For example, slice division may be used instead of tile division. 
       FIG. 46  is a diagram illustrating an example of dividing point cloud data into six tiles.  FIG. 46  shows an example in which the smallest unit is a point and geometry information (geometry) and attribute information (attribute) are divided together. It should be noted that the same applies to a case in which geometry information and attribute information are divided using separate division methods or by separate division numbers, a case in which there is no attribute information, and a case in which there are pieces of attribute information. 
     In the example shown in  FIG. 46 , tile division results in tiles (#1, #2, #4, #6) including points and tiles (#3, #5) including no points. A tile including no points is referred to as a null tile. 
     It should be noted that the present disclosure is not limited to the division into six tiles, and any division method may be used. For example, a division unit may be a cube or have a non-cubic shape such as a cuboid or round column. Division units may be identical or different in shape. Moreover, a predetermined method may be used as a division method, or a different method may be used for each predetermined unit (e.g., PCC frame). 
     In the present division method, when point cloud data is divided into tiles and one or more of the tiles include no data, a bitstream including information indicating that the one or more tiles are null tiles is generated. 
     The following describes a method of transmitting a null tile and a method of signaling a null tile. The three-dimensional data encoding device may generate, as addition information (metadata) regarding data division, for example, the following information and transmit the generated information.  FIG. 47  is a diagram illustrating an example of syntax of tile additional information (TileMetaData). Tile additional information includes division method information (type_of_divide), division method null information (type_of_divide_null), a tile division number (number_of_tiles), and a tile null flag (tile_null_flag). 
     Division method information (type_of_divide) is information regarding a division method or a division type. For example, division method information indicates one or more division methods or division types. Examples of a division method include top view (top_view) division and equal division. It should be noted that when the number of definitions of a division method is one, tile additional information need not include division method information. 
     Division method null information (type_of_divide_null) is information indicating whether a division method to be used is the following first division method or second division method. Here, the first division method is a division method in which each of all division units always includes one or more point data. The second division method is a division method in which division units include one or more division units including no point data or a division method in which division units are likely to include one or more division units including no point data. 
     Tile additional information may also include, as division information about tiles as a whole, at least one of (i) information (a tile division number (number_of_tiles)) indicating a tile division number or information for specifying a tile division number, (ii) information indicating the number of null tiles or information for specifying the number of null tiles, or (iii) information indicating the number of tiles other than null tiles or information for specifying the number of tiles other than null tiles. In addition, the tile additional information may include, as division information about tiles as a whole, information indicating shapes of tiles or whether tiles overlap each other. 
     Moreover, the tile additional information indicates division information of each tile in sequence. For example, the order of tiles is predetermined for each division method, and is already known to the three-dimensional data encoding device and the three-dimensional data decoding device. It should be noted that when the order of tiles is not predetermined, the three-dimensional data encoding device may transmit information indicating the order to the three-dimensional data decoding device. 
     Division information of each tile includes a tile null flag (tile_null_flag) indicating whether the tile includes data (a point). It should be noted that when a tile includes no data, a tile null flag may be included as tile division information. 
     Moreover, when a tile is not a null tile, tile additional information includes division information (position information (e.g., the coordinates of the origin (origin_x, origin_y, origin_z), tile height information, etc.) of each tile. Furthermore, when a tile is a null tile, tile additional information does not include division information of each tile. 
     For example, when slice division information of each tile is stored into division information of each tile, the three-dimensional data encoding device need not store slice division information of a null tile into additional information. 
     It should be noted that in this example, a tile division number (number_of_tiles) indicates the number of tiles including null tiles.  FIG. 48  is a diagram illustrating an example of index information (idx) of a tile. In the example shown in  FIG. 48 , index information is also assigned to a null tile. 
     The following describes a data structure of encoded data including null tiles and a transmission method.  FIG. 49  to  FIG. 51  each are a diagram illustrating a data structure when the third and fifth tiles include no data after geometry information and attribute information are divided into six tiles. 
       FIG. 49  is a diagram illustrating an example of a dependency relationship of each data. The pointed end of an arrow in the figure indicates a dependee, and the other end of the arrow indicates a depender. Moreover, in the figure, Gtn denotes geometry information for tile number n, and Atn denotes attribute information for tile number n, n being an integer from 1 to 6. Mtile denotes tile additional information. 
       FIG. 50  is a diagram illustrating a structural example of transmitted data that is encoded data transmitted by the three-dimensional data encoding device.  FIG. 51  is a diagram illustrating a structure of encoded data and a method of storing encoded data in a NAL unit. 
     As shown in  FIG. 51 , each of the headers of data of geometry information (divided geometry information) and attribute information (divided attribute information) includes index information (tile_idx) of a tile. 
     Moreover, as shown in structure  1  in  FIG. 50 , the three-dimensional data encoding device need not transmit geometry information or attribute information constituting a null tile. Alternatively, as shown in structure  2  in  FIG. 50 , the three-dimensional data encoding device may transmit, as data of a null tile, information indicating that a tile is a null tile. For example, the three-dimensional data encoding device may include, in tile_type stored in the header of a NAL unit or the header in a payload (nal_unit_payload) of a NAL unit, that a type of the data is a null tile, and transmit the header. It should be noted that the following description will be premised on structure  1 . 
     In structure  1 , when there are null tiles, some of values of index information (tile_idx) of tiles included in the header of geometry information data or attribute information data are missing and the values are not continuous in transmitted data. 
     Moreover, when data have a dependency relationship with each other, the three-dimensional data encoding device transmits the data so that data referred to can be decoded before data referring to the data. It should be noted that a tile of attribute information depends on a tile of geometry information. The same index number of a tile is assigned to attribute information and geometry information having a dependency relationship with each other. 
     It should be noted that tile additional information regarding tile division may be stored in both or one of a parameter set for geometry information (GPS) and a parameter set for attribute information (APS). When the tile additional information is stored in one of the GPS or the APS, reference information indicating a GPS or an APS to be referred to may be stored in the other of the GPS or the APS. Moreover, when a tile division method is different between geometry information and attribute information, different tile additional information is stored in each of a GPS and an APS. Furthermore, when an identical tile division method is used for sequences (PCC frames), tile additional information may be stored in a GPS, an APS, or a sequence parameter set (SPS). 
     For example, when tile additional information is stored in both a GPS and an APS, tile additional information for geometry information is stored in the GPS, and tile additional information for attribute information is stored in the APS. Moreover, when tile additional information is stored in common information such as an SPS, tile additional information to be commonly used for geometry information and attribute information may be stored, or tile additional information for the geometry information and tile additional information for the attribute information may be stored separately. 
     Hereinafter, a combination of tile division and slice division will be described. First, the following describe a data structure and data transmission when tile division is performed after slice division. 
       FIG. 52  is a diagram illustrating an example of a dependency relationship of each data when tile division is performed after slice division. The pointed end of an arrow in the figure indicates a dependee, and the other end of the arrow indicates a depender. Data indicated by a solid line in the figure is data actually transmitted, and data indicated by a broken line is data not transmitted. 
     In the figure, G denotes geometry information, and A denotes attribute information. Gs 1  denotes geometry information for slice number 1, and Gs 2  denotes geometry information for slice number 2. Gs 1   t   1  denotes geometry information for slice number 1 and tile number 1, and Gs 2   t   2  denotes geometry information for slice number 2 and tile number 2. Likewise, As 1  denotes attribute information for slice number 1, and As 2  denotes attribute information for slice number 2. As 1   t   1  denotes attribute information for slice number 1 and tile number 1, and As 2   t   1  denotes attribute information for slice number 2 and tile number 1. 
     Mslice denotes slice additional information, MGtile denotes geometry tile additional information, and MAtile denotes attribute tile additional information. Ds 1   t   1  denotes dependency relationship information of attribute information As 1   t   1 , and Ds 2   t   1  denotes dependency relationship information of attribute information As 2   t   1 . 
     The three-dimensional data encoding device need not generate and transmit geometry information and attribute information regarding a null tile. 
     Even when a tile division number is identical to all slices, there is a possibility that the number of tiles generated and transmitted is different between slices. For example, when a tile division number is different between geometry information and attribute information, there is a case in which a null tile is included in one of the geometry information and the attribute information, and a null tile is not included in the other of the geometry information and the attribute information. In the example shown in  FIG. 52 , geometry information of slice  1  (Gs 1 ) is divided into two tiles Gsit 1  and Gs 1   t   2 , and Gs 1   t   2  is a null tile. In contrast, attribute information of slice  1  (As 1 ) is not divided, with the result that there are one As 1   t   1  and no null tiles. 
     When data is included in at least a tile of attribution information regardless of whether a null tile is included in a slice of geometry information, the three-dimensional data encoding device generates and transmits dependency relationship information of the attribute information. For example, when the three-dimensional data encoding device stores slice division information of each tile in division information of each slice included in slice additional information regarding slice division, the three-dimensional data encoding device stores information indicating whether the tile is a null tile in the slice division information. 
       FIG. 53  is a diagram illustrating an example of decoding order of data. In the example shown in  FIG. 53 , data are decoded in order from the left. The three-dimensional data decoding device decodes, out of data having a dependency relationship with each other, data of a dependee first. For example, the three-dimensional data encoding device rearranges data in this order and transmits the data. It should be noted that any order may be used as long as data of a dependee takes precedence. Moreover, the three-dimensional data encoding device may transmit additional information and dependency relationship information before data. 
     Next, the following describe a data structure and data transmission when slice division is performed after tile division. 
       FIG. 54  is a diagram illustrating an example of a dependency relationship of each data when slice division is performed after tile division. The pointed end of an arrow in the figure indicates a dependee, and the other end of the arrow indicates a depender. Data indicated by a solid line in the figure is data actually transmitted, and data indicated by a broken line is data not transmitted. 
     In the figure, G denotes geometry information, and A denotes attribute information. Gt 1  denotes geometry information for tile number 1. Gt 1   s   1  denotes geometry information for tile number 1 and slice number 1, and Gt 1   s   2  denotes geometry information for tile number 1 and slice number 2. Likewise, At 1  denotes attribute information for tile number 1, and At 1   s   1  denotes attribute information for tile number 1 and slice number 1. 
     Mtile denotes tile additional information, MGslice denotes geometry slice additional information, and MAslice denotes attribute slice additional information. Dt 1   s   1  denotes dependency relationship information of attribute information At 1   s   1 , and Dt 2   s   1  denotes dependency relationship information of attribute information At 2   s   1 . 
     The three-dimensional data encoding device does not perform slice division on a null tile. In addition, the three-dimensional data encoding device need not generate and transmit geometry information and attribute information regarding a null tile, and dependency relationship information of the geometry information. 
       FIG. 55  is a diagram illustrating an example of decoding order of data. In the example shown in  FIG. 55 , data are decoded in order from the left. The three-dimensional data decoding device decodes, out of data having a dependency relationship with each other, data of a dependee first. For example, the three-dimensional data encoding device rearranges data in this order and transmits the data. It should be noted that any order may be used as long as data of a dependee takes precedence. Moreover, the three-dimensional data encoding device may transmit additional information and dependency relationship information before data. 
     The following describes procedures of a point cloud data division process and a point cloud data combination process. It should be noted that although examples of tile division and slice division will be described here, the same procedures can be applied to division of another space. 
       FIG. 56  is a flowchart of a three-dimensional data encoding process including a data division process performed by the three-dimensional data encoding device. First, the three-dimensional data encoding device determines a division method to be used (S 5101 ). Specifically, the three-dimensional data encoding device determines whether to use a first division method or a second division method. For example, the three-dimensional data encoding device may determine a division method based on instructions from a user or an external device (e.g., the three-dimensional data decoding device), or determine a division method according to inputted point cloud data. In addition, a division method to be used may be predetermined. 
     Here, the first division method is a division method in which each of all division units (tiles or slices) always includes one or more point data. The second division method is a division method in which division units include one or more division units including no point data or a division method in which division units are likely to include one or more division units including no point data. 
     When the determined division method is the first division method (FIRST DIVISION METHOD in S 5102 ), the three-dimensional data encoding device includes a result of the determination that the division method used is the first division method, in divided additional information (e.g., tile additional information or slice additional information) that is metadata regarding data division (S 5103 ). Finally, the three-dimensional data encoding device encodes all division units (S 5104 ). 
     On the other hand, when the determined division method is the second division method (SECOND DIVISION METHOD in S 5102 ), the three-dimensional data encoding device includes a result of the determination that the division method used in the second division method, in divided additional information (S 5105 ). Finally, the three-dimensional data encoding device encodes, among division units, division units other than division units (e.g., null tiles) including no point data (S 5106 ). 
       FIG. 57  is a flowchart of a three-dimensional data decoding process including a data combination process performed by the three-dimensional data decoding device. First, the three-dimensional data decoding device refers to divided additional information included in a bitstream and determines whether a division method used is the first division method or the second division method (S 5111 ). 
     When the division method used is the first division method (FIRST DIVISION METHOD in S 5112 ), the three-dimensional data decoding device receives encoded data of all division units and generates decoded data of all the division units by decoding the received encoded data (S 5113 ). Finally, the three-dimensional data decoding device reconstructs a three-dimensional point cloud using the decoded data of all the division units (S 5114 ). For example, the three-dimensional data decoding device reconstructs a three-dimensional point cloud by combining division units. 
     On the other hand, when the division method used is the second division method (SECOND DIVISION METHOD in S 5112 ), the three-dimensional data decoding device receives encoded data of division units including point data and encoded data of division units including no point data, and generates decoded data by decoding the received encoded data of the division units (S 5115 ). It should be noted that when division units including no point data are not transmitted, the three-dimensional data decoding device need not receive and decode the division units including no point data. Finally, the three-dimensional data decoding device reconstructs a three-dimensional point cloud using the decoded data of the division units including the point data (S 5116 ). For example, the three-dimensional data decoding device reconstructs a three-dimensional point cloud by combining division units. 
     The following describes other point cloud data division methods. When a space is divided equally as shown in (c) in  FIG. 45 , a divided space may include no points. In this case, the three-dimensional data encoding device combines the space including no points with another space including points. As a result, the three-dimensional data encoding device can form division units so that each of the division units includes one or more points. 
       FIG. 58  is a flowchart for data division in the above case. First, the three-dimensional data encoding device divides data using a specific method ( 55121 ). For example, the specific method is the above second division method. 
     Next, the three-dimensional data encoding device determines whether a current division unit that is a division unit to be processed includes points (S 5122 ). When the current division unit includes points (YES in S 5122 ), the three-dimensional data encoding device encodes the current division unit (S 5123 ). On the other hand, when the current division unit includes no points (NO in S 5122 ), the three-dimensional data encoding device combines the current division unit with another division unit including points, and encodes the combined division unit (S 5124 ). To put it another way, the three-dimensional data encoding device encodes the current division unit together with the other division unit including the points. 
     It should be noted that although the example of performing determination and combination for each division unit has been described above, a processing method is not limited to this. For example, the three-dimensional data encoding device may determine whether each of division units includes points, perform combination so that any division unit including no points will disappear, and encode each of the combined division units. 
     The following describes a method of transmitting data including a null tile. When a current tile that is a tile to be processed is a null tile, the three-dimensional data encoding device does not transmit data of the current tile.  FIG. 59  is a flowchart of a data transmission process. 
     First, the three-dimensional data encoding device determines a tile division method and divides point cloud data into tiles using the determined division method (S 5131 ). 
     Next, the three-dimensional data encoding device determines whether the current tile is a null tile (S 5132 ). In other words, the three-dimensional data encoding device determines whether no data is included in the current tile. 
     When the current tile is the null tile (YES in S 5132 ), the three-dimensional data encoding device includes a result of the determination that the current tile is the null tile, in tile additional information, and does not include information (tile position, size, etc.) about the current tile in the tile additional information (S 5133 ). In addition, the three-dimensional data encoding device does not transmit the current tile (S 5134 ). 
     On the other hand, when the current tile is not the null tile (NO in S 5132 ), the three-dimensional data encoding device includes a result of the determination that the current tile is not the null tile, in tile additional information, and includes information about each tile in the tile additional information (S 5135 ). In addition, the three-dimensional data encoding device transmits the current tile (S 5136 ). 
     As stated above, it is possible to reduce the amount of tile additional information by omitting information about a null tile from the tile additional information. 
     The following describes a method of decoding encoded data including a null tile. First, a process when there is no packet loss will be described. 
       FIG. 60  is a diagram illustrating an example of transmitted data that is encoded data transmitted by the three-dimensional data encoding device, and an example of received data inputted to the three-dimensional data decoding device. It should be noted that a system environment without packet loss is assumed here, and received data is identical to transmitted data. 
     When a system environment is free from packet loss, the three-dimensional data decoding device receives all transmitted data.  FIG. 61  is a flowchart of a process performed by the three-dimensional data decoding device. 
     First, the three-dimensional data decoding device refers to tile additional information (S 5141 ) and determines whether each of tiles is a null tile (S 5142 ). 
     When the tile additional information indicates that a current tile is not a null tile (NO in S 5142 ), the three-dimensional data decoding device determines that the current tile is not the null tile and decodes the current tile (S 5143 ). Finally, the three-dimensional data decoding device obtains information (position information (e.g., origin coordinates), size, etc. of the tiles) about the tiles from the tile additional information, and reconstructs three-dimensional data by combining the tiles using the obtained information (S 5144 ). 
     On the other hand, when the tile additional information indicates that a current tile is a null tile (YES in S 5142 ), the three-dimensional data decoding device determines that the current tile is the null tile and does not decode the current tile (S 5145 ). 
     It should be noted that the three-dimensional data decoding device may determine that missing data is a null tile, by sequentially analyzing index information indicated by the header of encoded data. In addition, the three-dimensional data decoding device may combine a determination method using tile additional information and a determination method using index information. 
     The following describes a process when there is packet loss.  FIG. 62  is a diagram illustrating an example of transmitted data from the three-dimensional data encoding device, and an example of received data inputted to the three-dimensional data decoding device. Here, a system environment with packet loss is assumed. 
     When packet loss occurs in a system environment, there is a possibility that the three-dimensional data decoding device cannot receive all transmitted data. In this example, packets of Gt 2  and At 2  are lost. 
       FIG. 63  is a flowchart of a process performed by the three-dimensional data decoding device in the above case. First, the three-dimensional data decoding device analyzes the continuity of index information indicated by the header of encoded data (S 5151 ) and determines whether an index number of a current tile is present (S 5152 ). 
     When the index number of the current tile is present (YES in S 5152 ), the three-dimensional data decoding device determines that the current tile is not a null tile and decodes the current tile (S 5153 ). Finally, the three-dimensional data decoding device obtains information (position information (e.g., origin coordinates), size, etc. of tiles) about tiles from tile additional information, and reconstructs three-dimensional data by combining the tiles using the obtained information (S 5154 ). 
     On the other hand, when the index number of the current tile is not present (NO in S 5152 ), the three-dimensional data decoding device refers to tile additional information (S 5155 ) and determines whether the current tile is a null tile (S 5156 ). 
     When the current tile is not the null tile (NO in S 5156 ), the three-dimensional data decoding device determines that the current tile is lost (packet loss) and performs error decoding (S 5157 ). Error decoding is, for example, a process of trying to decode original data assuming that the data existed. In this case, the three-dimensional data decoding device may regenerate three-dimensional data and reconstruct three-dimensional data (S 5154 ). 
     In contrast, when the current tile is the null tile (YES in S 5156 ), the three-dimensional data decoding device determines that the current tile is the null tile, and performs neither decoding nor the reconstruction of three-dimensional data (S 5158 ). 
     The following describes an encoding method when no null tiles are clearly shown. The three-dimensional data encoding device may generate encoded data and additional information using the following method. 
     The three-dimensional data encoding device does not include information about a null tile in tile additional information. The three-dimensional data encoding device appends index numbers of tiles other than the null tile to a data header. The three-dimensional data encoding device does not transmit the null tile. 
     In this case, a tile division number (number_of_tiles) indicates a division number excluding a null tile. It should be noted that the three-dimensional data encoding device may separately store information indicating the number of null tiles in a bitstream. In addition, the three-dimensional data encoding device may include information about a null tile in additional information or include part of information about a null tile in the additional information. 
       FIG. 64  is a flowchart of a three-dimensional data encoding process performed by the three-dimensional data decoding device in the above case. First, the three-dimensional data encoding device determines a tile division method and divides point cloud data into tiles using the determined division method (S 5161 ). 
     Next, the three-dimensional data encoding device determines whether a current tile is a null tile (S 5162 ). In other words, the three-dimensional data encoding device determines whether no data is included in the current tile. 
     When the current tile is not the null tile (NO in S 5162 ), the three-dimensional data encoding device appends index information of the current tile other than a null tile to a data header (S 5163 ). Finally, the three-dimensional data encoding device transmits the current tile (S 5164 ). 
     On the other hand, when the current tile is the null tile (YES in S 5162 ), the three-dimensional data encoding device neither appends index information of the current tile to a data header nor transmits the current tile. 
       FIG. 65  is a diagram illustrating an example of index information (idx) to be appended to a data header. As shown in  FIG. 65 , index information of any null tile is not appended, and serial numbers are put on tiles other than null tiles. 
       FIG. 66  is a diagram illustrating an example of a dependency relationship of each data. The pointed end of an arrow in the figure indicates a dependee, and the other end of the arrow indicates a depender. Moreover, in the figure, Gtn denotes geometry information for tile number n, and Atn denotes attribute information for tile number n, n being an integer from 1 to 4. Mtile denotes tile additional information. 
       FIG. 67  is a diagram illustrating a structural example of transmitted data that is encoded data transmitted by the three-dimensional data encoding device. 
     The following describes a decoding method when no null tiles are clearly shown.  FIG. 68  is a diagram illustrating an example of transmitted data from the three-dimensional data encoding device, and an example of received data inputted to the three-dimensional data decoding device. Here, a system environment with packet loss is assumed. 
       FIG. 69  is a flowchart of a process performed by the three-dimensional data decoding device in the above case. First, the three-dimensional data decoding device analyzes index information of tiles indicated by the header of encoded data, and determines whether an index number of a current tile is present. In addition, the three-dimensional data decoding device obtains a tile division number from tile additional information (S 5171 ). 
     When the index number of the current tile is present (YES in S 5172 ), the three-dimensional data decoding device decodes the current tile (S 5173 ). Finally, the three-dimensional data decoding device obtains information (position information (e.g., origin coordinates), size, etc. of the tiles) about the tiles from the tile additional information, and reconstructs three-dimensional data by combining the tiles using the obtained information (S 5175 ). 
     In contrast, when the index number of the current tile is not present (NO in S 5172 ), the three-dimensional data decoding device determines that the current tile is lost and performs error decoding (S 5174 ). In addition, the three-dimensional data decoding device determines that any space including no data is a null tile, and reconstructs three-dimensional data. 
     By clearly showing null tiles, the three-dimensional data encoding device can appropriately determine the absence of points in tiles, not data unavailability due to, for example, mismeasurement or data processing, or packet loss. 
     It should be noted that the three-dimensional data encoding device may use both a method of clearly showing null packets and a method of clearly showing no null packets. In this case, the three-dimensional data encoding device may include information indicating whether null packets are clearly shown, in tile additional information. Moreover, whether null packets are to be clearly shown may be determined in advance according to a type of a division method, and the three-dimensional data encoding device may indicate whether the null packets are to be clearly shown, by showing the type of the division method. 
     Although an example in which information regarding all tiles is included in tile additional information has been described in  FIG. 47  etc., information regarding some of tiles or information regarding null tiles of some of tiles may be included in tile additional information. 
     Moreover, although an example in which information regarding divided data such as information indicating whether divided data (tiles) are present is stored in tile additional information has been described, part or all of these pieces of information may be stored in a parameter set or may be stored as data. When these pieces of information are stored as data, for example, nal_unit_type denoting information indicating whether divided data are present may be defined, and the pieces of information may be stored in a NAL unit. Additionally, the pieces of information may be stored in both additional information and data. 
     Embodiment 6 
     Hereinafter, a process of quantization on a tile basis will be described. 
       FIG. 70  is a diagram showing a syntax example of GPS. As shown in  FIG. 70 , GPS includes an inter-tile duplicated point flag (UniqueBetweenTilesFlag). The inter-tile duplicated point flag is a flag that indicates whether or not there can be a point duplicated between tiles. 
       FIG. 71  is a flowchart of a three-dimensional data decoding process. First, three-dimensional data decoding device decodes UniqueBetweenTilesFlag and MergeDuplicatedPointFlag from the metadata included in the bitstream (S 6261 ). The three-dimensional data decoding device then decodes the attribute information and the geometry information for each tile to reconstruct the point cloud (S 6262 ). 
     The three-dimensional data decoding device then determines whether the merging of duplicated points is needed or not (S 6263 ). For example, the three-dimensional data decoding device determines whether the merging is needed or not based on whether an application can handle the duplicated points or not or whether the duplicated points should be merged or not. Alternatively, the three-dimensional data decoding device may perform a smoothing or filtering process on the plurality of pieces of attribute information on the duplicated points and determine to merge the duplicated points in order to remove noise or improve the estimation precision. 
     When the merging of duplicated points is needed (Yes in S 6263 ), the three-dimensional data decoding device determines whether there is a duplication between tiles (there is a point duplicated between tiles) or not (S 6264 ). For example, the three-dimensional data decoding device may determine whether there is a duplication between tiles or not based on the result of the decoding of UniqueBetweenTilesFlag and MergeDuplicatedPointFlag. This eliminates the need for the three-dimensional data decoding device to search for a duplicated point, and the processing load on the three-dimensional data decoding device can be reduced. Note that the three-dimensional data decoding device may determine whether there is a duplicated point or not by searching for a duplicated point after reconstruction of the tiles. 
     When there is a duplication between tiles (Yes in S 6264 ), the three-dimensional data decoding device merges the points duplicated between tiles (S 6265 ). The three-dimensional data decoding device then merges the plurality of duplicated pieces of attribute information (S 6266 ). 
     After Step S 6266 , or when there is not a duplication between tiles (No in S 6264 ), the three-dimensional data decoding device executes an application using the point cloud without a duplicated point (S 6267 ). 
     On the other hand, when the merging of duplicated points is not needed (No in S 6263 ), the three-dimensional data decoding device does not merge the duplicated points, and executes an application using the point cloud including duplicated points (S 6268 ). 
     In the following, an example of an application will be described. First, an example of an application that uses a point cloud without a duplicated point will be described. 
       FIG. 72  is a diagram showing an example of an application. The example shown in  FIG. 72  shows a use case where a moving body traveling from the area of tile A to the area of tile B downloads a map point cloud from a server in real time. The server stores encoded data of map point clouds of a plurality of overlapping areas. The moving body has already obtained map information on tile A and requests map information on tile B, which is located ahead in the direction of travel, from the server. 
     In this process, the moving body determines that the data on the part of tile B overlapping with tile A is unnecessary, and transmits to the server a direction to delete the part of tile B overlapping with tile A. The server deletes the overlapping part in tile B, and distributes the map information on tile B with the overlapping part deleted to the moving body. In this way, the amount of the transmission data and the load of the decoding process can be reduced. 
     Note that the moving body may confirm that there is no duplicated point based on a flag. If the moving body has not obtained the map information on tile A yet, the moving body requests the data on tile B in which the overlapping part is not deleted from the server. When the server does not have a capability of deleting a duplicated point, or when whether there is a duplicated point or not is unknown, the moving body can determine whether or not there are duplicated points by checking the distributed data, and merge duplicated points if there are duplicated points. 
     Next, an example of an application that uses a point cloud including duplicated points will be described. A moving body uploads map point cloud data obtained by LiDAR to a server in real time. For example, the moving body uploads data obtained for each tile to the server. In this case, although tile A and tile B have an overlapping area, the moving body on the encoding side does not merge the points duplicated between tiles but transmits data along with a flag indicating that there is a duplication between tiles to the server. The server does not merge duplicated data included in the received data and accumulates the received data without change. 
     When the point cloud data is transmitted or accumulated using a system, such as ISOBMFF, MPEG-DASH/MMT, or MPEG-TS, the device may replace the flag in GPS that indicates whether or not there are duplicated points in a tile or whether or not there are points duplicated between tiles with a describer or metadata in the system layer, and store the describer or metadata in SI, MPD, moov, or moof box, for example. This allows the application to use a function of the system. 
     As shown in  FIG. 73 , the three-dimensional data encoding device may divide tile B into a plurality of slices based on the areas overlapping with other tiles, for example. In the example shown in  FIG. 73 , slice  1  is an area that does not overlap with any other tile, slice  2  is an area that overlaps with tile A, and slice  3  is an area that overlaps with tile C. In this way, desired data can be more easily separated from the encoded data. 
     The map information may be point cloud data or mesh data. The point cloud data may be divided into tiles corresponding to different areas and saved in the server. 
       FIG. 74  is a flowchart showing a flow of a process performed by the system described above. First, a terminal (such as a moving body) detects a movement of the terminal from area A to area B (S 6271 ). The terminal then starts obtaining map information on area B (S 6272 ). 
     If the terminal has already downloaded information on area A (Yes in S 6273 ), the terminal requests, from the server, data on area B that includes no points duplicated between areas A and B (S 6274 ). The server deletes area A from area B, and transmits data on area B from which area A is deleted to the terminal (S 6275 ). Note that, depending on the direction from the terminal, the server may transmit data on area B by encoding the data so that no duplicated point occurs in real time. 
     The terminal then merges (combines) the map information on area B to the map information on area A, and displays the merged map information (S 6276 ). 
     On the other hand, if the terminal has not downloaded the information on area A yet (No S 6273 ), the terminal requests, from the server, data on area B that includes points duplicated between areas A and B (S 6277 ). The server transmits data on area B to the terminal (S 6278 ). The terminal then displays the map information on area B including points duplicated between areas A and B (S 6279 ). 
       FIG. 75  is a flowchart showing another example of the operation of the system. A transmission device (three-dimensional data encoding device) successively transmits data on tiles (S 6281 ). The transmission device adds, to data on a tile to be transmitted, a flag that indicates whether the tile of the data to be transmitted overlaps with the tile of the previously transmitted data, and then transmits the data (S 6282 ). 
     A receiving device (three-dimensional data decoding device) determines whether the tile of the received data overlaps with the tile of the previously received data based on the flag added to the data (S 6283 ). If the tile of the received data overlaps with the tile of the previously received data (Yes in S 6283 ), the receiving device deletes or merges the duplicated points (S 6284 ). On the other hand, if the tile of the received data does not overlap with the tile of the previously received data (No in S 6283 ), the receiving device does not perform the process of deleting or merging the duplicated points, and ends the process. In this way, the processing load on the receiving device can be reduced, and the precision of the estimation of the attribute information can be improved. Note that the receiving device need not to merge the duplicated points if the merging of the duplicated points is not needed. 
     Embodiment 7 
     In this embodiment, a display method based on a viewpoint, a random access method for encoded data, and encoding method and decoding method for point cloud data in an application using a point cloud will be described. 
     With the increasing capabilities of sensors, three-dimensional point clouds of higher qualities have become able to be obtained. However, in order to view a three-dimensional point cloud of high quality, a viewing device (viewer) that can reproduce the three-dimensional point cloud of high quality is needed. Specifically, it is desired to be able to display, without delay, a three-dimensional point cloud (point cloud) of high quality having a large data amount. In this embodiment, a viewer (first application) for a three-dimensional point cloud that can efficiently display point cloud data having high density in a scalable method using point cloud compression will be described. 
     Point cloud compression is achieved by a plurality of data division methods. For example, using levels of details (LoDs), a resolution required to represent point cloud data is calculated in accordance with the distance between a virtual camera and the point cloud data. In this way, separations into layers or classifications into layers are achieved. 
     A three-dimensional point cloud viewing device (referred to also as a three-dimensional data decoding device) selects a visible point cloud for rendering. At this point, the three-dimensional data decoding device preferably confirms that all visible points are data obtained by actual scanning rather than approximation. 
       FIG. 76  is a block diagram showing an example configuration of a three-dimensional data encoding device. The three-dimensional data encoding device includes point cloud encoder  8701  and file format generator  8702 . Point cloud encoder  8701  generates encoded data (bitstream) by encoding point cloud data. For example, point cloud encoder  8701  encodes point cloud data using a geometry information-based encoding method using an octree or a video-based encoding method, for example. 
     File format generator  8702  changes the encoded data (bitstream) into data in a predetermined file format. For example, the file format is ISOBMFF or MP4. Note that the three-dimensional data encoding device may output (transmit to a three-dimensional data decoding device, for example) encoded data in a file format or output encoded data in a bitstream format of the encoding type. 
       FIG. 77  is a block diagram showing an example configuration of three-dimensional data decoding device  8705 . Three-dimensional data decoding device  8705  generates point cloud data by decoding encoded data. Here, the encoded data is encoded data in a bitstream format or MP4 format, for example. Note that point cloud data that is not encoded can also be used. 
     The whole or a part of the group of data of a point cloud is referred to as a brick. Note that the brick may be referred to as divisional data, a tile, or a slice. Divisional data may be further divided. 
     Three-dimensional data decoding device  8705  obtains, from the outside, camera viewpoint information that indicates the viewpoint (angle) of a camera. Three-dimensional data decoding device  8705  generates point cloud data by obtaining the whole or a part of encoded data based on the camera viewpoint information and decoding the obtained encoded data. For example, the camera viewpoint information indicates the position and direction (orientation) of a camera. After that, three-dimensional data decoding device  8705  displays the decoded point cloud data. 
     Three-dimensional data decoding device  8705  includes point cloud decoder  8706  and brick decoding controller  8707 . The camera viewpoint information (camera view angle) is input to brick decoding controller  8707 . Brick decoding controller  8707  selects a brick to be decoded based on the visibility of bricks determined based on the camera viewpoint information. Point cloud decoder  8706  decodes the selected brick and outputs the decoded brick. 
     In the following, a configuration of a three-dimensional data encoding device according to this embodiment will be described.  FIG. 78  is a block diagram showing a configuration of three-dimensional data encoding device  8710  according to this embodiment. Three-dimensional data encoding device  8710  generates encoded data (encoded stream) by encoding point cloud data (point cloud). Three-dimensional data encoding device  8710  includes divider  8711 , a plurality of geometry information encoders  8712 , a plurality of attribute information encoders  8713 , additional information encoder  8714 , multiplexer  8715 , and normal vector generator  8716 . 
     Divider  8711  generates items of divisional data by dividing point cloud data. Specifically, divider  8711  generates items of divisional data by dividing a space of point cloud data into a plurality of subspaces. Here, a subspace is any of bricks, tiles, and slices, or a combination of two or more of bricks, tiles, and slices. More specifically, point cloud data includes geometry information, attribute information (such as color or reflectance), and additional information. Divider  8711  divides geometry information into items of divisional geometry information, and divides attribute information into items of divisional attribute information. Divider  8711  also generates additional information concerning the division. 
     The plurality of geometry information encoders  8712  generate items of encoded geometry information by encoding items of divisional geometry information. For example, geometry information encoders  8712  encode divisional geometry information using an N-ary tree structure, such as an octree. Specifically, in the case of an octree, a current space is divided into eight nodes (subspaces), and 8-bit information (occupancy code) that indicates whether each node includes a point cloud or not is generated. A node including a point cloud is further divided into eight nodes, and 8-bit information that indicates whether each of the eight nodes includes a point cloud or not is generated. This process is repeated until a predetermined layer is reached or the number of the point clouds included in each node becomes equal to or less than a threshold. For example, the plurality of geometry information encoders  8712  process items of divisional geometry information in parallel. 
     Attribute information encoder  8713  generates encoded attribute information, which is encoded data, by encoding attribute information using configuration information generated by geometry information encoder  8712 . For example, attribute information encoder  8713  determines a reference point (reference node) that is to be referred to in encoding of a current point (current node) to be processed based on the octree structure generated by geometry information encoder  8712 . For example, attribute information encoder  8713  refers to a node whose parent node in the octree is the same as the parent node of the current node, among peripheral nodes or neighboring nodes. Note that the method of determining a reference relationship is not limited to this method. 
     The process of encoding geometry information or attribute information may include at least one of a quantization process, a prediction process, and an arithmetic encoding process. In this case, “refer to” means using a reference node for calculation of a predicted value of attribute information or using a state of a reference node (occupancy information that indicates whether a reference node includes a point cloud or not, for example) for determination of a parameter of encoding. For example, the parameter of encoding is a quantization parameter in the quantization process or a context or the like in the arithmetic encoding. 
     Normal vector generator  8716  calculates a normal vector for each item of divisional data. Note that the input data need not be divided. In that case, normal vector generator  8716  may calculate a normal vector for each point, rather than a normal vector for each item of divisional data. Alternatively, normal vector generator  8716  may calculate both a normal vector for each item of divisional data and a normal vector for each point. 
     Additional information encoder  8714  generates encoded additional information by encoding additional information included in the point cloud data, the additional information concerning the data division generated in the division by divider  8711 , and the normal vector generated by normal vector generator  8716 . 
     Multiplexer  8715  generates encoded stream (encoded stream) by multiplexing the items of encoded geometry information, the items of encoded attribute information, and the encoded additional information, and transmits the generated encoded data. The encoded additional information is used in the decoding. 
     In the following, a configuration of a three-dimensional data decoding device according to this embodiment will be described.  FIG. 79  is a block diagram showing a configuration of three-dimensional data decoding device  8720 . Three-dimensional data decoding device  8720  reproduces point cloud data by decoding encoded data (encoded stream) generated by encoding the point cloud data. Three-dimensional data decoding device  8720  includes demultiplexer  8721 , a plurality of geometry information decoders  8722 , a plurality of attribute information decoders  8723 , additional information decoder  8724 , combiner  8725 , normal vector extractor  8726 , random access controller  8727 , and selector  8728 . 
     Demultiplexer  8721  generates items of encoded geometry information, items of encoded attribute information, and encoded additional information by demultiplexing encoded data (encoded stream). Additional information decoder  8724  generates additional information by decoding the encoded additional information. 
     Normal vector extractor  8726  extracts a normal vector from the additional information. Random access controller  8727  determines divisional data to be extracted based on the normal vector for each item of divisional data. Selector  8728  extracts items of divisional data (items of encoded geometry information and items of encoded attribute information) determined by random access controller  8727  from the items of divisional data (the items of encoded geometry information and the items of encoded attribute information). Note that selector  8728  may extract one item of divisional data. 
     The plurality of geometry information decoders  8722  generates items of divisional geometry information by decoding the items of encoded geometry information extracted by selector  8728 . For example, the plurality of geometry information decoders  8722  process the items of encoded geometry information in parallel. 
     The plurality of attribute information decoders  8723  generate items of divisional attribute information by decoding the items of encoded attribute information extracted by selector  8728 . For example, the plurality of attribute information decoders  8723  process the items of encoded attribute information in parallel. 
     Combiner  8725  generates geometry information by combining the items of divisional geometry information using the additional information. Combiner  8725  generates attribute information by combining the items of divisional attribute information using the additional information. 
     Next, a first example in which a normal vector for each point is generated and encoded will be described.  FIG. 80  is a diagram showing an example of point cloud data.  FIG. 81  is a diagram showing an example of a normal vector for each point. Encoding of normal vectors can be independently performed for each three-dimensional point cloud.  FIG. 80  and  FIG. 81  show a three-dimensional point cloud of a book and normal vectors for the three-dimensional point cloud. As shown in  FIG. 81 , there are a plurality of normal vectors extending upward, rightward, and forward. Here, the surfaces of the book are flat surfaces, and a plurality of normal vectors to a surface extend in the same direction. On the other hand, if the surface is round, the normal vectors extend in a plurality of directions in conformity with the normal to the surface. 
       FIG. 82  is a diagram showing a syntax example of a normal vector in a bitstream. Concerning Normal vector NormalVector[i][face] shown in  FIG. 82 , [i] represents a counter of each three-dimensional point cloud, and [face]represents an x-axis, a y-axis, or a z-axis that represents a three-dimensional point cloud. That is, NormalVector represents the magnitude of a normal vector along each axis. 
       FIG. 83  is a flowchart of a three-dimensional data encoding process. First, the three-dimensional data encoding device encodes geometry information (geometry) and attribute information for each point (S 8701 ). For example, the three-dimensional data encoding device encodes geometry information for each point. When there is attribute information corresponding to points, the three-dimensional data encoding device may encode attribute information for each point. 
     The three-dimensional data encoding device then encodes a normal vector (x, y, z) for each point (S 8702 ). The three-dimensional data encoding device may encode a normal vector for each point. The three-dimensional data encoding device may encode difference information that indicates the difference between a normal vector for a point to be processed and a normal vector for another point, for example. In that case, the data amount can be reduced. The three-dimensional data encoding device may encode a normal vector included in geometry information or a normal vector included in attribute information. The three-dimensional data encoding device may encode a normal vector independently from geometry information or attribute information. Note that when there are a plurality of normal vectors for one point, the three-dimensional data encoding device may encode a plurality of normal vectors for each point. 
       FIG. 84  is a flowchart of a three-dimensional data decoding process. First, the three-dimensional data decoding device decodes geometry information and attribute information for each point from the bitstream (S 8706 ). The three-dimensional data decoding device then decodes a normal vector for each point from the bitstream (S 8707 ). 
     The orders of processings shown in  FIG. 83  and  FIG. 84  are examples, and the encoding order and the decoding order can be changed. 
     The three-dimensional data encoding device may reduce the data amount by encoding a normal vector using geometry information or a correlation between items of geometry information. In that case, the three-dimensional data decoding device decodes a normal vector using geometry information. In the manner described above, a normal vector for each point in a point cloud can be encoded and decoded. 
     Next, a second example in which a normal vector for each point is generated and encoded will be described. In another method of encoding a normal vector for each point, a normal vector is encoded as an item of attribute information. In the following, an example will be described in which an attribute information encoder or an attribute information decoder encodes a normal vector as an item of attribute information. 
     For example, the three-dimensional data encoding device encodes color information as first attribute information and encodes a normal vector as second attribute information.  FIG. 85  is a diagram showing an example configuration of a bitstream. For example, Attr(0) shown in  FIG. 85  represents encoded data of first attribute information, and Attr(1) represents encoded data of second attribute information. Metadata concerning the encoding is stored in a parameter set (APS). The three-dimensional data decoding device decodes encoded data by referring to APS corresponding to the encoded data. 
     Note that SPS stores identification information (attribute_type=Normal Vector) that indicates that the second attribute information is a normal vector. When the attribute information is a normal vector, information that indicates that a normal vector is data having three elements for one point may be stored in SPS or the like. SPS also stores identification information (attribute_type=Color) that indicates that the first attribute information is color information. 
       FIG. 86  is a diagram showing an example of point cloud information including geometry information, color information, and a normal vector. The three-dimensional data encoding device encodes point cloud data that is not compressed shown in  FIG. 86 . 
     The value of a normal vector ranges from a floating-point value of −1 to a floating-point value of 1. To simplify the representation, the three-dimensional data encoding device may transform a floating-point value into an integer in accordance with the required precision. For example, the three-dimensional data encoding device may transform a floating-point value into a value from −127 to 128 using an 8-bit representation. That is, the three-dimensional data encoding device can transform a floating-point value into an integer value or a positive integer value. Since the normal vector is treated as an item of attribute information, a different quantization process can be applied to the normal vector. For example, a different quantization parameter may be used for each item of attribute information. In that case, different precision levels can be achieved. The quantization parameters are stored in APS, for example. 
       FIG. 87  is a flowchart of a three-dimensional data encoding process. The three-dimensional data encoding device encodes geometry information and attribute information (such as color information) for each point (S 8711 ). The three-dimensional data encoding device also encodes a normal vector for each point as an item of attribute information having attribute_type=“normal vector” in a predetermined method (S 8712 ). 
       FIG. 88  is a flowchart of a three-dimensional data decoding process. The three-dimensional data decoding device decodes geometry information and attribute information for each point from the bitstream (S 8716 ). The three-dimensional data decoding device also decodes a normal vector for each point as an item of attribute information having attribute_type=“normal vector” from the bitstream in a predetermined method (S 8717 ). 
     Note that the orders of processings shown in  FIG. 87  and  FIG. 88  are examples, and the encoding order and the decoding order can be changed. 
     Next, an example in which a normal vector is generated for each data unit including a plurality of points will be described. The three-dimensional data encoding device divides point cloud data into a plurality of objects or regions based on geometry information and characteristics of the point cloud. The divisional data is a tile or a slice, or layered data. The three-dimensional data encoding device generates a normal vector on a basis of the divisional data or, in other words, on a basis of a data unit containing one or more points. 
     Here, the visibility can be determined as the representation of a normal vector of an object in a brick.  FIG. 89  and  FIG. 90  are diagrams for illustrating this process. For example, as shown in  FIG. 89 , the three-dimensional data encoding device defines normal vector directions that are 300 apart from each other with respect to a horizontal axis and a vertical axis. In a simpler method, as shown in  FIG. 90 , the three-dimensional data encoding device may divide a normal vector into six directions (0, 0), (0, 90), (0, −90), (90, 0), (−90, 0), and (180, 180). 
     The three-dimensional data encoding device may calculate an effective normal vector by using a median, an average, or other more effective algorithm. The three-dimensional data encoding device may use a representative value as an effective normal vector or represent an effective normal vector in other ways. 
     A normal vector for each item of divisional data may be represented by original values of x, y, and z, quantized every 30 degrees as described above, or quantized every 90 degrees. The information amount can be reduced by the quantization. 
       FIG. 91  is a diagram showing a human face as an example object, which is an example of point cloud data.  FIG. 92  is a diagram showing an example of normal vectors in this case. As shown in  FIG. 92 , normal vectors of the human face object shown in  FIG. 91  are oriented in the (0, 0) direction and the (90, 0) direction. The three-dimensional data encoding device can indicate whether an object has a normal vector in a direction by using one bit for each direction. 
     As described above, there may be two or more normal vectors for one item of divisional data. In that case, a plurality of normal vectors may be indicated for one divisional data unit. 
     For example, in the example of data including a human face object shown in  FIG. 91  and  FIG. 92 , the data has six normal vectors provided on a 90-degree basis, one normal vector for each face. In this example, two normal vectors in the (0, 0) direction and the (90, 0) direction are normal vectors for the divisional data. 
     In a method of indicating a normal vector, each of the six normal vectors may be represented by 1-bit information.  FIG. 93  is a diagram showing an example of such normal vector information. The 1-bit information is set at a value of 1 when the divisional data has a corresponding normal vector, and is set at a value of 0 when the divisional data has no corresponding normal vector. In this way, compared with the method of indicating the values of x, y, and z as they are, the information amount can be reduced since the data is quantized. 
     In the following, a simpler method of representing a normal vector will be described. A normal vector and the feasibility (visibility) thereof are represented with respect to a particular camera viewpoint using a cube having six faces.  FIG. 94  to  FIG. 97  are diagrams for illustrating this process.  FIG. 94  shows an example of a cube having six faces.  FIG. 95 ,  FIG. 95 , and  FIG. 96  are a diagram showing front face a and rear face b, a diagram showing left face c and right face d, and a diagram showing top face e and bottom face f, respectively. Depending on the orientation of the object depending on the view angle, a normal vector may pass through at least one or three faces. Any of the six faces (a, b, c, d, e, and f) of the cube representing each system can be represented using six 1-bit flags. For example, (100000) is generated when the object is viewed from the front, (001000) is generated when the object is viewed from a side, and (000001) is generated when the object is viewed from the bottom. In this representation, magnitude is not significant, and only directions are represented. An object for which three faces are designated may occur. Face a is opposite to face b, face c is opposite to face d, and face e is opposite to face f. Therefore, face a and face b cannot be seen at the same time. That is, a normal vector can be represented using three flags (ace). 
     As described above, when the camera viewpoint (camera angle) is known in advance, information on a normal vector can be represented by three bits.  FIG. 98  is a diagram showing the visibility at the time when objects of slice A and slice B are viewed from the direction of face c. Slice A is visible from the direction of face c, and therefore is represented by ace=(010). On the other hand, slice B is hidden behind slice A when viewed from the direction of face c, and therefore is represented by ace=(000). 
     Next, a first method in which a normal vector is encoded and decoded for each brick will be described.  FIG. 99  is a diagram showing an example configuration of a bitstream in this case. In the example shown in  FIG. 99 , information on a normal vector is stored in a slide header of geometry information on each slice. Note that the information on a normal vector may be stored in a header of attribute information or may be stored in metadata that is independent from the geometry information and the attribute information. 
       FIG. 100  is a diagram showing a syntax example of a slice header of geometry information (Geometry slice header information). The slice header of geometry information includes normal_vector_number, normal_vector_x, normal_vector_y, and normal_vector_z. 
     normal_vector_number indicates the number of normal vectors corresponding to slice data. normal_vector_x, normal_vector_y, and normal_vector_z represent elements (x, y, z) of a normal vector corresponding to slice data, respectively. 
     In this example, the number of elements normal_vector can be changed. As many elements normal_vector as indicated by normal_vector_number are shown. 
     Note that when the information on normal vectors is common to all slices, normal_vector_number may be stored in GPS or SPS that can store information common to a plurality of slices. 
     The values of x, y, and z of a normal vector may be quantized. For example, the three-dimensional data encoding device may quantize the original values of a normal vector by shifting the values by common bit amount s (bit), and transmit information that indicates bit amount s and information that indicates the quantized normal vector (normal_vector_x «s, normal_vector_y «s, and normal_vector_z «z). In this way, the bit amount can be reduced. 
       FIG. 101  is a diagram showing another syntax example of a slice header of geometry information. In this example, a normal vector simplified (quantized) into six-face data is shown for each item of divisional data. Whether there is a normal vector or not is indicated for each face. 
     The slice header of geometry information includes is_normal_vector. is_normal_vector is set at 1 when there is a normal vector corresponding to the slice data, and is set at 0 when there is no normal vector corresponding to the slice data. For example, the order of the faces is predetermined. 
     Note that the precision of the quantization and the number or order of normal vectors are not limited to these. The precision of the quantization and the number or order of normal vectors may be fixed or variable. 
       FIG. 102  is a flowchart of a three-dimensional data encoding process. First, the three-dimensional data encoding device generates items of divisional data by dividing point cloud data (S 8721 ). The three-dimensional data encoding device then encodes geometry information and attribute information for each item of divisional data (S 8722 ). The three-dimensional data encoding device then stores a normal vector for each item of divisional data in a slice header (S 8723 ). 
       FIG. 103  is a flowchart of a three-dimensional data decoding process. The three-dimensional data decoding device decodes geometry information and attribute information for each item of divisional data from a bitstream (S 8726 ). The three-dimensional data decoding device then decodes a normal vector for each item of divisional data from a slice header of the divisional data (S 8727 ). The three-dimensional data decoding device then integrates the items of divisional data (S 8728 ). 
       FIG. 104  is a flowchart of a three-dimensional data decoding process in the case where data is partially decoded. First, the three-dimensional data decoding device decodes a normal vector for each item of divisional data from a slice header of the divisional data (S 8731 ). The three-dimensional data decoding device then determines divisional data to be decoded based on the normal vectors, and decodes the determined divisional data (S 8732 ). The three-dimensional data decoding device then integrates the items of decoded divisional data (S 8733 ). 
     Next, a second example in which a normal vector is encoded and decoded for each brick will be described. Another method of encoding information on a normal vector is a method that uses metadata (such as SEI: supplemental enhancement information).  FIG. 105  is a diagram showing an example configuration of a bitstream. SEI may be included in the bitstream as shown in  FIG. 105 , or may be generated as a file different from the main encoded bitstream depending on how SEI is implemented in both the encoding device and the decoding device. 
       FIG. 106  is a diagram showing a syntax example of slice information (slice_information) included in SEI. Slice information includes number_of_slice, bounding_box_origin_x, bounding_box_origin_y, bounding_box_origin_z, bounding_box_width, bounding_box_height, bounding_box_depth, normalVector_QP, number_of_normal_vector, normal_vectorx, normal_vector_y, and normal_vector_z. 
     number_of_slice indicates the number of items of divisional data. bounding_box_origin_x, bounding_box_origin_y, and bounding_box_origin_z indicate coordinates of the origin of a bounding box of slice data. bounding_box_width, bounding_box_height, and bounding_box_depth indicate the width, the height, and the depth of a bounding box of slice data, respectively. normalVector_QP indicates scale information or bit shift information of the quantization when normal_vector is quantized. number_of_normal_vector indicates the number of normal vectors included in slice data. normal_vector_x, normal_vector_y, and normal_vector_z indicate elements or components (x, y, z) of a normal vector, respectively. 
       FIG. 107  is a diagram showing another example of slice information included in SEI. In the example shown in  FIG. 107 , a normal vector simplified (quantized) into six-face data is shown for each item of divisional data. Whether there is a normal vector or not is indicated for each face. 
     The slice information includes is_normal_vector. is_normal_vector is set at 1 when there is a normal vector corresponding to the slice data, and is set at 0 when there is no normal vector corresponding to the slice data. For example, the order of the faces is predetermined. 
     Note that the slice information may include a flag that indicates whether or not the slice information includes information (origin, width, height, and depth) on the bounding box of each slice. In that case, the slice information include information on the bounding box of each slice when the flag is on (such as 1), and does not include information on the bounding box of each slice when the flag is off (such as 0). The slice information may include a flag that indicates whether or not the slice information includes information on a normal vector of each slice. In that case, the slice information includes information on a normal vector of each slice when the flag is on (such as 1), and does not include information on a normal vector of each slice when the flag is off (such as 0). 
     Next, random access and partial decoding will be described. The three-dimensional data decoding device independently decode data of each slice by using one or both of information on the slice, such as bounding box information on and a normal vector of the slice. 
       FIG. 108  is a flowchart of a three-dimensional data decoding process. First, the three-dimensional data decoding device determines the slices to be decoded and the decoding order of the slices in a predetermined manner (S 8741 ). The three-dimensional data decoding device decodes particular slices in the determined order (S 8742 ). 
       FIG. 109  is a diagram showing an example of this partial decoding process. For example, the three-dimensional data decoding device receives encoded data divided into slices shown in (a) in  FIG. 109 . As shown in (b) in  FIG. 109 , the three-dimensional data decoding device decodes encoded data of some slices and does not decode encoded data of the other slices. Alternatively, as shown in (c) in  FIG. 109 , the three-dimensional data decoding device decodes items of encoded data by changing the order of the items of encoded data. 
       FIG. 110  is a diagram showing an example configuration of a three-dimensional data decoding device. As shown in  FIG. 110 , the three-dimensional data decoding device includes attribute information decoder  8731  and random access controller  8732 . Attribute information decoder  8731  extracts bounding box information and a normal vector for each slice from encoded data. Random access controller  8732  determines the identification numbers and orders of slices to be decoded based on the bounding box information and the normal vector for each slice and sensor information obtained from the outside, such as camera angle (camera orientation) and camera position. 
       FIG. 111  and  FIG. 112  are diagrams showing example processes performed by random access controller  8732 . As shown in  FIG. 111 , for example, random access controller  8732  may calculate distance information that indicates the distance between each slice and the camera from the bounding box of the slice and the camera position. Alternatively, as shown in  FIG. 112 , random access controller  8732  may derive, for each slice, visibility information that indicates whether the object is visible from the camera or not from the normal vector of the slice and the camera angle. Note that random access controller  8732  may calculate one or both of the distance information and the visibility information. 
     In the following, the visibility information and the distance information will be described.  FIG. 113  is a diagram showing an example of a relationship between distance and resolution. For example, a thing that is visible from the camera is decoded (frustum culling). Furthermore, the resolution of the decoding depends on the distance between the virtual camera and the point cloud data. 
     That is, the three-dimensional data decoding device determines whether or not each slice is visible from the camera based on the normal vector of the slice and the camera viewpoint (camera angle), and decodes any slice that is visible from the camera. Furthermore, the three-dimensional data decoding device may calculate the distance between the slice to be decoded and the camera, and decode data of high resolution when the distance from the camera is short and decode data of low resolution when the distance from the camera is long. 
     Note that in this case, the encoded data is layered when the data is encoded, and the three-dimensional data decoding device can independently decode data of low resolution. When decoding data of high resolution, the three-dimensional data decoding device decode difference information between the data of low resolution and the data of high resolution, and generates the data of high resolution by adding the difference information to the data of low resolution. Note that when the encoded data is not layered when the data is encoded, the three-dimensional data decoding device need not perform this process, and may determine whether to perform this process or not based on whether the data is layered or not. 
     Next, the determination of the visibility based on the normal vector will be described.  FIG. 114  is a diagram showing an example of bricks and normal vectors. In the example shown in  FIG. 114 , two bricks (such as slices) at the front facing the camera (frustum) or, in other words, the bricks having a normal vector extending toward the camera are decoded. 
     First, the three-dimensional data decoding device determines, for each item of slice data, whether or not one or more normal vectors included in the metadata include a normal vector extending in the opposite direction to the camera direction. If the slice data of the current slice includes a normal vector extending in the opposite direction to the camera direction, the three-dimensional data decoding device determines the current slice to be visible, and determines the current slice as a target of decoding. 
     Note that when there is another slice between the camera and the current slice, the three-dimensional data decoding device may determine the current slice to be invisible (that is, to be unable to be seen). The three-dimensional data decoding device may determine whether a slice is visible or not by determining whether the angle of the normal vector with respect to the camera direction falls within a predetermined angle range or not, rather than by determining whether the direction of the normal vector and the camera direction are exactly opposite to each other or not. 
     Next, a process using Level of Detail (LoD) will be described. In the following, an example of a decoding process for layers of different resolutions will be described. 
       FIG. 115  is a diagram showing an example of levels (LoDs).  FIG. 116  is a diagram showing an example of an octree structure. Each brick is divided into layers in order to control the levels of resolution to be decoded. For example, the level is the depth of division in the division into octrees. As shown in  FIG. 115 , the number of voxels (Voxels) included in each level may be defined as 2 (3×level) . Note that the level-based division method and the number of voxels may be defined in other manners. 
     By using LoD, the three-dimensional data decoding device can achieve a quick visibility determination and a quick distance calculation. The decoding time affects the real-time rendering. Using LoD allows display of an intermediate brick, so that real-time rendering and smooth interaction can be achieved. 
       FIG. 117  is a flowchart of a three-dimensional data decoding process using LoD. First, the three-dimensional data decoding device determines a level to be decoded in accordance with the purpose (S 8751 ). The three-dimensional data decoding device then decodes a first level (level 0) (S 8752 ). The three-dimensional data decoding device then determines whether or not decoding of all levels to be decoded has been completed (S 8753 ). When decoding of all levels has not been completed (No in S 8753 ), the three-dimensional data decoding device decodes the subsequent level (S 8754 ). In this step, the three-dimensional data decoding device may decode the subsequent level using data of the previous level. When decoding of all levels to be decoded has been completed (Yes in S 8753 ), the three-dimensional data decoding device displays the decoded data (S 8755 ). 
     As described above, the three-dimensional data decoding device decodes data up to the determined level, and does not decode data for the levels following the determined level. In this way, the processing amount involved with the decoding can be reduced, and the processing speed can be increased. In addition, the three-dimensional data decoding device displays data up to the determined level, and does not display data for the levels following the determined level. In this way, the processing amount involved with the display can be reduced, and the processing speed can be increased. Note that the three-dimensional data decoding device may determine the level to be decoded of the brick based on the distance between the brick and the camera or based on whether the brick is visible from the camera or not, for example. 
     Next, an example implementation of the process using LoD will be described.  FIG. 118  is a flowchart of a three-dimensional data decoding process. First, the three-dimensional data decoding device obtains encoded data (S 8761 ). For example, the encoded data is point cloud data compressed by encoding in an arbitrary encoding method. The encoded data may be in the bitstream format or the file format. 
     The three-dimensional data decoding device then obtains, from the encoded data, a normal vector of and geometry information on a brick to be processed (S 8762 ). For example, the three-dimensional data decoding device obtains a normal vector for each brick and geometry information on the brick from metadata (SEI or data header) included in the encoded data. Note that the three-dimensional data decoding device may determine the distance between the brick and the camera based on the geometry information on the brick and information on the camera position. The three-dimensional data decoding device may determine the visibility of the brick (whether the brick is oriented in the direction of the camera or not) based on the normal vector and the camera direction. 
     The three-dimensional data decoding device then determines which brick is to be decoded, and decodes a first level (level 0) of the determined brick (S 8763 ).  FIG. 119  is a diagram showing an example of a brick to be decoded. As shown in  FIG. 119 , the three-dimensional data decoding device decodes all visible bricks with a resolution of level 0. 
     The three-dimensional data decoding device then determines whether to decode the subsequent level of each brick or not based on the geometry information, and decodes the subsequent level of the brick determined to be decoded (S 8764 ). This process is repeated until the decoding processing of all levels is completed (S 8765 ). Specifically, the resolution of bricks closer to the position of the virtual camera is set to be higher. For example, in accordance with the resource of the memory or the like, levels to be decoded are gradually added by giving priority to bricks closer to the camera. 
       FIG. 120  is a diagram showing an example of levels to be decoded of each brick. As shown in  FIG. 120 , the three-dimensional data decoding device decodes bricks closer to the camera with higher resolutions and decodes bricks farther from the camera with lower resolutions, in accordance with the distance from the camera. The three-dimensional data decoding device does not decode any invisible brick. 
     When decoding of all levels has been completed (Yes in S 8765 ), the three-dimensional data decoding device outputs the obtained three-dimensional point cloud (S 8766 ). 
     A method has been described above in which the three-dimensional data encoding device calculates and encodes a normal vector and bounding box information for each item of slice data, and the three-dimensional data decoding device calculates visibility and distance information based on the information and sensor input information to determine the slice to be decoded. In the following, an example will be described in which the three-dimensional data encoding device calculates and encodes visibility and distance information for the camera direction for data of each slice in advance. 
       FIG. 121  is a diagram showing a syntax example of a slice header of geometry information (Geometry slice header information). The slice header of geometry information includes number_of_angle, view_angle, and visibility. 
     number_of_angle indicates the number of camera angles (camera directions). view_angle indicates the camera angle, such as the vector of a camera angle. visibility indicates whether a slice is visible from the relevant camera angle or not. Note that the number of elements view_angle may be variable or a predetermined fixed value. When the number of elements view_angle and the value of view_angle are predetermined, view_angle may be omitted. 
     Although an example in which visibility for the camera angle is indicated has been shown here, in another example, the three-dimensional data encoding device may calculate visibility for the camera position or a camera parameter in advance, and store the calculated visibility in the encoded data. 
       FIG. 122  is a flowchart of a three-dimensional data encoding process. First, the three-dimensional data encoding device divides point cloud data into items of divisional data (such as slices) (S 8771 ). The three-dimensional data encoding device then encodes geometry information and attribute information on a basis of the divisional data (S 8772 ). The three-dimensional data encoding device stores visibility information (visibility) for the camera angle in metadata for each item of divisional data (S 8773 ). 
       FIG. 123  is a flowchart of a three-dimensional data decoding process. First, the three-dimensional data decoding device obtains visibility information for the camera angle from metadata of each item of divisional data (S 8776 ). The three-dimensional data decoding device then determines, based on the visibility information, divisional data that is visible from a desired camera angle, and decodes the divisional data that is visible (S 8777 ). 
       FIG. 124  and  FIG. 125  are diagrams showing examples of point cloud data. In these drawings, a, c, d, and e each denote a plane. Therefore, the three-dimensional data encoding device can perform the slice division by taking advantage of the fact that the three-dimensional points of each slice have normal vectors extending in the same direction. The same approach can be applied to the tile division. 
       FIG. 126  to  FIG. 129  are diagrams showing example configurations of a system including a three-dimensional data encoding device, a three-dimensional data decoding device, and a display device. 
     In the example shown in  FIG. 126 , the three-dimensional data encoding device generates encoded data by encoding slice data, and a normal vector and bounding box information for each slice. The three-dimensional data decoding device identifies data to be decoded based on the encoded data and sensor information, and generates decoded slice data by decoding the identified data. The display device displays the decoded slice data. With this configuration, the three-dimensional data decoding device can flexibly determine camera viewpoint information and whether to perform decoding or not. 
     In the example shown in  FIG. 127 , the three-dimensional data encoding device generates encoded data by encoding slice data, and a normal vector and bounding box information for each slice. The three-dimensional data decoding device determines data to be decoded and the order of decoding based on the encoded data and sensor information, and decodes the determined data in the determined order. With this configuration, the three-dimensional data decoding device can first decode data ( 3 ,  4 , and  5 , for example) that need to be displayed first, and therefore, the ease of viewing of the display can be improved. 
     In the example shown in  FIG. 128 , the three-dimensional data encoding device generates encoded data by encoding slice data, and visibility information for each camera angle. The three-dimensional data decoding device identifies data to be decoded based on the encoded data information and sensor information, and decodes the identified data. Note that the three-dimensional data decoding device may further determine the order of decoding. With this configuration, the three-dimensional data decoding device need not calculate visibility information, so that the processing amount of the three-dimensional data decoding device can be reduced. 
     In the example shown in  FIG. 129 , the three-dimensional data decoding device notifies the three-dimensional data encoding device of the camera angle, the camera position or the like of the three-dimensional data decoding device by communication or the like. The three-dimensional data encoding device calculates visibility information for each slice, determines data to be encoded and the order of encoding, and generates encoded data by encoding the determined data in the determined order. The three-dimensional data decoding device directly decodes the received slice data. With this configuration that allow interactions, required parts are encoded and decoded, so that the processing amount and the communication band can be reduced. 
     Note that when the camera position or the camera angle varies, the three-dimensional data decoding device may determine the slice to be decoded again when the variation exceeds a predetermined value. In that case, quick decoding and display can be achieved if differential data excluding the data already decoded is decoded. 
     In the following, a method of storing encoded data in a file format, such as ISOBMFF, will be described.  FIG. 130  is a diagram showing an example configuration of a bitstream.  FIG. 131  is a diagram showing an example configuration of a three-dimensional data encoding device. The three-dimensional data encoding device includes encoder  8741  and file transformer  8742 . Encoder  8741  generates a bitstream including encoded data and control information by encoding point cloud data. File transformer  8742  transforms the bitstream into a file format. 
       FIG. 132  is a diagram showing an example configuration of a three-dimensional data decoding device. The three-dimensional data decoding device includes file inverse transformer  8751  and decoder  8752 . File inverse transformer  8751  transforms a file format into a bitstream including encoded data and control information. Decoder  8752  generates point cloud data by decoding the bitstream. 
       FIG. 133  is a diagram showing a basic structure of ISOBMFF.  FIG. 134  is a diagram showing a protocol stack in a case where a common PCC codec NAL unit is stored in ISOBMFF. Here, what is stored in ISOBMFF is a PCC codec NAL unit. 
     NAL units include NAL units for data and NAL units for metadata. NAL units for data include geometry information slice data (Geometry Slice Data) and attribute information slice data (Attribute Slice Data). NAL units for metadata include SPS, GPS, APS, and SEI, for example. 
     ISO based media file format (ISOBMFF) is a file format standard prescribed in ISO/LEC14496-12. ISOBMFF is a standard that does not depend on any medium, and prescribes a format that allows various media, such as a video, an audio, and a text, to be multiplexed and stored. 
     A basic unit of ISOBMFF is a box. A box is formed by type, length, and data, and a file is a set of various types of boxes. A file mainly includes boxes, such as ftyp that indicates the brand of the file by 4CC, moov that stores metadata, such as control information, and mdat that stores data. 
     A method for storing each medium in ISOBMFF is separately prescribed. For example, a method of storing an AVC video or an HEVC video is prescribed in ISO/IEC14496-15. Here, it can be contemplated to expand the functionality of ISOBMFF and use ISOBMFF to accumulate or transmit PCC-encoded data. 
     When storing a NAL unit for metadata in ISOBMFF, SEI may be stored in “mdat box” along with PCC data, or may be stored in “track box” that describes control information concerning the stream. When packetizing and transmitting data, SEI may be stored in the packet header. By indicating SEI in a system layer, attribute information, tiles and slice data can be more easily accessed, and the access speed is improved. 
     Next, a method of generating a PCC random access table will be described. The three-dimensional data encoding device generates a random access table using metadata including bounding box information and normal vector information for each slice.  FIG. 135  is a diagram showing an example of a transform of a bitstream into a file format. 
     The three-dimensional data encoding device stores each item of slice data in mdat of the file format. The three-dimensional data encoding device calculates a memory location of the slice data as offset information (offsets  1  to  4  in  FIG. 135 ) on the beginning of the file, and includes the calculated offset information in the random access table (PCC random access table). 
       FIG. 136  is a diagram showing a syntax example of slice information (slice_information).  FIG. 137  to  FIG. 139  are diagrams showing syntax examples of a PCC random access table. 
     The PCC random access table includes bounding box information (bounding_box_info), normal vector information (normal_vector_info), and offset information (offset) stored in slice information (slice_information). 
     The three-dimensional data decoding device analyzes the PCC random access table, and identifies a slice to be decoded. The three-dimensional data decoding device can access desired data by obtaining the offset information from the PCC random access table. 
     As described above, the three-dimensional data encoding device according to this embodiment performs the process shown in  FIG. 140 . The three-dimensional data encoding device generates a bitstream by encoding geometry information and one or more items of attribute information on each of a plurality of three-dimensional points included in point cloud data (S 8781 ). In the encoding (S 8781 ), the three-dimensional data encoding device encodes a normal vector of each of the plurality of three-dimensional points as an item of attribute information included in the one or more items of attribute information. 
     With this configuration, the three-dimensional data encoding device encodes a normal vector as attribute information, and therefore can process the normal vector in the same manner as other attribute information. Therefore, the three-dimensional data encoding device can reduce the processing amount. That is, the three-dimensional data encoding device can encode a normal vector as attribute information without changing the definition or the like of the attribute information. 
     For example, in the encoding (S 8781 ), the three-dimensional data encoding device encodes a normal vector represented by a floating-point number after transforming the normal vector into an integer. Therefore, the three-dimensional data encoding device can process the normal vector in the same manner as other attribute information when other attribute information is represented by an integer, for example. 
     For example, the bitstream includes control information (such as SPS) common to geometry information and one or more items of attribute information, and the control information (such as SPS) includes at least one of information (such as attribute_type=Normal Vector) that indicates that an item of attribute information included in the one or more items of attribute information is a normal vector and information that indicates that a normal vector is data having three elements for one point. 
     For example, the three-dimensional data encoding device includes a processor and a memory, and the processor performs the process described above using the memory. 
     The three-dimensional data decoding device according to this embodiment performs the process shown in  FIG. 141 . The three-dimensional data decoding device obtains a bitstream generated by encoding geometry information and one or more items of attribute information on each of a plurality of three-dimensional points included in point cloud data, a normal vector of each of the plurality of three-dimensional points being encoded in the bitstream as an item of attribute information included in the one or more items of attribute information (S 8786 ), and obtains a normal vector by decoding an item of attribute information from the bitstream (S 8787 ). 
     With this configuration, the three-dimensional data decoding device decodes a normal vector as attribute information, and therefore can process the normal vector in the same manner as other attribute information. Therefore, the three-dimensional data decoding device can reduce the processing amount. 
     For example, in the obtaining of a normal vector (S 8787 ), the three-dimensional data decoding device obtains a normal vector represented by an integer. Therefore, the three-dimensional data decoding device can process the normal vector in the same manner as other attribute information when other attribute information is represented by an integer, for example. 
     For example, the bitstream includes control information (such as SPS) common to geometry information and one or more items of attribute information, and the control information (such as SPS) includes at least one of information (such as attribute_type=Normal Vector) that indicates that an item of attribute information included in the one or more items of attribute information is a normal vector and information that indicates that a normal vector is data having three elements for one point. 
     For example, the three-dimensional data decoding device includes a processor and a memory, and the processor performs the process described above using the memory. 
     The three-dimensional data encoding device according to this embodiment performs the process shown in  FIG. 142 . The three-dimensional data encoding device divides point cloud data into items of divisional data (such as bricks, slices, or tiles) (S 8791 ), and generates a bitstream by encoding the items of divisional data (S 8792 ). The bitstream includes information that indicates a normal vector for each of the items of divisional data. 
     By encoding a normal vector for each item of divisional data in this way, the three-dimensional data encoding device can reduce the processing amount and the code amount compared with the case where a normal vector is encoded for each point. For example, each of the items of divisional data is a unit of random access. 
     For example, the three-dimensional data encoding device includes a processor and a memory, and the processor performs the process described above using the memory. 
     The three-dimensional data decoding device according to this embodiment performs the process shown in  FIG. 143 . The three-dimensional data decoding device obtains a bitstream generated by encoding items of divisional data (such as bricks, slices, or tiles) generated by dividing point cloud data (S 8796 ), and obtains, from the bitstream, information that indicates a normal vector for each of the items of divisional data (S 8797 ). 
     By decoding a normal vector for each item of divisional data in this way, the three-dimensional data decoding device can reduce the processing amount compared with the case where a normal vector is decoded for each point. For example, each of the items of divisional data is a unit of random access. 
     For example, the three-dimensional data decoding device further determines divisional data to be decoded among the items of divisional data based on the normal vectors, and decodes the divisional data to be decoded. 
     For example, the three-dimensional data decoding device further determines the order of decoding of the items of divisional data based on the normal vectors, and decodes the items of divisional data in the determined order. 
     For example, the three-dimensional data decoding device includes a processor and a memory, and the processor performs the process described above using the memory. 
     Embodiment 8 
     The following describes the structure of three-dimensional data creation device  810  according to the present embodiment.  FIG. 144  is a block diagram of an exemplary structure of three-dimensional data creation device  810  according to the present embodiment. Such three-dimensional data creation device  810  is equipped, for example, in a vehicle. Three-dimensional data creation device  810  transmits and receives three-dimensional data to and from an external cloud-based traffic monitoring system, a preceding vehicle, or a following vehicle, and creates and stores three-dimensional data. 
     Three-dimensional data creation device  810  includes data receiver  811 , communication unit  812 , reception controller  813 , format converter  814 , a plurality of sensors  815 , three-dimensional data creator  816 , three-dimensional data synthesizer  817 , three-dimensional data storage  818 , communication unit  819 , transmission controller  820 , format converter  821 , and data transmitter  822 . 
     Data receiver  811  receives three-dimensional data  831  from a cloud-based traffic monitoring system or a preceding vehicle. Three-dimensional data  831  includes, for example, information on a region undetectable by sensors  815  of the own vehicle, such as a point cloud, visible light video, depth information, sensor position information, and speed information. 
     Communication unit  812  communicates with the cloud-based traffic monitoring system or the preceding vehicle to transmit a data transmission request, etc. to the cloud-based traffic monitoring system or the preceding vehicle. 
     Reception controller  813  exchanges information, such as information on supported formats, with a communications partner via communication unit  812  to establish communication with the communications partner. 
     Format converter  814  applies format conversion, etc. on three-dimensional data  831  received by data receiver  811  to generate three-dimensional data  832 . Format converter  814  also decompresses or decodes three-dimensional data  831  when three-dimensional data  831  is compressed or encoded. 
     A plurality of sensors  815  are a group of sensors, such as visible light cameras and infrared cameras, that obtain information on the outside of the vehicle and generate sensor information  833 . Sensor information  833  is, for example, three-dimensional data such as a point cloud (point group data), when sensors  815  are laser sensors such as LiDARs. Note that a single sensor may serve as a plurality of sensors  815 . 
     Three-dimensional data creator  816  generates three-dimensional data  834  from sensor information  833 . Three-dimensional data  834  includes, for example, information such as a point cloud, visible light video, depth information, sensor position information, and speed information. 
     Three-dimensional data synthesizer  817  synthesizes three-dimensional data  834  created on the basis of sensor information  833  of the own vehicle with three-dimensional data  832  created by the cloud-based traffic monitoring system or the preceding vehicle, etc., thereby forming three-dimensional data  835  of a space that includes the space ahead of the preceding vehicle undetectable by sensors  815  of the own vehicle. 
     Three-dimensional data storage  818  stores generated three-dimensional data  835 , etc. 
     Communication unit  819  communicates with the cloud-based traffic monitoring system or the following vehicle to transmit a data transmission request, etc. to the cloud-based traffic monitoring system or the following vehicle. 
     Transmission controller  820  exchanges information such as information on supported formats with a communications partner via communication unit  819  to establish communication with the communications partner. Transmission controller  820  also determines a transmission region, which is a space of the three-dimensional data to be transmitted, on the basis of three-dimensional data formation information on three-dimensional data  832  generated by three-dimensional data synthesizer  817  and the data transmission request from the communications partner. 
     More specifically, transmission controller  820  determines a transmission region that includes the space ahead of the own vehicle undetectable by a sensor of the following vehicle, in response to the data transmission request from the cloud-based traffic monitoring system or the following vehicle. Transmission controller  820  judges, for example, whether a space is transmittable or whether the already transmitted space includes an update, on the basis of the three-dimensional data formation information to determine a transmission region. For example, transmission controller  820  determines, as a transmission region, a region that is: a region specified by the data transmission request; and a region, corresponding three-dimensional data  835  of which is present. Transmission controller  820  then notifies format converter  821  of the format supported by the communications partner and the transmission region. 
     Of three-dimensional data  835  stored in three-dimensional data storage  818 , format converter  821  converts three-dimensional data  836  of the transmission region into the format supported by the receiver end to generate three-dimensional data  837 . Note that format converter  821  may compress or encode three-dimensional data  837  to reduce the data amount. 
     Data transmitter  822  transmits three-dimensional data  837  to the cloud-based traffic monitoring system or the following vehicle. Such three-dimensional data  837  includes, for example, information on a blind spot, which is a region hidden from view of the following vehicle, such as a point cloud ahead of the own vehicle, visible light video, depth information, and sensor position information. 
     Note that an example has been described in which format converter  814  and format converter  821  perform format conversion, etc., but format conversion may not be performed. 
     With the above structure, three-dimensional data creation device  810  obtains, from an external device, three-dimensional data  831  of a region undetectable by sensors  815  of the own vehicle, and synthesizes three-dimensional data  831  with three-dimensional data  834  that is based on sensor information  833  detected by sensors  815  of the own vehicle, thereby generating three-dimensional data  835 . Three-dimensional data creation device  810  is thus capable of generating three-dimensional data of a range undetectable by sensors  815  of the own vehicle. 
     Three-dimensional data creation device  810  is also capable of transmitting, to the cloud-based traffic monitoring system or the following vehicle, etc., three-dimensional data of a space that includes the space ahead of the own vehicle undetectable by a sensor of the following vehicle, in response to the data transmission request from the cloud-based traffic monitoring system or the following vehicle. 
     The following describes the steps performed by three-dimensional data creation device  810  of transmitting three-dimensional data to a following vehicle.  FIG. 145  is a flowchart showing exemplary steps performed by three-dimensional data creation device  810  of transmitting three-dimensional data to a cloud-based traffic monitoring system or a following vehicle. 
     First, three-dimensional data creation device  810  generates and updates three-dimensional data  835  of a space that includes space on the road ahead of the own vehicle (S 801 ). More specifically, three-dimensional data creation device  810  synthesizes three-dimensional data  834  created on the basis of sensor information  833  of the own vehicle with three-dimensional data  831  created by the cloud-based traffic monitoring system or the preceding vehicle, etc., for example, thereby forming three-dimensional data  835  of a space that also includes the space ahead of the preceding vehicle undetectable by sensors  815  of the own vehicle. 
     Three-dimensional data creation device  810  then judges whether any change has occurred in three-dimensional data  835  of the space included in the space already transmitted (S 802 ). 
     When a change has occurred in three-dimensional data  835  of the space included in the space already transmitted due to, for example, a vehicle or a person entering such space from outside (Yes in S 802 ), three-dimensional data creation device  810  transmits, to the cloud-based traffic monitoring system or the following vehicle, the three-dimensional data that includes three-dimensional data  835  of the space in which the change has occurred (S 803 ). 
     Three-dimensional data creation device  810  may transmit three-dimensional data in which a change has occurred, at the same timing of transmitting three-dimensional data that is transmitted at a predetermined time interval, or may transmit three-dimensional data in which a change has occurred soon after the detection of such change. Stated differently, three-dimensional data creation device  810  may prioritize the transmission of three-dimensional data of the space in which a change has occurred to the transmission of three-dimensional data that is transmitted at a predetermined time interval. 
     Also, three-dimensional data creation device  810  may transmit, as three-dimensional data of a space in which a change has occurred, the whole three-dimensional data of the space in which such change has occurred, or may transmit only a difference in the three-dimensional data (e.g., information on three-dimensional points that have appeared or vanished, or information on the displacement of three-dimensional points). 
     Three-dimensional data creation device  810  may also transmit, to the following vehicle, meta-data on a risk avoidance behavior of the own vehicle such as hard breaking warning, before transmitting three-dimensional data of the space in which a change has occurred. This enables the following vehicle to recognize at an early stage that the preceding vehicle is to perform hard braking, etc., and thus to start performing a risk avoidance behavior at an early stage such as speed reduction. 
     When no change has occurred in three-dimensional data  835  of the space included in the space already transmitted (No in S 802 ), or after step S 803 , three-dimensional data creation device  810  transmits, to the cloud-based traffic monitoring system or the following vehicle, three-dimensional data of the space included in the space having a predetermined shape and located ahead of the own vehicle by distance L (S 804 ). 
     The processes of step S 801  through step S 804  are repeated, for example at a predetermined time interval. 
     When three-dimensional data  835  of the current space to be transmitted includes no difference from the three-dimensional map, three-dimensional data creation device  810  may not transmit three-dimensional data  837  of the space. 
     In the present embodiment, a client device transmits sensor information obtained through a sensor to a server or another client device. 
     A structure of a system according to the present embodiment will first be described.  FIG. 146  is a diagram showing the structure of a transmission/reception system of a three-dimensional map and sensor information according to the present embodiment. This system includes server  901 , and client devices  902 A and  902 B. Note that client devices  902 A and  902 B are also referred to as client device  902  when no particular distinction is made therebetween. 
     Client device  902  is, for example, a vehicle-mounted device equipped in a mobile object such as a vehicle. Server  901  is, for example, a cloud-based traffic monitoring system, and is capable of communicating with the plurality of client devices  902 . 
     Server  901  transmits the three-dimensional map formed by a point cloud to client device  902 . Note that a structure of the three-dimensional map is not limited to a point cloud, and may also be another structure expressing three-dimensional data such as a mesh structure. 
     Client device  902  transmits the sensor information obtained by client device  902  to server  901 . The sensor information includes, for example, at least one of information obtained by LiDAR, a visible light image, an infrared image, a depth image, sensor position information, or sensor speed information. 
     The data to be transmitted and received between server  901  and client device  902  may be compressed in order to reduce data volume, and may also be transmitted uncompressed in order to maintain data precision. When compressing the data, it is possible to use a three-dimensional compression method on the point cloud based on, for example, an octree structure. It is possible to use a two-dimensional image compression method on the visible light image, the infrared image, and the depth image. The two-dimensional image compression method is, for example, MPEG-4 AVC or HEVC standardized by MPEG. 
     Server  901  transmits the three-dimensional map managed by server  901  to client device  902  in response to a transmission request for the three-dimensional map from client device  902 . Note that server  901  may also transmit the three-dimensional map without waiting for the transmission request for the three-dimensional map from client device  902 . For example, server  901  may broadcast the three-dimensional map to at least one client device  902  located in a predetermined space. Server  901  may also transmit the three-dimensional map suited to a position of client device  902  at fixed time intervals to client device  902  that has received the transmission request once. Server  901  may also transmit the three-dimensional map managed by server  901  to client device  902  every time the three-dimensional map is updated. 
     Client device  902  sends the transmission request for the three-dimensional map to server  901 . For example, when client device  902  wants to perform the self-location estimation during traveling, client device  902  transmits the transmission request for the three-dimensional map to server  901 . 
     Note that in the following cases, client device  902  may send the transmission request for the three-dimensional map to server  901 . Client device  902  may send the transmission request for the three-dimensional map to server  901  when the three-dimensional map stored by client device  902  is old. For example, client device  902  may send the transmission request for the three-dimensional map to server  901  when a fixed period has passed since the three-dimensional map is obtained by client device  902 . 
     Client device  902  may also send the transmission request for the three-dimensional map to server  901  before a fixed time when client device  902  exits a space shown in the three-dimensional map stored by client device  902 . For example, client device  902  may send the transmission request for the three-dimensional map to server  901  when client device  902  is located within a predetermined distance from a boundary of the space shown in the three-dimensional map stored by client device  902 . When a movement path and a movement speed of client device  902  are understood, a time when client device  902  exits the space shown in the three-dimensional map stored by client device  902  may be predicted based on the movement path and the movement speed of client device  902 . 
     Client device  902  may also send the transmission request for the three-dimensional map to server  901  when an error during alignment of the three-dimensional data and the three-dimensional map created from the sensor information by client device  902  is at least at a fixed level. 
     Client device  902  transmits the sensor information to server  901  in response to a transmission request for the sensor information from server  901 . Note that client device  902  may transmit the sensor information to server  901  without waiting for the transmission request for the sensor information from server  901 . For example, client device  902  may periodically transmit the sensor information during a fixed period when client device  902  has received the transmission request for the sensor information from server  901  once. Client device  902  may determine that there is a possibility of a change in the three-dimensional map of a surrounding area of client device  902  having occurred, and transmit this information and the sensor information to server  901 , when the error during alignment of the three-dimensional data created by client device  902  based on the sensor information and the three-dimensional map obtained from server  901  is at least at the fixed level. 
     Server  901  sends a transmission request for the sensor information to client device  902 . For example, server  901  receives position information, such as GPS information, about client device  902  from client device  902 . Server  901  sends the transmission request for the sensor information to client device  902  in order to generate a new three-dimensional map, when it is determined that client device  902  is approaching a space in which the three-dimensional map managed by server  901  contains little information, based on the position information about client device  902 . Server  901  may also send the transmission request for the sensor information, when wanting to (i) update the three-dimensional map, (ii) check road conditions during snowfall, a disaster, or the like, or (iii) check traffic congestion conditions, accident/incident conditions, or the like. 
     Client device  902  may set an amount of data of the sensor information to be transmitted to server  901  in accordance with communication conditions or bandwidth during reception of the transmission request for the sensor information to be received from server  901 . Setting the amount of data of the sensor information to be transmitted to server  901  is, for example, increasing/reducing the data itself or appropriately selecting a compression method. 
       FIG. 147  is a block diagram showing an example structure of client device  902 . Client device  902  receives the three-dimensional map formed by a point cloud and the like from server  901 , and estimates a self-location of client device  902  using the three-dimensional map created based on the sensor information of client device  902 . Client device  902  transmits the obtained sensor information to server  901 . 
     Client device  902  includes data receiver  1011 , communication unit  1012 , reception controller  1013 , format converter  1014 , sensors  1015 , three-dimensional data creator  1016 , three-dimensional image processor  1017 , three-dimensional data storage  1018 , format converter  1019 , communication unit  1020 , transmission controller  1021 , and data transmitter  1022 . 
     Data receiver  1011  receives three-dimensional map  1031  from server  901 . Three-dimensional map  1031  is data that includes a point cloud such as a WLD or a SWLD. Three-dimensional map  1031  may include compressed data or uncompressed data. 
     Communication unit  1012  communicates with server  901  and transmits a data transmission request (e.g., transmission request for three-dimensional map) to server  901 . 
     Reception controller  1013  exchanges information, such as information on supported formats, with a communications partner via communication unit  1012  to establish communication with the communications partner. 
     Format converter  1014  performs a format conversion and the like on three-dimensional map  1031  received by data receiver  1011  to generate three-dimensional map  1032 . Format converter  1014  also performs a decompression or decoding process when three-dimensional map  1031  is compressed or encoded. Note that format converter  1014  does not perform the decompression or decoding process when three-dimensional map  1031  is uncompressed data. 
     Sensors  1015  are a group of sensors, such as LiDARs, visible light cameras, infrared cameras, or depth sensors that obtain information about the outside of a vehicle equipped with client device  902 , and generate sensor information  1033 . Sensor information  1033  is, for example, three-dimensional data such as a point cloud (point group data) when sensors  1015  are laser sensors such as LiDARs. Note that a single sensor may serve as sensors  1015 . 
     Three-dimensional data creator  1016  generates three-dimensional data  1034  of a surrounding area of the own vehicle based on sensor information  1033 . For example, three-dimensional data creator  1016  generates point cloud data with color information on the surrounding area of the own vehicle using information obtained by LiDAR and visible light video obtained by a visible light camera. 
     Three-dimensional image processor  1017  performs a self-location estimation process and the like of the own vehicle, using (i) the received three-dimensional map  1032  such as a point cloud, and (ii) three-dimensional data  1034  of the surrounding area of the own vehicle generated using sensor information  1033 . Note that three-dimensional image processor  1017  may generate three-dimensional data  1035  about the surroundings of the own vehicle by merging three-dimensional map  1032  and three-dimensional data  1034 , and may perform the self-location estimation process using the created three-dimensional data  1035 . 
     Three-dimensional data storage  1018  stores three-dimensional map  1032 , three-dimensional data  1034 , three-dimensional data  1035 , and the like. 
     Format converter  1019  generates sensor information  1037  by converting sensor information  1033  to a format supported by a receiver end. Note that format converter  1019  may reduce the amount of data by compressing or encoding sensor information  1037 . Format converter  1019  may omit this process when format conversion is not necessary. Format converter  1019  may also control the amount of data to be transmitted in accordance with a specified transmission range. 
     Communication unit  1020  communicates with server  901  and receives a data transmission request (transmission request for sensor information) and the like from server  901 . 
     Transmission controller  1021  exchanges information, such as information on supported formats, with a communications partner via communication unit  1020  to establish communication with the communications partner. 
     Data transmitter  1022  transmits sensor information  1037  to server  901 . Sensor information  1037  includes, for example, information obtained through sensors  1015 , such as information obtained by LiDAR, a luminance image obtained by a visible light camera, an infrared image obtained by an infrared camera, a depth image obtained by a depth sensor, sensor position information, and sensor speed information. 
     A structure of server  901  will be described next.  FIG. 148  is a block diagram showing an example structure of server  901 . Server  901  transmits sensor information from client device  902  and creates three-dimensional data based on the received sensor information. Server  901  updates the three-dimensional map managed by server  901  using the created three-dimensional data. Server  901  transmits the updated three-dimensional map to client device  902  in response to a transmission request for the three-dimensional map from client device  902 . 
     Server  901  includes data receiver  1111 , communication unit  1112 , reception controller  1113 , format converter  1114 , three-dimensional data creator  1116 , three-dimensional data merger  1117 , three-dimensional data storage  1118 , format converter  1119 , communication unit  1120 , transmission controller  1121 , and data transmitter  1122 . 
     Data receiver  1111  receives sensor information  1037  from client device  902 . Sensor information  1037  includes, for example, information obtained by LiDAR, a luminance image obtained by a visible light camera, an infrared image obtained by an infrared camera, a depth image obtained by a depth sensor, sensor position information, sensor speed information, and the like. 
     Communication unit  1112  communicates with client device  902  and transmits a data transmission request (e.g., transmission request for sensor information) and the like to client device  902 . 
     Reception controller  1113  exchanges information, such as information on supported formats, with a communications partner via communication unit  1112  to establish communication with the communications partner. 
     Format converter  1114  generates sensor information  1132  by performing a decompression or decoding process when received sensor information  1037  is compressed or encoded. Note that format converter  1114  does not perform the decompression or decoding process when sensor information  1037  is uncompressed data. 
     Three-dimensional data creator  1116  generates three-dimensional data  1134  of a surrounding area of client device  902  based on sensor information  1132 . For example, three-dimensional data creator  1116  generates point cloud data with color information on the surrounding area of client device  902  using information obtained by LiDAR and visible light video obtained by a visible light camera. 
     Three-dimensional data merger  1117  updates three-dimensional map  1135  by merging three-dimensional data  1134  created based on sensor information  1132  with three-dimensional map  1135  managed by server  901 . 
     Three-dimensional data storage  1118  stores three-dimensional map  1135  and the like. 
     Format converter  1119  generates three-dimensional map  1031  by converting three-dimensional map  1135  to a format supported by the receiver end. Note that format converter  1119  may reduce the amount of data by compressing or encoding three-dimensional map  1135 . Format converter  1119  may omit this process when format conversion is not necessary. Format converter  1119  may also control the amount of data to be transmitted in accordance with a specified transmission range. 
     Communication unit  1120  communicates with client device  902  and receives a data transmission request (transmission request for three-dimensional map) and the like from client device  902 . 
     Transmission controller  1121  exchanges information, such as information on supported formats, with a communications partner via communication unit  1120  to establish communication with the communications partner. 
     Data transmitter  1122  transmits three-dimensional map  1031  to client device  902 . Three-dimensional map  1031  is data that includes a point cloud such as a WLD or a SWLD. Three-dimensional map  1031  may include one of compressed data and uncompressed data. 
     An operational flow of client device  902  will be described next.  FIG. 149  is a flowchart of an operation when client device  902  obtains the three-dimensional map. 
     Client device  902  first requests server  901  to transmit the three-dimensional map (point cloud, etc.) (S 1001 ). At this point, by also transmitting the position information about client device  902  obtained through GPS and the like, client device  902  may also request server  901  to transmit a three-dimensional map relating to this position information. 
     Client device  902  next receives the three-dimensional map from server  901  (S 1002 ). When the received three-dimensional map is compressed data, client device  902  decodes the received three-dimensional map and generates an uncompressed three-dimensional map (S 1003 ). 
     Client device  902  next creates three-dimensional data  1034  of the surrounding area of client device  902  using sensor information  1033  obtained by sensors  1015  (S 1004 ). Client device  902  next estimates the self-location of client device  902  using three-dimensional map  1032  received from server  901  and three-dimensional data  1034  created using sensor information  1033  (S 1005 ). 
       FIG. 150  is a flowchart of an operation when client device  902  transmits the sensor information. Client device  902  first receives a transmission request for the sensor information from server  901  (S 1011 ). Client device  902  that has received the transmission request transmits sensor information  1037  to server  901  (S 1012 ). Note that client device  902  may generate sensor information  1037  by compressing each piece of information using a compression method suited to each piece of information, when sensor information  1033  includes a plurality of pieces of information obtained by sensors  1015 . 
     An operational flow of server  901  will be described next.  FIG. 151  is a flowchart of an operation when server  901  obtains the sensor information. Server  901  first requests client device  902  to transmit the sensor information (S 1021 ). Server  901  next receives sensor information  1037  transmitted from client device  902  in accordance with the request (S 1022 ). Server  901  next creates three-dimensional data  1134  using the received sensor information  1037  (S 1023 ). Server  901  next reflects the created three-dimensional data  1134  in three-dimensional map  1135  (S 1024 ). 
       FIG. 152  is a flowchart of an operation when server  901  transmits the three-dimensional map. Server  901  first receives a transmission request for the three-dimensional map from client device  902  (S 1031 ). Server  901  that has received the transmission request for the three-dimensional map transmits the three-dimensional map to client device  902  (S 1032 ). At this point, server  901  may extract a three-dimensional map of a vicinity of client device  902  along with the position information about client device  902 , and transmit the extracted three-dimensional map. Server  901  may compress the three-dimensional map formed by a point cloud using, for example, an octree structure compression method, and transmit the compressed three-dimensional map. 
     The following describes variations of the present embodiment. 
     Server  901  creates three-dimensional data  1134  of a vicinity of a position of client device  902  using sensor information  1037  received from client device  902 . Server  901  next calculates a difference between three-dimensional data  1134  and three-dimensional map  1135 , by matching the created three-dimensional data  1134  with three-dimensional map  1135  of the same area managed by server  901 . Server  901  determines that a type of anomaly has occurred in the surrounding area of client device  902 , when the difference is greater than or equal to a predetermined threshold. For example, it is conceivable that a large difference occurs between three-dimensional map  1135  managed by server  901  and three-dimensional data  1134  created based on sensor information  1037 , when land subsidence and the like occurs due to a natural disaster such as an earthquake. 
     Sensor information  1037  may include information indicating at least one of a sensor type, a sensor performance, and a sensor model number. Sensor information  1037  may also be appended with a class ID and the like in accordance with the sensor performance. For example, when sensor information  1037  is obtained by LiDAR, it is conceivable to assign identifiers to the sensor performance. A sensor capable of obtaining information with precision in units of several millimeters is class  1 , a sensor capable of obtaining information with precision in units of several centimeters is class  2 , and a sensor capable of obtaining information with precision in units of several meters is class  3 . Server  901  may estimate sensor performance information and the like from a model number of client device  902 . For example, when client device  902  is equipped in a vehicle, server  901  may determine sensor specification information from a type of the vehicle. In this case, server  901  may obtain information on the type of the vehicle in advance, and the information may also be included in the sensor information. Server  901  may change a degree of correction with respect to three-dimensional data  1134  created using sensor information  1037 , using obtained sensor information  1037 . For example, when the sensor performance is high in precision (class  1 ), server  901  does not correct three-dimensional data  1134 . When the sensor performance is low in precision (class  3 ), server  901  corrects three-dimensional data  1134  in accordance with the precision of the sensor. For example, server  901  increases the degree (intensity) of correction with a decrease in the precision of the sensor. 
     Server  901  may simultaneously send the transmission request for the sensor information to the plurality of client devices  902  in a certain space. Server  901  does not need to use all of the sensor information for creating three-dimensional data  1134  and may, for example, select sensor information to be used in accordance with the sensor performance, when having received a plurality of pieces of sensor information from the plurality of client devices  902 . For example, when updating three-dimensional map  1135 , server  901  may select high-precision sensor information (class  1 ) from among the received plurality of pieces of sensor information, and create three-dimensional data  1134  using the selected sensor information. 
     Server  901  is not limited to only being a server such as a cloud-based traffic monitoring system, and may also be another (vehicle-mounted) client device.  FIG. 153  is a diagram of a system structure in this case. 
     For example, client device  902 C sends a transmission request for sensor information to client device  902 A located nearby, and obtains the sensor information from client device  902 A. Client device  902 C then creates three-dimensional data using the obtained sensor information of client device  902 A, and updates a three-dimensional map of client device  902 C. This enables client device  902 C to generate a three-dimensional map of a space that can be obtained from client device  902 A, and fully utilize the performance of client device  902 C. For example, such a case is conceivable when client device  902 C has high performance. 
     In this case, client device  902 A that has provided the sensor information is given rights to obtain the high-precision three-dimensional map generated by client device  902 C. Client device  902 A receives the high-precision three-dimensional map from client device  902 C in accordance with these rights. 
     Server  901  may send the transmission request for the sensor information to the plurality of client devices  902  (client device  902 A and client device  902 B) located nearby client device  902 C. When a sensor of client device  902 A or client device  902 B has high performance, client device  902 C is capable of creating the three-dimensional data using the sensor information obtained by this high-performance sensor. 
       FIG. 154  is a block diagram showing a functionality structure of server  901  and client device  902 . Server  901  includes, for example, three-dimensional map compression/decoding processor  1201  that compresses and decodes the three-dimensional map and sensor information compression/decoding processor  1202  that compresses and decodes the sensor information. 
     Client device  902  includes three-dimensional map decoding processor  1211  and sensor information compression processor  1212 . Three-dimensional map decoding processor  1211  receives encoded data of the compressed three-dimensional map, decodes the encoded data, and obtains the three-dimensional map. Sensor information compression processor  1212  compresses the sensor information itself instead of the three-dimensional data created using the obtained sensor information, and transmits the encoded data of the compressed sensor information to server  901 . With this structure, client device  902  does not need to internally store a processor that performs a process for compressing the three-dimensional data of the three-dimensional map (point cloud, etc.), as long as client device  902  internally stores a processor that performs a process for decoding the three-dimensional map (point cloud, etc.). This makes it possible to limit costs, power consumption, and the like of client device  902 . 
     As stated above, client device  902  according to the present embodiment is equipped in the mobile object, and creates three-dimensional data  1034  of a surrounding area of the mobile object using sensor information  1033  that is obtained through sensor  1015  equipped in the mobile object and indicates a surrounding condition of the mobile object. Client device  902  estimates a self-location of the mobile object using the created three-dimensional data  1034 . Client device  902  transmits obtained sensor information  1033  to server  901  or another client device  902 . 
     This enables client device  902  to transmit sensor information  1033  to server  901  or the like. This makes it possible to further reduce the amount of transmission data compared to when transmitting the three-dimensional data. Since there is no need for client device  902  to perform processes such as compressing or encoding the three-dimensional data, it is possible to reduce the processing amount of client device  902 . As such, client device  902  is capable of reducing the amount of data to be transmitted or simplifying the structure of the device. 
     Client device  902  further transmits the transmission request for the three-dimensional map to server  901  and receives three-dimensional map  1031  from server  901 . In the estimating of the self-location, client device  902  estimates the self-location using three-dimensional data  1034  and three-dimensional map  1032 . 
     Sensor information  1033  includes at least one of information obtained by a laser sensor, a luminance image, an infrared image, a depth image, sensor position information, or sensor speed information. 
     Sensor information  1033  includes information that indicates a performance of the sensor. 
     Client device  902  encodes or compresses sensor information  1033 , and in the transmitting of the sensor information, transmits sensor information  1037  that has been encoded or compressed to server  901  or another client device  902 . This enables client device  902  to reduce the amount of data to be transmitted. 
     For example, client device  902  includes a processor and memory. The processor performs the above processes using the memory. 
     Server  901  according to the present embodiment is capable of communicating with client device  902  equipped in the mobile object, and receives sensor information  1037  that is obtained through sensor  1015  equipped in the mobile object and indicates a surrounding condition of the mobile object. Server  901  creates three-dimensional data  1134  of a surrounding area of the mobile object using received sensor information  1037 . 
     With this, server  901  creates three-dimensional data  1134  using sensor information  1037  transmitted from client device  902 . This makes it possible to further reduce the amount of transmission data compared to when client device  902  transmits the three-dimensional data. Since there is no need for client device  902  to perform processes such as compressing or encoding the three-dimensional data, it is possible to reduce the processing amount of client device  902 . As such, server  901  is capable of reducing the amount of data to be transmitted or simplifying the structure of the device. 
     Server  901  further transmits a transmission request for the sensor information to client device  902 . 
     Server  901  further updates three-dimensional map  1135  using the created three-dimensional data  1134 , and transmits three-dimensional map  1135  to client device  902  in response to the transmission request for three-dimensional map  1135  from client device  902 . 
     Sensor information  1037  includes at least one of information obtained by a laser sensor, a luminance image, an infrared image, a depth image, sensor position information, or sensor speed information. 
     Sensor information  1037  includes information that indicates a performance of the sensor. 
     Server  901  further corrects the three-dimensional data in accordance with the performance of the sensor. This enables the three-dimensional data creation method to improve the quality of the three-dimensional data. 
     In the receiving of the sensor information, server  901  receives a plurality of pieces of sensor information  1037  received from a plurality of client devices  902 , and selects sensor information  1037  to be used in the creating of three-dimensional data  1134 , based on a plurality of pieces of information that each indicates the performance of the sensor included in the plurality of pieces of sensor information  1037 . This enables server  901  to improve the quality of three-dimensional data  1134 . 
     Server  901  decodes or decompresses received sensor information  1037 , and creates three-dimensional data  1134  using sensor information  1132  that has been decoded or decompressed. This enables server  901  to reduce the amount of data to be transmitted. 
     For example, server  901  includes a processor and memory. The processor performs the above processes using the memory. 
     The following will describe a variation of the present embodiment.  FIG. 155  is a diagram illustrating a configuration of a system according to the present embodiment. The system illustrated in  FIG. 155  includes server  2001 , client device  2002 A, and client device  2002 B. 
     Client device  2002 A and client device  2002 B are each provided in a mobile object such as a vehicle, and transmit sensor information to server  2001 . Server  2001  transmits a three-dimensional map (a point cloud) to client device  2002 A and client device  2002 B. 
     Client device  2002 A includes sensor information obtainer  2011 , storage  2012 , and data transmission possibility determiner  2013 . It should be noted that client device  2002 B has the same configuration. Additionally, when client device  2002 A and client device  2002 B are not particularly distinguished below, client device  2002 A and client device  2002 B are also referred to as client device  2002 . 
       FIG. 156  is a flowchart illustrating operation of client device  2002  according to the present embodiment. 
     Sensor information obtainer  2011  obtains a variety of sensor information using sensors (a group of sensors) provided in a mobile object. In other words, sensor information obtainer  2011  obtains sensor information obtained by the sensors (the group of sensors) provided in the mobile object and indicating a surrounding state of the mobile object. Sensor information obtainer  2011  also stores the obtained sensor information into storage  2012 . This sensor information includes at least one of information obtained by LiDAR, a visible light image, an infrared image, or a depth image. Additionally, the sensor information may include at least one of sensor position information, speed information, obtainment time information, or obtainment location information. Sensor position information indicates a position of a sensor that has obtained sensor information. Speed information indicates a speed of the mobile object when a sensor obtained sensor information. Obtainment time information indicates a time when a sensor obtained sensor information. Obtainment location information indicates a position of the mobile object or a sensor when the sensor obtained sensor information. 
     Next, data transmission possibility determiner  2013  determines whether the mobile object (client device  2002 ) is in an environment in which the mobile object can transmit sensor information to server  2001  (S 2002 ). For example, data transmission possibility determiner  2013  may specify a location and a time at which client device  2002  is present using GPS information etc., and may determine whether data can be transmitted. Additionally, data transmission possibility determiner  2013  may determine whether data can be transmitted, depending on whether it is possible to connect to a specific access point. 
     When client device  2002  determines that the mobile object is in the environment in which the mobile object can transmit the sensor information to server  2001  (YES in S 2002 ), client device  2002  transmits the sensor information to server  2001  (S 2003 ). In other words, when client device  2002  becomes capable of transmitting sensor information to server  2001 , client device  2002  transmits the sensor information held by client device  2002  to server  2001 . For example, an access point that enables high-speed communication using millimeter waves is provided in an intersection or the like. When client device  2002  enters the intersection, client device  2002  transmits the sensor information held by client device  2002  to server  2001  at high speed using the millimeter-wave communication. 
     Next, client device  2002  deletes from storage  2012  the sensor information that has been transmitted to server  2001  (S 2004 ). It should be noted that when sensor information that has not been transmitted to server  2001  meets predetermined conditions, client device  2002  may delete the sensor information. For example, when an obtainment time of sensor information held by client device  2002  precedes a current time by a certain time, client device  2002  may delete the sensor information from storage  2012 . In other words, when a difference between the current time and a time when a sensor obtained sensor information exceeds a predetermined time, client device  2002  may delete the sensor information from storage  2012 . Besides, when an obtainment location of sensor information held by client device  2002  is separated from a current location by a certain distance, client device  2002  may delete the sensor information from storage  2012 . In other words, when a difference between a current position of the mobile object or a sensor and a position of the mobile object or the sensor when the sensor obtained sensor information exceeds a predetermined distance, client device  2002  may delete the sensor information from storage  2012 . Accordingly, it is possible to reduce the capacity of storage  2012  of client device  2002 . 
     When client device  2002  does not finish obtaining sensor information (NO in S 2005 ), client device  2002  performs step S 2001  and the subsequent steps again. Further, when client device  2002  finishes obtaining sensor information (YES in S 2005 ), client device  2002  completes the process. 
     Client device  2002  may select sensor information to be transmitted to server  2001 , in accordance with communication conditions. For example, when high-speed communication is available, client device  2002  preferentially transmits sensor information (e.g., information obtained by LiDAR) of which the data size held in storage  2012  is large. Additionally, when high-speed communication is not readily available, client device  2002  transmits sensor information (e.g., a visible light image) which has high priority and of which the data size held in storage  2012  is small. Accordingly, client device  2002  can efficiently transmit sensor information held in storage  2012 , in accordance with network conditions 
     Client device  2002  may obtain, from server  2001 , time information indicating a current time and location information indicating a current location. Moreover, client device  2002  may determine an obtainment time and an obtainment location of sensor information based on the obtained time information and location information. In other words, client device  2002  may obtain time information from server  2001  and generate obtainment time information using the obtained time information. Client device  2002  may also obtain location information from server  2001  and generate obtainment location information using the obtained location information. 
     For example, regarding time information, server  2001  and client device  2002  perform clock synchronization using a means such as the Network Time Protocol (NTP) or the Precision Time Protocol (PTP). This enables client device  2002  to obtain accurate time information. What&#39;s more, since it is possible to synchronize clocks between server  2001  and client devices  2002 , it is possible to synchronize times included in pieces of sensor information obtained by separate client devices  2002 . As a result, server  2001  can handle sensor information indicating a synchronized time. It should be noted that a means of synchronizing clocks may be any means other than the NTP or PTP. In addition, GPS information may be used as the time information and the location information. 
     Server  2001  may specify a time or a location and obtain pieces of sensor information from client devices  2002 . For example, when an accident occurs, in order to search for a client device in the vicinity of the accident, server  2001  specifies an accident occurrence time and an accident occurrence location and broadcasts sensor information transmission requests to client devices  2002 . Then, client device  2002  having sensor information obtained at the corresponding time and location transmits the sensor information to server  2001 . In other words, client device  2002  receives, from server  2001 , a sensor information transmission request including specification information specifying a location and a time. When sensor information obtained at a location and a time indicated by the specification information is stored in storage  2012 , and client device  2002  determines that the mobile object is present in the environment in which the mobile object can transmit the sensor information to server  2001 , client device  2002  transmits, to server  2001 , the sensor information obtained at the location and the time indicated by the specification information. Consequently, server  2001  can obtain the pieces of sensor information pertaining to the occurrence of the accident from client devices  2002 , and use the pieces of sensor information for accident analysis etc. 
     It should be noted that when client device  2002  receives a sensor information transmission request from server  2001 , client device  2002  may refuse to transmit sensor information. Additionally, client device  2002  may set in advance which pieces of sensor information can be transmitted. Alternatively, server  2001  may inquire of client device  2002  each time whether sensor information can be transmitted. 
     A point may be given to client device  2002  that has transmitted sensor information to server  2001 . This point can be used in payment for, for example, gasoline expenses, electric vehicle (EV) charging expenses, a highway toll, or rental car expenses. After obtaining sensor information, server  2001  may delete information for specifying client device  2002  that has transmitted the sensor information. For example, this information is a network address of client device  2002 . Since this enables the anonymization of sensor information, a user of client device  2002  can securely transmit sensor information from client device  2002  to server  2001 . Server  2001  may include servers. For example, by servers sharing sensor information, even when one of the servers breaks down, the other servers can communicate with client device  2002 . Accordingly, it is possible to avoid service outage due to a server breakdown. 
     A specified location specified by a sensor information transmission request indicates an accident occurrence location etc., and may be different from a position of client device  2002  at a specified time specified by the sensor information transmission request. For this reason, for example, by specifying, as a specified location, a range such as within XX meters of a surrounding area, server  2001  can request information from client device  2002  within the range. Similarly, server  2001  may also specify, as a specified time, a range such as within N seconds before and after a certain time. As a result, server  2001  can obtain sensor information from client device  2002  present for a time from t-N to t+N and in a location within XX meters from absolute position S. When client device  2002  transmits three-dimensional data such as LiDAR, client device  2002  may transmit data created immediately after time t. 
     Server  2001  may separately specify information indicating, as a specified location, a location of client device  2002  from which sensor information is to be obtained, and a location at which sensor information is desirably obtained. For example, server  2001  specifies that sensor information including at least a range within YY meters from absolute position S is to be obtained from client device  2002  present within XX meters from absolute position S. When client device  2002  selects three-dimensional data to be transmitted, client device  2002  selects one or more pieces of three-dimensional data so that the one or more pieces of three-dimensional data include at least the sensor information including the specified range. Each of the one or more pieces of three-dimensional data is a random-accessible unit of data. In addition, when client device  2002  transmits a visible light image, client device  2002  may transmit pieces of temporally continuous image data including at least a frame immediately before or immediately after time t. 
     When client device  2002  can use physical networks such as 5G, Wi-Fi, or modes in 5G for transmitting sensor information, client device  2002  may select a network to be used according to the order of priority notified by server  2001 . Alternatively, client device  2002  may select a network that enables client device  2002  to ensure an appropriate bandwidth based on the size of transmit data. Alternatively, client device  2002  may select a network to be used, based on data transmission expenses etc. A transmission request from server  2001  may include information indicating a transmission deadline, for example, performing transmission when client device  2002  can start transmission by time t. When server  2001  cannot obtain sufficient sensor information within a time limit, server  2001  may issue a transmission request again. 
     Sensor information may include header information indicating characteristics of sensor data along with compressed or uncompressed sensor data. Client device  2002  may transmit header information to server  2001  via a physical network or a communication protocol that is different from a physical network or a communication protocol used for sensor data. For example, client device  2002  transmits header information to server  2001  prior to transmitting sensor data. Server  2001  determines whether to obtain the sensor data of client device  2002 , based on a result of analysis of the header information. For example, header information may include information indicating a point cloud obtainment density, an elevation angle, or a frame rate of LiDAR, or information indicating, for example, a resolution, an SN ratio, or a frame rate of a visible light image. Accordingly, server  2001  can obtain the sensor information from client device  2002  having the sensor data of determined quality. 
     As stated above, client device  2002  is provided in the mobile object, obtains sensor information that has been obtained by a sensor provided in the mobile object and indicates a surrounding state of the mobile object, and stores the sensor information into storage  2012 . Client device  2002  determines whether the mobile object is present in an environment in which the mobile object is capable of transmitting the sensor information to server  2001 , and transmits the sensor information to server  2001  when the mobile object is determined to be present in the environment in which the mobile object is capable of transmitting the sensor information to server  2001 . 
     Additionally, client device  2002  further creates, from the sensor information, three-dimensional data of a surrounding area of the mobile object, and estimates a self-location of the mobile object using the three-dimensional data created. 
     Besides, client device  2002  further transmits a transmission request for a three-dimensional map to server  2001 , and receives the three-dimensional map from server  2001 . In the estimating, client device  2002  estimates the self-location using the three-dimensional data and the three-dimensional map. 
     It should be noted that the above process performed by client device  2002  may be realized as an information transmission method for use in client device  2002 . 
     In addition, client device  2002  may include a processor and memory. Using the memory, the processor may perform the above process. 
     Next, a sensor information collection system according to the present embodiment will be described.  FIG. 157  is a diagram illustrating a configuration of the sensor information collection system according to the present embodiment. As illustrated in  FIG. 157 , the sensor information collection system according to the present embodiment includes terminal  2021 A, terminal  2021 B, communication device  2022 A, communication device  2022 B, network  2023 , data collection server  2024 , map server  2025 , and client device  2026 . It should be noted that when terminal  2021 A and terminal  2021 B are not particularly distinguished, terminal  2021 A and terminal  2021 B are also referred to as terminal  2021 . Additionally, when communication device  2022 A and communication device  2022 B are not particularly distinguished, communication device  2022 A and communication device  2022 B are also referred to as communication device  2022 . 
     Data collection server  2024  collects data such as sensor data obtained by a sensor included in terminal  2021  as position-related data in which the data is associated with a position in a three-dimensional space. 
     Sensor data is data obtained by, for example, detecting a surrounding state of terminal  2021  or an internal state of terminal  2021  using a sensor included in terminal  2021 . Terminal  2021  transmits, to data collection server  2024 , one or more pieces of sensor data collected from one or more sensor devices in locations at which direct communication with terminal  2021  is possible or at which communication with terminal  2021  is possible by the same communication system or via one or more relay devices. 
     Data included in position-related data may include, for example, information indicating an operating state, an operating log, a service use state, etc. of a terminal or a device included in the terminal. In addition, the data include in the position-related data may include, for example, information in which an identifier of terminal  2021  is associated with a position or a movement path etc. of terminal  2021 . 
     Information indicating a position included in position-related data is associated with, for example, information indicating a position in three-dimensional data such as three-dimensional map data. The details of information indicating a position will be described later. 
     Position-related data may include at least one of the above-described time information or information indicating an attribute of data included in the position-related data or a type (e.g., a model number) of a sensor that has created the data, in addition to position information that is information indicating a position. The position information and the time information may be stored in a header area of the position-related data or a header area of a frame that stores the position-related data. Further, the position information and the time information may be transmitted and/or stored as metadata associated with the position-related data, separately from the position-related data. 
     Map server  2025  is connected to, for example, network  2023 , and transmits three-dimensional data such as three-dimensional map data in response to a request from another device such as terminal  2021 . Besides, as described in the aforementioned embodiments, map server  2025  may have, for example, a function of updating three-dimensional data using sensor information transmitted from terminal  2021 . 
     Data collection server  2024  is connected to, for example, network  2023 , collects position-related data from another device such as terminal  2021 , and stores the collected position-related data into a storage of data collection server  2024  or a storage of another server. In addition, data collection server  2024  transmits, for example, metadata of collected position-related data or three-dimensional data generated based on the position-related data, to terminal  2021  in response to a request from terminal  2021 . 
     Network  2023  is, for example, a communication network such as the Internet. Terminal  2021  is connected to network  2023  via communication device  2022 . Communication device  2022  communicates with terminal  2021  using one communication system or switching between communication systems. Communication device  2022  is a communication satellite that performs communication using, for example, (1) a base station compliant with Long-Term Evolution (LTE) etc., (2) an access point (AP) for Wi-Fi or millimeter-wave communication etc., (3) a low-power wide-area (LPWA) network gateway such as SIGFOX, LoRaWAN, or Wi-SUN, or (4) a satellite communication system such as DVB-S2. 
     It should be noted that a base station may communicate with terminal  2021  using a system classified as an LPWA network such as Narrowband Internet of Things (NB IoT) or LTE-M, or switching between these systems. 
     Here, although, in the example given, terminal  2021  has a function of communicating with communication device  2022  that uses two types of communication systems, and communicates with map server  2025  or data collection server  2024  using one of the communication systems or switching between the communication systems and between communication devices  2022  to be a direct communication partner; a configuration of the sensor information collection system and terminal  2021  is not limited to this. For example, terminal  2021  need not have a function of performing communication using communication systems, and may have a function of performing communication using one of the communication systems. Terminal  2021  may also support three or more communication systems. Additionally, each terminal  2021  may support a different communication system. 
     Terminal  2021  includes, for example, the configuration of client device  902  illustrated in  FIG. 147 . Terminal  2021  estimates a self-location etc. using received three-dimensional data. Besides, terminal  2021  associates sensor data obtained from a sensor and position information obtained by self-location estimation to generate position-related data. 
     Position information appended to position-related data indicates, for example, a position in a coordinate system used for three-dimensional data. For example, the position information is coordinate values represented using a value of a latitude and a value of a longitude. Here, terminal  2021  may include, in the position information, a coordinate system serving as a reference for the coordinate values and information indicating three-dimensional data used for location estimation, along with the coordinate values. Coordinate values may also include altitude information. 
     The position information may be associated with a data unit or a space unit usable for encoding the above three-dimensional data. Such a unit is, for example, WLD, GOS, SPC, VLM, or VXL. Here, the position information is represented by, for example, an identifier for identifying a data unit such as the SPC corresponding to position-related data. It should be noted that the position information may include, for example, information indicating three-dimensional data obtained by encoding a three-dimensional space including a data unit such as the SPC or information indicating a detailed position within the SPC, in addition to the identifier for identifying the data unit such as the SPC. The information indicating the three-dimensional data is, for example, a file name of the three-dimensional data. 
     As stated above, by generating position-related data associated with position information based on location estimation using three-dimensional data, the system can give more accurate position information to sensor information than when the system appends position information based on a self-location of a client device (terminal  2021 ) obtained using a GPS to sensor information. As a result, even when another device uses the position-related data in another service, there is a possibility of more accurately determining a position corresponding to the position-related data in an actual space, by performing location estimation based on the same three-dimensional data. 
     It should be noted that although the data transmitted from terminal  2021  is the position-related data in the example given in the present embodiment, the data transmitted from terminal  2021  may be data unassociated with position information. In other words, the transmission and reception of three-dimensional data or sensor data described in the other embodiments may be performed via network  2023  described in the present embodiment. 
     Next, a different example of position information indicating a position in a three-dimensional or two-dimensional actual space or in a map space will be described. The position information appended to position-related data may be information indicating a relative position relative to a keypoint in three-dimensional data. Here, the keypoint serving as a reference for the position information is encoded as, for example, SWLD, and notified to terminal  2021  as three-dimensional data. 
     The information indicating the relative position relative to the keypoint may be, for example, information that is represented by a vector from the keypoint to the point indicated by the position information, and indicates a direction and a distance from the keypoint to the point indicated by the position information. Alternatively, the information indicating the relative position relative to the keypoint may be information indicating an amount of displacement from the keypoint to the point indicated by the position information along each of the x axis, the y axis, and the z axis. Additionally, the information indicating the relative position relative to the keypoint may be information indicating a distance from each of three or more keypoints to the point indicated by the position information. It should be noted that the relative position need not be a relative position of the point indicated by the position information represented using each keypoint as a reference, and may be a relative position of each keypoint represented with respect to the point indicated by the position information. Examples of position information based  1 ′ 74  on a relative position relative to a keypoint include information for identifying a keypoint to be a reference, and information indicating the relative position of the point indicated by the position information and relative to the keypoint. When the information indicating the relative position relative to the keypoint is provided separately from three-dimensional data, the information indicating the relative position relative to the keypoint may include, for example, coordinate axes used in deriving the relative position, information indicating a type of the three-dimensional data, and/or information indicating a magnitude per unit amount (e.g., a scale) of a value of the information indicating the relative position. 
     The position information may include, for each keypoint, information indicating a relative position relative to the keypoint. When the position information is represented by relative positions relative to keypoints, terminal  2021  that intends to identify a position in an actual space indicated by the position information may calculate candidate points of the position indicated by the position information from positions of the keypoints each estimated from sensor data, and may determine that a point obtained by averaging the calculated candidate points is the point indicated by the position information. Since this configuration reduces the effects of errors when the positions of the keypoints are estimated from the sensor data, it is possible to improve the estimation accuracy for the point in the actual space indicated by the position information. Besides, when the position information includes information indicating relative positions relative to keypoints, if it is possible to detect any one of the keypoints regardless of the presence of keypoints undetectable due to a limitation such as a type or performance of a sensor included in terminal  2021 , it is possible to estimate a value of the point indicated by the position information. 
     A point identifiable from sensor data can be used as a keypoint. Examples of the point identifiable from the sensor data include a point or a point within a region that satisfies a predetermined keypoint detection condition, such as the above-described three-dimensional feature or feature of visible light data is greater than or equal to a threshold value. 
     Moreover, a marker etc. placed in an actual space may be used as a keypoint. In this case, the maker may be detected and located from data obtained using a sensor such as LiDAR or a camera. For example, the marker may be represented by a change in color or luminance value (degree of reflection), or a three-dimensional shape (e.g., unevenness). Coordinate values indicating a position of the marker, or a two-dimensional bar code or a bar code etc. generated from an identifier of the marker may be also used. 
     Furthermore, a light source that transmits an optical signal may be used as a marker. When a light source of an optical signal is used as a marker, not only information for obtaining a position such as coordinate values or an identifier but also other data may be transmitted using an optical signal. For example, an optical signal may include contents of service corresponding to the position of the marker, an address for obtaining contents such as a URL, or an identifier of a wireless communication device for receiving service, and information indicating a wireless communication system etc. for connecting to the wireless communication device. The use of an optical communication device (a light source) as a marker not only facilitates the transmission of data other than information indicating a position but also makes it possible to dynamically change the data. 
     Terminal  2021  finds out a correspondence relationship of keypoints between mutually different data using, for example, a common identifier used for the data, or information or a table indicating the correspondence relationship of the keypoints between the data. When there is no information indicating a correspondence relationship between keytpoints, terminal  2021  may also determine that when coordinates of a keypoint in three-dimensional data are converted into a position in a space of another three-dimensional data, a keypoint closest to the position is a corresponding keypoint. 
     When the position information based on the relative position described above is used, terminal  2021  that uses mutually different three-dimensional data or services can identify or estimate a position indicated by the position information with respect to a common keypoint included in or associated with each three-dimensional data. As a result, terminal  2021  that uses the mutually different three-dimensional data or the services can identify or estimate the same position with higher accuracy. 
     Even when map data or three-dimensional data represented using mutually different coordinate systems are used, since it is possible to reduce the effects of errors caused by the conversion of a coordinate system, it is possible to coordinate services based on more accurate position information. 
     Hereinafter, an example of functions provided by data collection server  2024  will be described. Data collection server  2024  may transfer received position-related data to another data server. When there are data servers, data collection server  2024  determines to which data server received position-related data is to be transferred, and transfers the position-related data to a data server determined as a transfer destination. 
     Data collection server  2024  determines a transfer destination based on, for example, transfer destination server determination rules preset to data collection server  2024 . The transfer destination server determination rules are set by, for example, a transfer destination table in which identifiers respectively associated with terminals  2021  are associated with transfer destination data servers. 
     Terminal  2021  appends an identifier associated with terminal  2021  to position-related data to be transmitted, and transmits the position-related data to data collection server  2024 . Data collection server  2024  determines a transfer destination data server corresponding to the identifier appended to the position-related data, based on the transfer destination server determination rules set out using the transfer destination table etc.; and transmits the position-related data to the determined data server. The transfer destination server determination rules may be specified based on a determination condition set using a time, a place, etc. at which position-related data is obtained. Here, examples of the identifier associated with transmission source terminal  2021  include an identifier unique to each terminal  2021  or an identifier indicating a group to which terminal  2021  belongs. 
     The transfer destination table need not be a table in which identifiers associated with transmission source terminals are directly associated with transfer destination data servers. For example, data collection server  2024  holds a management table that stores tag information assigned to each identifier unique to terminal  2021 , and a transfer destination table in which the pieces of tag information are associated with transfer destination data servers. Data collection server  2024  may determine a transfer destination data server based on tag information, using the management table and the transfer destination table. Here, the tag information is, for example, control information for management or control information for providing service assigned to a type, a model number, an owner of terminal  2021  corresponding to the identifier, a group to which terminal  2021  belongs, or another identifier. Moreover, in the transfer destination able, identifiers unique to respective sensors may be used instead of the identifiers associated with transmission source terminals  2021 . Furthermore, the transfer destination server determination rules may be set by client device  2026 . 
     Data collection server  2024  may determine data servers as transfer destinations, and transfer received position-related data to the data servers. According to this configuration, for example, when position-related data is automatically backed up or when, in order that position-related data is commonly used by different services, there is a need to transmit the position-related data to a data server for providing each service, it is possible to achieve data transfer as intended by changing a setting of data collection server  2024 . As a result, it is possible to reduce the number of steps necessary for building and changing a system, compared to when a transmission destination of position-related data is set for each terminal  2021 . 
     Data collection server  2024  may register, as a new transfer destination, a data server specified by a transfer request signal received from a data server; and transmit position-related data subsequently received to the data server, in response to the transfer request signal. 
     Data collection server  2024  may store position-related data received from terminal  2021  into a recording device, and transmit position-related data specified by a transmission request signal received from terminal  2021  or a data server to request source terminal  2021  or the data server in response to the transmission request signal. 
     Data collection server  2024  may determine whether position-related data is suppliable to a request source data server or terminal  2021 , and transfer or transmit the position-related data to the request source data server or terminal  2021  when determining that the position-related data is suppliable. 
     When data collection server  2024  receives a request for current position-related data from client device  2026 , even if it is not a timing for transmitting position-related data by terminal  2021 , data collection server  2024  may send a transmission request for the current position-related data to terminal  2021 , and terminal  2021  may transmit the current position-related data in response to the transmission request. 
     Although terminal  2021  transmits position information data to data collection server  2024  in the above description, data collection server  2024  may have a function of managing terminal  2021  such as a function necessary for collecting position-related data from terminal  2021  or a function used when collecting position-related data from terminal  2021 . 
     Data collection server  2024  may have a function of transmitting, to terminal  2021 , a data request signal for requesting transmission of position information data, and collecting position-related data. 
     Management information such as an address for communicating with terminal  2021  from which data is to be collected or an identifier unique to terminal  2021  is registered in advance in data collection server  2024 . Data collection server  2024  collects position-related data from terminal  2021  based on the registered management information. Management information may include information such as types of sensors included in terminal  2021 , the number of sensors included in terminal  2021 , and communication systems supported by terminal  2021 . 
     Data collection server  2024  may collect information such as an operating state or a current position of terminal  2021  from terminal  2021 . 
     Registration of management information may be instructed by client device  2026 , or a process for the registration may be started by terminal  2021  transmitting a registration request to data collection server  2024 . Data collection server  2024  may have a function of controlling communication between data collection server  2024  and terminal  2021 . 
     Communication between data collection server  2024  and terminal  2021  may be established using a dedicated line provided by a service provider such as a mobile network operator (MNO) or a mobile virtual network operator (MVNO), or a virtual dedicated line based on a virtual private network (VPN). According to this configuration, it is possible to perform secure communication between terminal  2021  and data collection server  2024 . 
     Data collection server  2024  may have a function of authenticating terminal  2021  or a function of encrypting data to be transmitted and received between data collection server  2024  and terminal  2021 . Here, the authentication of terminal  2021  or the encryption of data is performed using, for example, an identifier unique to terminal  2021  or an identifier unique to a terminal group including terminals  2021 , which is shared in advance between data collection server  2024  and terminal  2021 . Examples of the identifier include an international mobile subscriber identity (IMSI) that is a unique number stored in a subscriber identity module (SIM) card. An identifier for use in authentication and an identifier for use in encryption of data may be identical or different. 
     The authentication or the encryption of data between data collection server  2024  and terminal  2021  is feasible when both data collection server  2024  and terminal  2021  have a function of performing the process. The process does not depend on a communication system used by communication device  2022  that performs relay. Accordingly, since it is possible to perform the common authentication or encryption without considering whether terminal  2021  uses a communication system, the user&#39;s convenience of system architecture is increased. However, the expression “does not depend on a communication system used by communication device  2022  that performs relay” means a change according to a communication system is not essential. In other words, in order to improve the transfer efficiency or ensure security, the authentication or the encryption of data between data collection server  2024  and terminal  2021  may be changed according to a communication system used by a relay device. 
     Data collection server  2024  may provide client device  2026  with a User Interface (UI) that manages data collection rules such as types of position-related data collected from terminal  2021  and data collection schedules. Accordingly, a user can specify, for example, terminal  2021  from which data is to be collected using client device  2026 , a data collection time, and a data collection frequency. Additionally, data collection server  2024  may specify, for example, a region on a map from which data is to be desirably collected, and collect position-related data from terminal  2021  included in the region. 
     When the data collection rules are managed on a per terminal  2021  basis, client device  2026  presents, on a screen, a list of terminals  2021  or sensors to be managed. The user sets, for example, a necessity for data collection or a collection schedule for each item in the list. 
     When a region on a map from which data is to be desirably collected is specified, client device  2026  presents, on a screen, a two-dimensional or three-dimensional map of a region to be managed. The user selects the region from which data is to be collected on the displayed map. Examples of the region selected on the map include a circular or rectangular region having a point specified on the map as the center, or a circular or rectangular region specifiable by a drag operation. Client device  2026  may also select a region in a preset unit such as a city, an area or a block in a city, or a main road, etc. Instead of specifying a region using a map, a region may be set by inputting values of a latitude and a longitude, or a region may be selected from a list of candidate regions derived based on inputted text information. Text information is, for example, a name of a region, a city, or a landmark. 
     Moreover, data may be collected while the user dynamically changes a specified region by specifying one or more terminals  2021  and setting a condition such as within 100 meters of one or more terminals  2021 . 
     When client device  2026  includes a sensor such as a camera, a region on a map may be specified based on a position of client device  2026  in an actual space obtained from sensor data. For example, client device  2026  may estimate a self-location using sensor data, and specify, as a region from which data is to be collected, a region within a predetermined distance from a point on a map corresponding to the estimated location or a region within a distance specified by the user. Client device  2026  may also specify, as the region from which the data is to be collected, a sensing region of the sensor, that is, a region corresponding to obtained sensor data. Alternatively, client device  2026  may specify, as the region from which the data is to be collected, a region based on a location corresponding to sensor data specified by the user. Either client device  2026  or data collection server  2024  may estimate a region on a map or a location corresponding to sensor data. 
     When a region on a map is specified, data collection server  2024  may specify terminal  2021  within the specified region by collecting current position information of each terminal  2021 , and may send a transmission request for position-related data to specified terminal  2021 . When data collection server  2024  transmits information indicating a specified region to terminal  2021 , determines whether terminal  2021  is present within the specified region, and determines that terminal  2021  is present within the specified region, rather than specifying terminal  2021  within the region, terminal  2021  may transmit position-related data. 
     Data collection server  2024  transmits, to client device  2026 , data such as a list or a map for providing the above-described User Interface (UI) in an application executed by client device  2026 . Data collection server  2024  may transmit, to client device  2026 , not only the data such as the list or the map but also an application program. Additionally, the above UI may be provided as contents created using HTML displayable by a browser. It should be noted that part of data such as map data may be supplied from a server, such as map server  2025 , other than data collection server  2024 . 
     When client device  2026  receives an input for notifying the completion of an input such as pressing of a setup key by the user, client device  2026  transmits the inputted information as configuration information to data collection server  2024 . Data collection server  2024  transmits, to each terminal  2021 , a signal for requesting position-related data or notifying position-related data collection rules, based on the configuration information received from client device  2026 , and collects the position-related data. 
     Next, an example of controlling operation of terminal  2021  based on additional information added to three-dimensional or two-dimensional map data will be described. 
     In the present configuration, object information that indicates a position of a power feeding part such as a feeder antenna or a feeder coil for wireless power feeding buried under a road or a parking lot is included in or associated with three-dimensional data, and such object information is provided to terminal  2021  that is a vehicle or a drone. 
     A vehicle or a drone that has obtained the object information to get charged automatically moves so that a position of a charging part such as a charging antenna or a charging coil included in the vehicle or the drone becomes opposite to a region indicated by the object information, and such vehicle or a drone starts to charge itself. It should be noted that when a vehicle or a drone has no automatic driving function, a direction to move in or an operation to perform is presented to a driver or an operator by using an image displayed on a screen, audio, etc. When a position of a charging part calculated based on an estimated self-location is determined to fall within the region indicated by the object information or a predetermined distance from the region, an image or audio to be presented is changed to a content that puts a stop to driving or operating, and the charging is started. 
     Object information need not be information indicating a position of a power feeding part, and may be information indicating a region within which placement of a charging part results in a charging efficiency greater than or equal to a predetermined threshold value. A position indicated by object information may be represented by, for example, the central point of a region indicated by the object information, a region or a line within a two-dimensional plane, or a region, a line, or a plane within a three-dimensional space. 
     According to this configuration, since it is possible to identify the position of the power feeding antenna unidentifiable by sensing data of LiDAR or an image captured by the camera, it is possible to highly accurately align a wireless charging antenna included in terminal  2021  such as a vehicle with a wireless power feeding antenna buried under a road. As a result, it is possible to increase a charging speed at the time of wireless charging and improve the charging efficiency. 
     Object information may be an object other than a power feeding antenna. For example, three-dimensional data includes, for example, a position of an AP for millimeter-wave wireless communication as object information. Accordingly, since terminal  2021  can identify the position of the AP in advance, terminal  2021  can steer a directivity of beam to a direction of the object information and start communication. As a result, it is possible to improve communication quality such as increasing transmission rates, reducing the duration of time before starting communication, and extending a communicable period. 
     Object information may include information indicating a type of an object corresponding to the object information. In addition, when terminal  2021  is present within a region in an actual space corresponding to a position in three-dimensional data of the object information or within a predetermined distance from the region, the object information may include information indicating a process to be performed by terminal  2021 . 
     Object information may be provided by a server different from a server that provides three-dimensional data. When object information is provided separately from three-dimensional data, object groups in which object information used by the same service is stored may be each provided as separate data according to a type of a target service or a target device. 
     Three-dimensional data used in combination with object information may be point cloud data of WLD or keypoint data of SWLD. 
     In the three-dimensional data encoding device, when attribute information of a current three-dimensional point to be encoded is layer-encoded using Levels of Detail (LoDs), the three-dimensional data decoding device may decode the attribute information in layers down to LoD required by the three-dimensional data decoding device and need not decode the attribute information in layers not required. For example, when the total number of LoDs for the attribute information in a bitstream generated by the three-dimensional data encoding device is N, the three-dimensional data decoding device may decode M LoDs (M&lt;N), i.e., layers from the uppermost layer LoD 0  to LoD(M−1), and need not decode the remaining LoDs, i.e., layers down to LoD(N−1). With this, while reducing the processing load, the three-dimensional data decoding device can decode the attribute information in layers from LoD 0  to LoD(M−1) required by the three-dimensional data decoding device. 
       FIG. 158  is a diagram illustrating the foregoing use case. In the example shown in  FIG. 158 , a server stores a three-dimensional map obtained by encoding three-dimensional geometry information and attribute information. The server (the three-dimensional data encoding device) broadcasts the three-dimensional map to client devices (the three-dimensional data decoding devices: for example, vehicles, drones, etc.) in an area managed by the server, and each client device uses the three-dimensional map received from the server to perform a process for identifying the self-position of the client device or a process for displaying map information to a user or the like who operates the client device. 
     The following describes an example of the operation in this case. First, the server encodes the geometry information of the three-dimensional map using an octree structure or the like. Then, the sever layer-encodes the attribute information of the three-dimensional map using N LoDs established based on the geometry information. The server stores a bitstream of the three-dimensional map obtained by the layer-encoding. 
     Next, in response to a send request for the map information from the client device in the area managed by the server, the server sends the bitstream of the encoded three-dimensional map to the client device. 
     The client device receives the bitstream of the three-dimensional map sent from the server, and decodes the geometry information and the attribute information of the three-dimensional map in accordance with the intended use of the client device. For example, when the client device performs highly accurate estimation of the self-position using the geometry information and the attribute information in N LoDs, the client device determines that a decoding result to the dense three-dimensional points is necessary as the attribute information, and decodes all the information in the bitstream. 
     Moreover, when the client device displays the three-dimensional map information to a user or the like, the client device determines that a decoding result to the sparse three-dimensional points is necessary as the attribute information, and decodes the geometry information and the attribute information in M LoDs (M&lt;N) starting from an upper layer LoD 0 . 
     In this way, the processing load of the client device can be reduced by changing LoDs for the attribute information to be decoded in accordance with the intended use of the client device. 
     In the example shown in  FIG. 158 , for example, the three-dimensional map includes geometry information and attribute information. The geometry information is encoded using the octree. The attribute information is encoded using N LoDs. 
     Client device A performs highly accurate estimation of the self-position. In this case, client device A determines that all the geometry information and all the attribute information are necessary, and decodes all the geometry information and all the attribute information constructed from N LoDs in the bitstream. 
     Client device B displays the three-dimensional map to a user. In this case, client device B determines that the geometry information and the attribute information in M LoDs (M&lt;N) are necessary, and decodes the geometry information and the attribute information constructed from M LoDs in the bitstream. 
     It is to be noted that the server may broadcast the three-dimensional map to the client devices, or multicast or unicast the three-dimensional map to the client devices. 
     The following describes a variation of the system according to the present embodiment. In the three-dimensional data encoding device, when attribute information of a current three-dimensional point to be encoded is layer-encoded using LoDs, the three-dimensional data encoding device may encode the attribute information in layers down to LoD required by the three-dimensional data decoding device and need not encode the attribute information in layers not required. For example, when the total number of LoDs is N, the three-dimensional data encoding device may generate a bitstream by encoding M LoDs (M&lt;N), i.e., layers from the uppermost layer LoD 0  to LoD(M−1), and not encoding the remaining LoDs, i.e., layers down to LoD(N−1). With this, in response to a request from the three-dimensional data decoding device, the three-dimensional data encoding device can provide a bitstream in which the attribute information from LoD 0  to LoD(M−1) required by the three-dimensional data decoding device is encoded. 
       FIG. 159  is a diagram illustrating the foregoing use case. In the example shown in  FIG. 159 , a server stores a three-dimensional map obtained by encoding three-dimensional geometry information and attribute information. The server (the three-dimensional data encoding device) unicasts, in response to a request from the client device, the three-dimensional map to a client device (the three-dimensional data decoding device: for example, a vehicle, a drone, etc.) in an area managed by the server, and the client device uses the three-dimensional map received from the server to perform a process for identifying the self-position of the client device or a process for displaying map information to a user or the like who operates the client device. 
     The following describes an example of the operation in this case. First, the server encodes the geometry information of the three-dimensional map using an octree structure, or the like. Then, the sever generates a bitstream of three-dimensional map A by layer-encoding the attribute information of the three-dimensional map using N LoDs established based on the geometry information, and stores the generated bitstream in the server. The sever also generates a bitstream of three-dimensional map B by layer-encoding the attribute information of the three-dimensional map using M LoDs (M&lt;N) established based on the geometry information, and stores the generated bitstream in the server. 
     Next, the client device requests the server to send the three-dimensional map in accordance with the intended use of the client device. For example, when the client device performs highly accurate estimation of the self-position using the geometry information and the attribute information in N LoDs, the client device determines that a decoding result to the dense three-dimensional points is necessary as the attribute information, and requests the server to send the bitstream of three-dimensional map A. Moreover, when the client device displays the three-dimensional map information to a user or the like, the client device determines that a decoding result to the sparse three-dimensional points is necessary as the attribute information, and requests the server to send the bitstream of three-dimensional map B including the geometry information and the attribute information in M LoDs (M&lt;N) starting from an upper layer LoD 0 . Then, in response to the send request for the map information from the client device, the server sends the bitstream of encoded three-dimensional map A or B to the client device. 
     The client device receives the bitstream of three-dimensional map A or B sent from the server in accordance with the intended use of the client device, and decodes the received bitstream. In this way, the server changes a bitstream to be sent, in accordance with the intended use of the client device. With this, it is possible to reduce the processing load of the client device. 
     In the example shown in  FIG. 159 , the server stores three-dimensional map A and three-dimensional map B. The server generates three-dimensional map A by encoding the geometry information of the three-dimensional map using, for example, an octree structure, and encoding the attribute information of the three-dimensional map using N LoDs. In other words, NumLoD included in the bitstream of three-dimensional map A indicates N. 
     The server also generates three-dimensional map B by encoding the geometry information of the three-dimensional map using, for example, an octree structure, and encoding the attribute information of the three-dimensional map using M LoDs. In other words, NumLoD included in the bitstream of three-dimensional map B indicates M. 
     Client device A performs highly accurate estimation of the self-position. In this case, client device A determines that all the geometry information and all the attribute information are necessary, and requests the server to send three-dimensional map A including all the geometry information and the attribute information constructed from N LoDs. Client device A receives three-dimensional map A, and decodes all the geometry information and the attribute information constructed from N LoDs. 
     Client device B displays the three-dimensional map to a user. In this case, client device B determines that all the geometry information and the attribute information in M LoDs (M&lt;N) are necessary, and requests the server to send three-dimensional map B including all the geometry information and the attribute information constructed from M LoDs. Client device B receives three-dimensional map B, and decodes all the geometry information and the attribute information constructed from M LoDs. 
     It is to be noted that in addition to three-dimensional map B, the server (the three-dimensional data encoding device) may generate three-dimensional map C in which attribute information in the remaining N-M LoDs is encoded, and send three-dimensional map C to client device B in response to the request from client device B. Moreover, client device B may obtain the decoding result of N LoDs using the bitstreams of three-dimensional maps B and C. 
     Hereinafter, an example of an application process will be described.  FIG. 160  is a flowchart illustrating an example of the application process. When an application operation is started, a three-dimensional data demultiplexing device obtains an ISOBMFF file including point cloud data and a plurality of pieces of encoded data (S 7301 ). For example, the three-dimensional data demultiplexing device may obtain the ISOBMFF file through communication, or may read the ISOBMFF file from the accumulated data. 
     Next, the three-dimensional data demultiplexing device analyzes the general configuration information in the ISOBMFF file, and specifies the data to be used for the application (S 7302 ). For example, the three-dimensional data demultiplexing device obtains data that is used for processing, and does not obtain data that is not used for processing. 
     Next, the three-dimensional data demultiplexing device extracts one or more pieces of data to be used for the application, and analyzes the configuration information on the data ( 57303 ). 
     When the type of the data is encoded data (encoded data in S 7304 ), the three-dimensional data demultiplexing device converts the ISOBMFF to an encoded stream, and extracts a timestamp (S 7305 ). Additionally, the three-dimensional data demultiplexing device refers to, for example, the flag indicating whether or not the synchronization between data is aligned to determine whether or not the synchronization between data is aligned, and may perform a synchronization process when not aligned. 
     Next, the three-dimensional data demultiplexing device decodes the data with a predetermined method according to the timestamp and the other instructions, and processes the decoded data (S 7306 ). 
     On the other hand, when the type of the data is RAW data (RAW data in S 7304 ), the three-dimensional data demultiplexing device extracts the data and timestamp (S 7307 ). Additionally, the three-dimensional data demultiplexing device may refer to, for example, the flag indicating whether or not the synchronization between data is aligned to determine whether or not the synchronization between data is aligned, and may perform a synchronization process when not aligned. Next, the three-dimensional data demultiplexing device processes the data according to the timestamp and the other instructions (S 7308 ). 
     For example, an example will be described in which the sensor signals obtained by a beam LiDAR, a FLASH LiDAR, and a camera are encoded and multiplexed with respective different encoding schemes.  FIG. 161  is a diagram illustrating examples of the sensor ranges of a beam LiDAR, a FLASH LiDAR, and a camera. For example, the beam LiDAR detects all directions in the periphery of a vehicle (sensor), and the FLASH LiDAR and the camera detect the range in one direction (for example, the front) of the vehicle. 
     In the case of an application that integrally handles a LiDAR point cloud, the three-dimensional data demultiplexing device refers to the general configuration information, and extracts and decodes the encoded data of the beam LiDAR and the FLASH LiDAR. Additionally, the three-dimensional data demultiplexing device does not extract camera images. 
     According to the timestamps of the beam LiDAR and the FLASH LiDAR, the three-dimensional data demultiplexing device simultaneously processes the respective encoded data of the time of the same timestamp. 
     For example, the three-dimensional data demultiplexing device may present the processed data with a presentation device, may synthesize the point cloud data of the beam LiDAR and the FLASH LiDAR, or may perform a process such as rendering. 
     Additionally, in the case of an application that performs calibration between data, the three-dimensional data demultiplexing device may extract sensor geometry information, and use the sensor geometry information in the application. 
     For example, the three-dimensional data demultiplexing device may select whether to use beam LiDAR information or FLASH LiDAR information in the application, and may switch the process according to the selection result. 
     In this manner, since it is possible to adaptively change the obtaining of data and the encoding process according to the process of the application, the processing amount and the power consumption can be reduced. 
     Hereinafter, a use case in automated driving will be described.  FIG. 162  is a diagram illustrating a configuration example of an automated driving system. This automated driving system includes cloud server  7350 , and edge  7360  such as an in-vehicle device or a mobile device. Cloud server  7350  includes demultiplexer  7351 , decoders  7352 A,  7352 B, and  7355 , point cloud data synthesizer  7353 , large data accumulator  7354 , comparator  7356 , and encoder  7357 . Edge  7360  includes sensors  7361 A and  7361 B, point cloud data generators  7362 A and  7362 B, synchronizer  7363 , encoders  7364 A and  7364 B, multiplexer  7365 , update data accumulator  7366 , demultiplexer  7367 , decoder  7368 , filter  7369 , self-position estimator  7370 , and driving controller  7371 . 
     In this system, edge  7360  downloads large data, which is large point-cloud map data accumulated in cloud server  7350 . Edge  7360  performs a self-position estimation process of edge  7360  (a vehicle or a terminal) by matching the large data with the sensor information obtained by edge  7360 . Additionally, edge  7360  uploads the obtained sensor information to cloud server  7350 , and updates the large data to the latest map data. 
     Additionally, in various applications that handle point cloud data in the system, point cloud data with different encoding methods are handled. 
     Cloud server  7350  encodes and multiplexes large data. Specifically, encoder  7357  performs encoding by using a third encoding method suitable for encoding a large point cloud. Additionally, encoder  7357  multiplexes encoded data. Large data accumulator  7354  accumulates the data encoded and multiplexed by encoder  7357 . 
     Edge  7360  performs sensing. Specifically, point cloud data generator  7362 A generates first point cloud data (geometry information (geometry) and attribute information) by using the sensing information obtained by sensor  7361 A. Point cloud data generator  7362 B generates second point cloud data (geometry information and attribute information) by using the sensing information obtained by sensor  7361 B. The generated first point cloud data and second point cloud data are used for the self-position estimation or vehicle control of automated driving, or for map updating. In each process, a part of information of the first point cloud data and the second point cloud data may be used. 
     Edge  7360  performs the self-position estimation. Specifically, edge  7360  downloads large data from cloud server  7350 . Demultiplexer  7367  obtains encoded data by demultiplexing the large data in a file format. Decoder  7368  obtains large data, which is large point-cloud map data, by decoding the obtained encoded data. 
     Self-position estimator  7370  estimates the self-position in the map of a vehicle by matching the obtained large data with the first point cloud data and the second point cloud data generated by point cloud data generators  7362 A and  7362 B. Additionally, driving controller  7371  uses the matching result or the self-position estimation result for driving control. 
     Note that self-position estimator  7370  and driving controller  7371  may extract specific information, such as geometry information, of the large data, and may perform processes by using the extracted information. Additionally, filter  7369  performs a process such as correction or decimation on the first point cloud data and the second point cloud data. Self-position estimator  7370  and driving controller  7371  may use the first point cloud data and second point cloud data on which the process has been performed. Additionally, self-position estimator  7370  and driving controller  7371  may use the sensor signals obtained by sensors  7361 A and  7361 B. 
     Synchronizer  7363  performs time synchronization and geometry correction between a plurality of sensor signals or the pieces of data of a plurality of pieces of point cloud data. Additionally, synchronizer  7363  may correct the geometry information on the sensor signal or point cloud data to match the large data, based on geometry correction information on the large data and sensor data generated by the self-position estimation process. 
     Note that synchronization and geometry correction may be performed not by edge  7360 , but by cloud server  7350 . In this case, edge  7360  may multiplex the synchronization information and the geometry information to transmit the synchronization information and the geometry information to cloud server  7350 . 
     Edge  7360  encodes and multiplexes the sensor signal or point cloud data. Specifically, the sensor signal or point cloud data is encoded by using a first encoding method or a second encoding method suitable for encoding each signal. For example, encoder  7364 A generates first encoded data by encoding first point cloud data by using the first encoding method. Encoder  7364 B generates second encoded data by encoding second point cloud data by using the second encoding method. 
     Multiplexer  7365  generates a multiplexed signal by multiplexing the first encoded data, the second encoded data, the synchronization information, and the like. Update data accumulator  7366  accumulates the generated multiplexed signal. Additionally, update data accumulator  7366  uploads the multiplexed signal to cloud server  7350 . 
     Cloud server  7350  synthesizes the point cloud data. Specifically, demultiplexer  7351  obtains the first encoded data and the second encoded data by demultiplexing the multiplexed signal uploaded to cloud server  7350 . Decoder  7352 A obtains the first point cloud data (or sensor signal) by decoding the first encoded data. Decoder  7352 B obtains the second point cloud data (or sensor signal) by decoding the second encoded data. 
     Point cloud data synthesizer  7353  synthesizes the first point cloud data and the second point cloud data with a predetermined method. When the synchronization information and the geometry correction information are multiplexed in the multiplexed signal, point cloud data synthesizer  7353  may perform synthesis by using these pieces of information. 
     Decoder  7355  demultiplexes and decodes the large data accumulated in large data accumulator  7354 . Comparator  7356  compares the point cloud data generated based on the sensor signal obtained by edge  7360  with the large data held by cloud server  7350 , and determines the point cloud data that needs to be updated. Comparator  7356  updates the point cloud data that is determined to need to be updated of the large data to the point cloud data obtained from edge  7360 . 
     Encoder  7357  encodes and multiplexes the updated large data, and accumulates the obtained data in large data accumulator  7354 . 
     As described above, the signals to be handled may be different, and the signals to be multiplexed or encoding methods may be different, according to the usage or applications to be used. Even in such a case, flexible decoding and application processes are enabled by multiplexing data of various encoding schemes by using the present embodiment. Additionally, even in a case where the encoding schemes of signals are different, by conversion to an encoding scheme suitable for demultiplexing, decoding, data conversion, encoding, and multiplexing processing, it becomes possible to build various applications and systems, and to offer of flexible services. 
     Hereinafter, an example of decoding and application of divided data will be described. First, the information on divided data will be described.  FIG. 163  is a diagram illustrating a configuration example of a bitstream. The general information of divided data indicates, for each divided data, the sensor ID (sensor_id) and data ID (data_id) of the divided data. Note that the data ID is also indicated in the header of each encoded data. 
     Note that the general information of divided data illustrated in  FIG. 163  includes, in addition to the sensor ID, at least one of the sensor information (Sensor), the version (Version) of the sensor, the maker name (Maker) of the sensor, the mount information (Mount Info.) of the sensor, and the position coordinates of the sensor (World Coordinate). Accordingly, the three-dimensional data decoding device can obtain the information on various sensors from the configuration information. 
     The general information of divided data may be stored in SPS, GPS, or APS, which is the metadata, or may be stored in SEI, which is the metadata not required for encoding. Additionally, at the time of multiplexing, the three-dimensional data encoding device stores the SEI in a file of ISOBMFF. The three-dimensional data decoding device can obtain desired divided data based on the metadata. 
     In  FIG. 163 , SPS is the metadata of the entire encoded data, GPS is the metadata of the geometry information, APS is the metadata for each attribute information, G is encoded data of the geometry information for each divided data, and A 1 , etc. are encoded data of the attribute information for each divided data. 
     Next, an application example of divided data will be described. An example of application will be described in which an arbitrary point cloud is selected, and the selected point cloud is presented.  FIG. 164  is a flowchart of a point cloud selection process performed by this application.  FIG. 165  to  FIG. 167  are diagrams illustrating screen examples of the point cloud selection process. 
     As illustrated in  FIG. 165 , the three-dimensional data decoding device that performs the application includes, for example, a UI unit that displays an input UI (user interface)  8661  for selecting an arbitrary point cloud. Input UI  8661  includes presenter  8662  that presents the selected point cloud, and an operation unit (buttons  8663  and  8664 ) that receives operations by a user. After a point cloud is selected in UI  8661 , the three-dimensional data decoding device obtains desired data from accumulator  8665 . 
     First, based on an operation by the user on input UI  8661 , the point cloud information that the user wants to display is selected (S 8631 ). Specifically, by selecting button  8663 , the point cloud based on sensor  1  is selected. By selecting button  8664 , the point cloud based on sensor  2  is selected. Alternatively, by selecting both button  8663  and button  8664 , the point cloud based on sensor  1  and the point cloud based on sensor  2  are selected. Note that it is an example of the selection method of point cloud, and it is not limited to this. 
     Next, the three-dimensional data decoding device analyzes the general information of divided data included in the multiplexed signal (bitstream) or encoded data, and specifies the data ID (data_id) of the divided data constituting the selected point cloud from the sensor ID (sensor_id) of the selected sensor (S 8632 ). Next, the three-dimensional data decoding device extracts, from the multiplexed signal, the encoded data including the specified and desired data ID. and decodes the extracted encoded data to decode the point cloud based on the selected sensor (S 8633 ). Note that the three-dimensional data decoding device does not decode the other encoded data. 
     Lastly, the three-dimensional data decoding device presents (for example, displays) the decoded point cloud (S 8634 ).  FIG. 166  illustrates an example in the case where button  8663  for sensor  1  is pressed, and the point cloud of sensor  1  is presented.  FIG. 167  illustrates an example in the case where both button  8663  for sensor  1  and button  8664  for sensor  2  are pressed, and the point clouds of sensor  1  and sensor  2  are presented. 
     Embodiment 9 
     Recently, the performance of sensors has been improving, and it has become possible to generate very high quality three-dimensional point clouds (three-dimensional data). On the other hand, the data size of a three-dimensional point cloud has been increasing along with the improvement in the performance of sensors. In a three-dimensional point cloud having an enormous data size, when used for navigation, display applications, or the like, a problem arises in that the processing amount is increased in decoding and loading. 
     In many applications used for the viewer for displaying a three-dimensional point cloud, the navigation of an autonomous travelling vehicle, or the like, a three-dimensional point cloud that exists in a position corresponding to the imaging range of the camera often becomes a point of interest, i.e., a three-dimensional point cloud especially requested by the user. By utilizing this point of interest together with partitioning of three-dimensional point clouds (for example, information indicating the spatial dividing positions of three-dimensional point clouds defined in advance, such as bounding boxes of tiles), a portion related to the user&#39;s current position or the like in a three-dimensional point cloud can be efficiently decoded and loaded to be utilized for the applications. 
     For example, let the vector connecting the camera position and the focal point position be a first vector. Additionally, for example, let the vector connecting the tile position and one of the camera position and the focal point position be a second vector. The angle between the vectors of the first vector and the second vector is calculated by an inner product. The visibility of a tile (also referred to here as three-dimensional space information) within a point cloud that is divided into tiles depends on the angle formed by the first vector and the second vector. 
     When a tile has visibility, the tile is decoded and loaded. On the other hand, when a tile does not have visibility, the tile is not decoded. The three-dimensional data decoding device according to the present embodiment determines the visibility of a tile based on whether or not the angle formed by the above-described two vectors is equal to or more than a threshold value, and decodes or does not decode the tile based on the determination result. 
     Accordingly, the three-dimensional data decoding device can selectively decode or load a part of a three-dimensional point cloud by efficiently using a memory. 
       FIG. 168  is a diagram for describing the center position of a tile according to the present embodiment. 
     The center position of a tile can be calculated by using, for example, the origin position of the tile, and the size information indicating the size of the tile obtained from the header of the tile, without using other information. 
     The origin position of a tile is, for example, a position where the values of coordinates are the smallest in the tile in the coordinate space in which the tile is located. In  FIG. 168 , the origin position of a tile is, for example, a position where the X coordinate, the Y coordinate, and the Z coordinate are the smallest in the XYZ coordinate system in which the tile is located. 
     Note that the origin position of a tile may be, for example, a position where the coordinate of one of the X coordinate, the Y coordinate, and the Z coordinate is the smallest, according to the shape, the posture, and the like of the tile. 
     The three-dimensional data encoding device that encodes a three-dimensional point cloud by using a point-cloud compression encoding scheme, such as G-PCC or V-PCC, can divide data into tiles or slices. When encoding a three-dimensional point cloud by using the point-cloud compression encoding scheme, such as G-PCC or V-PCC, the three-dimensional data encoding device independently encodes each of divided data without dependency. Additionally, since the three-dimensional data decoding device can independently decode each tile or slice of the three-dimensional point cloud encoded in this manner, the three-dimensional data decoding device can decode from a desired tile or slice in order, or can decode a plurality of tiles or slices in parallel by using a plurality of CPUs (Central Processing Units). 
     The three-dimensional data decoding device determines a tile that is a decoding target in, for example, controller  9902  described later, and decodes the determined tile. 
       FIG. 169  is a block diagram for describing a process of decoding an encoded three-dimensional point cloud according to the present embodiment. 
     When a three-dimensional point cloud (point cloud data) is input, encoder  9900  generates encoded data obtained by encoding the three-dimensional point cloud. For example, encoder  9900  encodes the three-dimensional point cloud by using the point-cloud compression encoding scheme, such as G-PCC or V-PCC, thereby dividing the three-dimensional point cloud into a plurality of tiles, and encoding the plurality of tiles on a tile-by-tile basis. 
     When encoded data is input, decoder  9901  decodes the encoded data. 
     Controller  9902  determines a tile (specifically, an encoded tile) to be decoded by decoder  9901 , and instructs the determined tile to decoder  9901 . Accordingly, decoder  9901  selectively decodes the tile specified by controller  9902  among a plurality of tiles, thereby outputting a part of (that is, partial) point cloud data of the point cloud data that can be decoded from the encoded data. 
     Accordingly, tiles desired by the user are partially decoded by appropriately selecting, by controller  9902 , for example, the tiles desired by the user. Therefore, since decoder  9901  need not perform the process of decoding tiles that are not particularly desired by the user, the processing amount in decoder  9901  can be reduced. 
     Note that controller  9902  may be included in the three-dimensional data decoding device, may be an application, or a tile that is a decoding target may be directly specified by the user, without the three-dimensional data decoding device having controller  9902 . 
     The present embodiment relates to the method of determining a tile to be decoded by the three-dimensional data decoding device, and specifically, the visibility of tiles is calculated, and the tile to be decoded is determined based on the calculated visibility. 
     Note that, although the unit of data division is described as a tile in the present embodiment, the unit of data division is not limited to this, and may be a slice, or may be other divided data. 
       FIG. 170  is a diagram for describing a process of calculating angle information used for determination of visibility by the three-dimensional data decoding device according to the present embodiment. 
     The three-dimensional data decoding device calculates the angle indicating the visibility to be used when selecting a tile to be decoded (or, to be decoded and loaded), by calculating, for example, the inner product of two vectors related to tiles, such as a vector (vector C illustrated in  FIG. 170 ) from a focal point position to the camera position, and a vector (vector S illustrated in  FIG. 170 ) from the focal point position to the center position of the tile. 
     Note that, in the present example, although the focal point position is the position of the focus of the camera located in the camera position, the focal point position may be arbitrarily set by the user, and is not particularly limited. The focal point position need not be the center of a three-dimensional point cloud. 
     The three-dimensional data decoding device calculates cos (φ) from the vector C and the vector S by using, for example, the following formula (Q1). 
     
       
         
           
             
               
                 
                   
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     Cos (φ) (more specifically, the value of cos (φ)) indicates the information (that is, the visibility) indicating how many tiles can be seen from the camera, when the focal point position of the camera is given. 
     For example, when cos (φ)=1, it is indicated that the vector C and the vector S have the same orientation. 
     Additionally, for example, when cos (φ)=−1, it is indicated that the vector C and the vector S have the opposite orientations. 
     In addition, when cos (φ)=0, it is indicated the vector C is perpendicular to the vector S. 
     The three-dimensional data decoding device determines whether or not to decode and load a tile based on, for example, whether or not cos (φ) is between 1-X and 1. That is, the three-dimensional data decoding device determines whether or not there is visibility based on whether or not the calculated angle satisfies a predetermined condition (for example, 1-X&lt;cos (φ)&lt;1). For example, it means that the larger the value of X, the larger the angle for which the three-dimensional data decoding device determines that there is visibility. 
     The value of X may be a fixed value defined in advance, or may be dynamically determined by using a predetermined method. 
     The visibility of each tile is calculated more accurately based on the distance between the focal point position and the position of an arbitrary point of a tile (more specifically, in the tile), such as the center position of the tile. 
     Note that, when employing an actual camera position, the camera position may be the coordinates of the camera position, or in the case of an application such as a viewer, the camera position may be a virtual camera position on the application. 
     Additionally, a similar method can be used not only for the camera but also for other sensors, such as LiDAR or millimeter waves. 
     The geometry information on a tile (for example, the information indicating the origin position of a tile), the information indicating the size of a tile, and the information related to the shape of a tile are stored in, for example, encoded data as metadata in advance. For example, the three-dimensional data decoding device obtains the above-described information on a tile by decoding the metadata from the encoded data, and calculates the center position of the tile. 
     Note that the information on the center of a tile may be stored in encoded data in advance. 
     Additionally, although the example has been illustrated in which the three-dimensional data decoding device calculates the inner product of the vector C and the vector S as the method of calculating the information related to the angle formed by the vector C and the vector S, the method is not limited to this. For example, the three-dimensional data decoding device may calculate sin (φ) or tan (φ), or a calculating method related to other angles may be used. 
     Additionally, not only the example of the vector from the focal point position to the center position of a tile, but also other positions of a tile (for example, the origin position (minimum position) of a tile, the maximum position of a tile (more specifically, the position indicating the maximum values of coordinates), or the position of the center of gravity of a tile) may be used, the origin position of data (for example, a slice) included in a tile, the maximum position of the data, or the position of the center of gravity of the data may be used, or a plurality of pieces of information each indicating a position may be combined and used. 
     Accordingly, the visibility suitable for the shape of a tile is calculated. 
     Additionally, the data of a tile need not be obtained from encoded data, and may be, for example, area information that is defined in advance. For example, when sections of map data or the like are defined in advance, the visibility may be calculated based on the sections, the camera position, and the focal point position. That is, a tile (more specifically, a bounding box indicating the range of a tile) in the present embodiment may be read as a section in map data. 
     In addition, the tile information may be included in encoded data by the three-dimensional data decoding device, or the three-dimensional data decoding device may hold the tile information in advance in the memory or the like included in the three-dimensional data decoding device. 
     Further, the three-dimensional data decoding device may set a plurality of camera positions and a plurality of focal point positions, and may determine the visibility of tiles in combination with respective vectors. 
     Note that a determination method of X and a determination method of a focal point position will be described later. 
       FIG. 171  is a block diagram illustrating the configuration for specifying a decoding target according to the present embodiment. For example, the three-dimensional data decoding device (more specifically, for example, controller  9902  described above) according to the present embodiment includes each configuration illustrated in  FIG. 171 . For example, the three-dimensional data decoding device includes tile position obtainer  9910 , focal point position obtainer  9911 , camera position obtainer  9912 , tile-focal point distance calculator  9913 , tile-focal point vector calculator  9914 , focal point-camera vector calculator  9915 , X determiner  9916 , angle calculator  9917 , selector  9918 , visibility determiner  9919 , and decoding tile determiner  9920 . 
     Tile position obtainer  9910  is a processing unit that obtains the information indicating the position of a tile from metadata or the like included in encoded data. For example, tile position obtainer  9910  obtains the information indicating the positions of a tile, such as the center position of a tile, the origin position of a tile, or the position of the center of gravity of a tile described above. Note that tile position obtainer  9910  may obtain the origin position of a tile, and the size information indicating the size of a tile obtained from the header of the tile, and may use the obtained information to calculate and obtain the information indicating the center position of the tile. Tile position obtainer  9910  outputs the obtained information indicating the tile position to tile-focal point distance calculator  9913  and tile-focal point vector calculator  9914 . 
     Focal point position obtainer  9911  is a processing unit that obtains the information indicating the focal point position of a camera from encoded data or the like. Focal point position obtainer  9911  outputs the obtained information indicating the focal point position of the camera to tile-focal point distance calculator  9913 , tile-focal point vector calculator  9914 , and focal point-camera vector calculator  9915 . 
     Camera position obtainer  9912  is a processing unit that obtains the information indicating the position of the camera from encoded data or the like. Camera position obtainer  9912  outputs the obtained information indicating the position of the camera to focal point-camera vector calculator  9915 . 
     Tile-focal point distance calculator  9913  is a processing unit that calculates the distance between a tile and the focal point position of the camera, based on the information indicating the position of the tile, and the information indicating the focal point position of the camera. For example, tile-focal point distance calculator  9913  calculates the distance from the focal point position of the camera to the center position of the tile. Tile-focal point distance calculator  9913  outputs the information indicating the calculated distance to X determiner  9916 . 
     Tile-focal point vector calculator  9914  is a processing unit that calculates a vector connecting a tile and the focal point position of the camera, based on the information indicating the position of the tile, and the information indicating the focal point position of the camera. For example, tile-focal point vector calculator  9914  calculates a vector (vector S) from the focal point position of the camera to the center position of the tile. Tile-focal point vector calculator  9914  outputs the calculated vector S to angle calculator  9917 . 
     Focal point-camera vector calculator  9915  is a processing unit that calculates a vector connecting the focal point position of the camera and the position of the camera, based on the information indicating the focal point position of the camera, and the information indicating the position of the camera. For example, the focal point-camera vector calculator  9915  calculates the vector (vector C) from the focal point position of the camera to the position of the camera. Focal point-camera vector calculator  9915  outputs the calculated vector C to angle calculator  9917 . 
     X determiner  9916  is a processing unit that determines (calculates) the above-described X (calculated value) based on the information indicating the distance between the tile and the focal point position of the camera calculated by tile-focal point distance calculator  9913 . That is, X determiner  9916  determines a threshold value of cos (φ) to be used when the visibility is determined by visibility determiner  9919  based on the distance between the tile and the focal point position of the camera. X determiner  9916  outputs the information indicating the calculated value of X to selector  9918 . 
     Angle calculator  9917  is a processing unit that calculates the angle formed by a line segment connecting the focal point position of the camera and the tile, and a line segment connecting the focal point position of the camera and the position of the camera. In the present embodiment, angle calculator  9917  calculates the above-described cos (φ) from the inner product of the vector S and the vector C. Angle calculator  9917  outputs the information indicating the calculated cos (φ) to visibility determiner  9919 . 
     Selector  9918  is a processing unit that selects a threshold value to be used when the visibility is determined by visibility determiner  9919 . Specifically, selector  9918  selects one of X (the calculated value) calculated by X determiner  9916 , and X (constant) obtained via encoded data, an input by the user, or the like, and outputs the selected X to visibility determiner  9919 . In this manner, X employed as the threshold value may be a fixed value that is defined in advance, or may be calculated with a predetermined method, such as calculation based on the distance between a tile and the focal point position of a camera. Note that X may be calculated not based on the distance between a tile and the focal point position of a camera, but on other parameters. Additionally, the selection method as to whether selector  9918  selects X (the calculated value) or selects X (the fixed value) may be arbitrarily defined. For example, when there is an input of X (the fixed value), selector  9918  selects X (the fixed value) and outputs X to visibility determiner  9919 , and when there is no input of X (the fixed value), selector  9918  selects X (the calculated value) and outputs X to visibility determiner  9919 . 
     Visibility determiner  9919  is a processing unit that determines the visibility of a tile, based on the information indicating the threshold value (more specifically, X) that is output from selector  9918 , and the information (more specifically, cos (φ)) indicating the angle that is output from angle calculator  9917 . Specifically, visibility determiner  9919  determines whether or not a tile has visibility, based on whether or not the angle formed by the line segment connecting the focal point position of the camera and the tile, and the line segment connecting the focal point position of the camera and the position of the camera is equal to or more than the threshold value. More specifically, visibility determiner  9919  determines whether or not a tile has visibility by determining whether or not 1-X&lt;cos (φ)&lt;1 is satisfied. For example, when it is determined that 1-X&lt;cos (φ)&lt;1 is satisfied, visibility determiner  9919  determines that the tile has visibility. On the other hand, for example, when it is determined that 1-X&lt;cos (φ)&lt;1 is not satisfied, visibility determiner  9919  determines that the tile does not have visibility. 
     Visibility determiner  9919  outputs the information indicating the determination result of the visibility of the tile to decoding tile determiner  9920 . 
     Decoding tile determiner  9920  is a processing unit that determines whether or not to decode the tile, based on the information indicating the determination result of the visibility of the tile determined by visibility determiner  9919 . For example, determiner  9920  determines to decode the decoding tile when the tile has visibility, and not to decode the tile when the tile does not have visibility. Decoding tile determiner  9920  outputs the information indicating the determined content to, for example, decoder  9901 . 
     The three-dimensional data decoding device repeats the determination of visibility for one or more tiles as described above, selects a tile that is determined to have visibility, and decodes the selected tile to present the information (for example, map information and the like) indicated by the tile to the user by using a presenter such as a display apparatus. 
     Note that a plurality of threshold values may be set to the above-described threshold value. For example, X determiner  9916  determines (calculates) X and Y (for example, it is assumed that X&lt;Y), and visibility determiner  9919  determines visibility based on one of the conditions (i) 1-X&lt;cos (φ)&lt;1 and (ii) 1-Y&lt;cos (φ)&lt;1-X. When X determiner  9916  calculated the two, i.e., X and Y, and selector  9918  outputs the information including the two values to visibility determiner  9919 , visibility determiner  9919  may select one of the conditions (i) 1-X&lt;cos (φ)&lt;1 and (ii) 1-Y&lt;cos (φ)&lt;1-X, according to the purpose (for example, based on the information that is input by the user). 
       FIG. 172  is a flowchart illustrating a process of determining a decoding target by the three-dimensional data decoding device according to the present embodiment. 
     First, the three-dimensional data decoding device obtains the information indicating the position of a tile from encoded data, and obtains the information (camera parameter) indicating the position of a camera and the focal point position of the camera (S 9901 ). The three-dimensional data decoding device obtains the camera parameter by, for example, communicating with the camera. Additionally, for example, the three-dimensional data decoding device obtains the information indicating the center position of a tile, as the information indicating the position of the tile. 
     Next, the three-dimensional data decoding device calculates a vector from the focal point position of the camera to the position of the camera, and a vector from the focal point position of the camera to the center position of the tile, based on the position (here, the center position) of the tile, the position of the camera, and the focal point position of the camera, and calculates the inner product of the two calculated vectors (S 9902 ). Accordingly, the three-dimensional data decoding device calculates a value (for example, the above-described cos (φ)) related to the angle formed by a line segment connecting the focal point position of the camera and the position of the camera, and a line segment connecting the focal point position of the camera and the center position of the tile. 
     Next, the three-dimensional data decoding device calculates the distance between the center position of the tile, and the focal point position of the camera, and determines the range of visibility (threshold value) for which the visibility of the tile is determined, based on the calculated distance (S 9903 ). For example, the three-dimensional data decoding device determines the above-described X. 
     Next, the three-dimensional data decoding device determines the visibility of the tile, based on the calculated inner product (more specifically, the information indicating the above-described angle), and the range of visibility (more specifically, the above-described X) (S 9904 ). 
     Next, the three-dimensional data decoding device determines whether or not the tile has visibility (S 9905 ). 
     When it is determined that the tile has visibility (Yes in S 9905 ), the three-dimensional data decoding device decodes the tile (S 9906 ). Note that when the three-dimensional data decoding device includes a presenter such as a display apparatus, or is communicatively connected to the presenter, the three-dimensional data decoding device may load (read) the decoded tile to cause the presenter to display the information indicated by the tile. 
     On the other hand, when it is determined that the tile does not have visibility (No in S 9905 ), or next to step S 9906 , the three-dimensional data decoding device determines whether or not the process of step S 9901  to step S 9906  has been completed for all the tiles included in encoded data (S 9907 ). 
     When it is determined that the process has been completed for all the tiles (Yes in S 9907 ), the three-dimensional data decoding device ends the process. 
     On the other hand, when it is determined that the process has not been completed for all the tiles (No in S 9907 ), the three-dimensional data decoding device returns the process to step S 9901 , and performs the process from step S 9901  for tiles that have not been processed. 
     In this manner, when there are a plurality of tiles, the three-dimensional data decoding device performs each step for each of the plurality of tiles. 
     Subsequently, a use case of a decoding process utilizing visibility will be described. 
       FIG. 173  is a diagram for describing a first example of the decoding process by the three-dimensional data decoding device according to the present embodiment. The first example is an example in which map data is divided into a plurality of tiles, and the focal point position of a camera is set in a moving direction of the camera. 
     Note that, in  FIG. 173 , tiles having visibility are indicated by solid line rectangles, tiles not having visibility are indicated by broken line rectangles, the center position of each tile is indicated by a dot, a vector from the focal point position of the camera to the position of the camera is indicated by a solid line arrow, and vectors from the focal point position of the camera to the center positions of tiles are indicated by broken line arrows. Additionally, in  FIG. 173 , objects that block the line of sight from the camera, such as walls, are indicated with hatching. 
     The three-dimensional data decoding device determines, based on, for example, the distance between the focal point position of the camera and the center position of a tile, angles (the range of angle) for determining the visibility of the tile. 
     For example, when the distance from the center position of the tile to the focal point position of the camera is small. The three-dimensional data decoding device determines the limit of cos (φ) to be 0.5 to 1 (X=0.5). For example, when the distance from the center position of the tile to the focal point position of the camera is shorter than a predetermined arbitrary distance, the three-dimensional data decoding device determines that X=0.5. 
     On the other hand, for example, when the distance from the center position of the tile to the focal point position of the camera is large, the three-dimensional data decoding device determines that the limit of cos (φ) to be 0.98 to 1 (X=0.02). For example, when the distance from the center position of the tile to the focal point position of the camera is equal to or more than the predetermined arbitrary distance, the three-dimensional data decoding device determines that X=0.02. 
     In this manner, for example, the three-dimensional data decoding device calculates the distance between the focal point position of the camera and a tile, (i) when the distance between the focal point position and the tile is short (for example, shorter than a predetermined distance), sets a large range of angle (that is, a threshold value) for determining whether or not there is visibility, and (ii) when the distance between the focal point position and the tile is long (for example, equal to or longer than the predetermined distance), sets a small range of angle for determining whether or not there is visibility (that is, the threshold value). 
     Accordingly, the three-dimensional data decoding device is enabled to set the range of angle, so as to determine that tiles located in the moving direction of a mobile body have visibility, by setting the position of a mobile body to the position of the camera, and aligning the imaging direction of the camera with the moving direction of the mobile body. 
     Note that the distance between the focal point position of the camera and the position of the camera may be a predetermined distance, or may be dynamically determined. For example, when the three-dimensional data decoding device obtains the information indicating the speed of the mobile body on which the camera is arranged, and the speed indicated in the obtained information is higher than a predetermined arbitrary speed, a focal point distance of the camera (the distance between the focal point position of the camera, and the position of the camera) may be increased as the speed is increased. Alternatively, the three-dimensional data decoding device may determine the focal point position by detecting a line of sight and a focal point position of a driver of the mobile body with a detector such as a camera. That is, the above-described position of the camera, and the focal point position of the camera may be the position of a user such as a driver, and the focal point position of the user. 
     The present example is suitable for the decoding process by the three-dimensional data decoding device in a case where, for example, map data for navigation, illustrating narrow passages in a building, is divided into a plurality of tiles. Even for the map data illustrating the narrow passages in the building, the three-dimensional data decoding device can partially decode a portion of the map data that is suitable for the user who passes through the passages, by, for example, appropriately setting a threshold value for determining visibility from cos (φ) according to the distance from the center position of a tile to the focal point position of the camera. 
       FIG. 174  is a diagram for describing a second example of the decoding process by the three-dimensional data decoding device according to the present embodiment. The second example is an example in which the map data is divided into a plurality of tiles, and the focal point position of the camera is set to a transverse direction with respect to the moving direction of the camera (a direction perpendicular to the moving direction in top view. 
     Note that, in  FIG. 174 , tiles having visibility are indicated by solid line rectangles, tiles not having visibility are indicated by broken line rectangles, the center position of each tile is indicated by a dot, a vector from the focal point position of the camera to the position of the camera is indicated by a solid line arrow, and vectors from the focal point position of the camera to the center positions of tiles are indicated by broken line arrows. 
     In the present example, the three-dimensional data decoding device sets the threshold value for determining visibility from cos (φ) to be 0 to 1, on the basis of the focal point position in the vicinity of the camera (virtual camera) arranged on the left side with respect to the moving direction of the mobile body. 
     Accordingly, the three-dimensional data decoding device can partially decode and load tiles that are at positions corresponding to the vicinity of the mobile body, and that are in narrow rows. 
     For example, the focal point distance is set to a fixed distance from the camera. 
     Note that the focal point distance may be changed based on the movement speed of the camera. 
     The three-dimensional data decoding device may use the decoding method as in the present example, in a case where a tile is between the position of the camera, and the focal point position of the camera. For example, when the three-dimensional data decoding devices broadly selects a plurality of tiles, such as when map data is displayed on a display apparatus, and a transverse direction of a mobile body is displayed on the display apparatus, the decoding method of the present example may be employed. 
       FIG. 175  is a diagram for describing a third example of the decoding process by the three-dimensional data decoding device according to the present embodiment. The third example is an example in which map data is divided into a plurality of tiles, and a plurality of cameras are set. More specifically, the third example is an example in which the first example and the second example are combined. 
     Note that, in  FIG. 175 , tiles having visibility are indicated by solid line rectangles, tiles not having visibility are indicated by broken line rectangles, the center position of each tile is indicated by a dot, a vector from the focal point position of the camera to the position of the camera is indicated by a solid line arrow, and vectors from the focal point position of the camera to the center positions of tiles are indicated by broken line arrows. 
     When there are a plurality of cameras as in the present example, the three-dimensional data decoding device changes the setting method of the focal point position, and the setting method of the range (more specifically, the above-described X) for determining whether or not there is visibility, based on the angle of each camera with respect to the moving direction. The present example is applied to a case where one camera has a plurality of focal points, or a case where one camera is rotated. 
     Note that, even when a camera and the focal point of the camera do not actually exist, the three-dimensional data decoding device may virtually set the position of the camera and the focal point position of the camera, and may select a tile to be decoded by using a predetermined method. 
     Additionally, when a mobile body equipped with a camera moves, the position and focal point position of the camera are changed. In this case, the three-dimensional data decoding device performs, for example, the determination of visibility again by using the angle described in the present embodiment, and selects a tile to be decoded. The three-dimensional data decoding device may perform the determination again based on the travel distance of the position of the camera, or may perform the determination again based on elapsed time. For example, the three-dimensional data decoding device may perform the determination again after a mobile body travels for a predetermined distance, or may perform the determination again every time a predetermined time elapses. Additionally, for example, the three-dimensional data decoding device may predict a future position of the camera and the focal point position of the camera, based on the direction of travel of the mobile body or the movement speed of the mobile body, and may perform the determination based on the position of the camera and the focal point position of the camera predicted in advance. 
       FIG. 176  is a diagram for describing a fourth example of the decoding process by the three-dimensional data decoding device according to the present embodiment. The fourth example is an example in which map data is divided into a plurality of tiles, and a plurality of virtual cameras are set. 
     Note that, in  FIG. 176 , tiles are indicated by solid line rectangles, a vector from the focal point position of a camera to the position of the camera is indicated by a solid line arrow, and vectors from the focal point position of the camera to the center positions of tiles are indicated by broken line arrows. Additionally, the value given in the center of each tile indicates the tile number of each tile. 
     For example, when three-dimensional point clouds from a plurality of viewpoints are presented at once in a rendering software or the like, in each viewpoint, a tile to be decoded is selected by determining the visibility of tiles based on the position of the camera, the focal point position of the camera, and the positions of the tiles. 
     The three-dimensional data decoding device takes OR of tiles selected in each viewpoint, and selects and decodes the tiles. That is, the three-dimensional data decoding device decodes all the tiles selected in each viewpoint. 
     For example, divided data selector  9922  selects a tile having visibility, based on the position of first virtual camera  9921  that is virtually set as first virtual camera  9921 , the focal point position of first virtual camera  9921 , and the position of the tile. Here, it is assumed that divided data selector  9922  has selected tiles with the tile numbers 1, 2, and 3. For example, divided data selector  9922  outputs the information indicating the selected tile to instructor  9928 . 
     Additionally, divided data selector  9924  selects a tile having visibility, based on the position of second virtual camera  9923  that is virtually set as second virtual camera  9923 , the focal point position of second virtual camera  9923 , and the position of the tile. Here, it is assumed that divided data selector  9924  has selected tiles with the tile numbers 1, 2, and 4. For example, divided data selector  9924  outputs the information indicating the selected tile to instructor  9928 . 
     Instructor  9928  outputs, to decoder  9925 , the information indicating an instruction for selecting and decoding the tiles with the tile numbers 1, 2, 3, and 4, based on the pieces of information that are output from divided data selectors  9922  and  9924 . 
     Based on the information that is output from instructor  9928 , decoder  9925  selects and decodes the tiles with the tile numbers 1, 2, 3, and 4. Additionally, decoder  9925  outputs each of the decoded tiles with the tile numbers 1, 2, and 3 to first presenter  9926 , and causes first presenter  9926  to present the information indicated by each of the tiles. In addition, decoder  9925  outputs each of the tiles with the tile numbers 1, 2, and 4 to second presenter  9927 , and causes second presenter  9927  to present the information indicated by each of the tiles. 
     Each of first presenter  9926  and second presenter  9927  is, for example, a display apparatus, and loads and displays the information on tiles that is output from the decoder. 
     Note that, even when the number of viewpoints (for example, cameras) is one, and the viewpoint is moved, the decoding method described in the present example may be employed in the decoding process performed by the three-dimensional data decoding device. For example, when a viewpoint is moved, that is, when the information indicating the position of the viewpoint is changed, the three-dimensional data decoding device selects a tile to be decoded, based on whether or not there is the above-described visibility, and decodes tiles that have not been decoded from selected tiles. 
       FIG. 177  is a diagram for describing the decoding process by the three-dimensional data decoding device according to Variation 1 of the present embodiment.  FIG. 178  is a diagram for describing a process of calculating angle information used for determination of visibility by the three-dimensional data decoding device according to Variation 1 of the present embodiment. 
     In the above, when the three-dimensional data decoding device calculates the inner product, the vector from the focal point position of a camera to the position of the camera, and the vector from the focal point position of the camera to a tile (for example, the center position of the tile) are used. 
     In the present variation, the three-dimensional data decoding device calculates, on the basis of the position of the camera, a vector to the focal point of the camera, and a vector to the tile, and calculates the angle by the inner product of the two vectors. That is, in the present variation, the three-dimensional data decoding device calculates the vector from the position of a camera to the focal point of the camera, and the vector from the position of the camera to a tile, and calculates the angle by the inner product of the two vectors. 
     Additionally, similar to the process performed by the above-described three-dimensional data decoding device according to the present embodiment, the three-dimensional data encoding device according to the present variation determines a threshold value based on the position of a camera, the position of a tile, the focal point position of the camera, the focal point distance of the camera, and the like. In addition, the three-dimensional data decoding device may determine whether or not a tile has visibility, based on whether or not an angle (more specifically, cos (φ)) obtained from the inner product is within a predetermined range, and may decode and present the tile, when it is determined that the tile has visibility. 
     Note that, as the process of determining the visibility of a tile, the three-dimensional data decoding device may select and perform one of the method of obtaining the inner product of the vectors on the basis of the focal point position, and the method of obtaining the inner product of the vectors on the basis of the position of the camera. 
     Additionally, in the above, although the example has been illustrated in which the three-dimensional data decoding device calculates the angle information (for example, cos (φ)), and determines the visibility of a tile based on the calculated angle information, the three-dimensional data decoding device may determine the resolution of a tile to decoded, based on the angle information. 
     For example, when the calculated angle is small, the three-dimensional data decoding device determines that a tile is located at a position close to the vector from the focal point position of the camera to the position of the camera, and sets a decoding resolution to be high. On the other hand, for example, when the calculated angle is large, the three-dimensional data decoding device determines that the tile is located at a position distant from the vector from the focal point position of the camera to the position of the camera, and sets the decoding resolution to be low. 
     Note that the resolution of a tile to be decoded may be determined based on the moving direction of the camera, and the vector between the position of the camera and the focal point position of the camera. 
     Note that the resolution may be set for each layer. 
     Additionally, the three-dimensional data decoding device may determine both the selection of a tile to be decoded, and the resolution of the tile in the case of decoding, based on the angle information. 
       FIG. 179  is a flowchart illustrating a process of determining the resolution based on the angle information by a three-dimensional data decoding device according to Variation 2 of the present embodiment. 
     Note that it is assumed that the three-dimensional data decoding device has calculated the above-described cos (φ), X, and Y before starting the flowchart of  FIG. 179 . 
     First, the three-dimensional data decoding device determines whether or not cos (φ) satisfies 1-X&lt;cos (φ)&lt;1 (S 9911 ). 
     When it is determined that cos (φ) satisfies 1-X&lt;cos (φ)&lt;1 (Yes in S 9911 ), the three-dimensional data decoding device decodes a tile at a high resolution (S 9912 ). 
     On the other hand, when it is determined that cos (φ) does not satisfy 1-X&lt;cos (φ)&lt;1 (No in S 9911 ), the three-dimensional data decoding device determines whether or not cos (φ) satisfies 1-Y&lt;cos (φ)&lt;1-X (S 9913 ). 
     When it is determined that cos (φ) satisfies 1-Y&lt;cos (φ)&lt;1-X (Yes in S 9913 ), the three-dimensional data decoding device decodes a tile at a medium resolution (S 9914 ). 
     On the other hand, when it is determined that cos (φ) does not satisfy 1-Y&lt;cos (φ)&lt;1-X (No in S 9913 ), the three-dimensional data decoding device decodes a tile at a low resolution (S 9915 ). 
     Note that the degree of resolution may be arbitrarily defined in advance, and is not particularly limited. For example, the three-dimensional data decoding device decodes all the three-dimensional points included in a tile in step S 9912 , decodes about 60 percent of the three-dimensional points included in the tile in step S 9914 , and decodes about twenty percent of the three-dimensional points included in the tile in step S 9915 . 
     As described above, the three-dimensional data decoding devices according to the present embodiment and the variations perform a process illustrated in  FIG. 180 . 
       FIG. 180  is a flowchart illustrating the decoding process by the three-dimensional data decoding devices according to the present embodiment and the variations. 
     First, the three-dimensional data decoding device obtains a tile including encoded three-dimensional points (S 9921 ). 
     Next, the three-dimensional data decoding device calculates an angle formed by a line segment connecting a predetermined position in the tile and a first base point and a line segment connecting the first base point and a second base point different from the first base point (S 9922 ). The three-dimensional data decoding device, for example, calculates the above-described cos (φ) as the angle. 
     Next, the three-dimensional data decoding device determines whether the angle calculated satisfies a predetermined condition (S 9923 ). For example, the three-dimensional data decoding device determines whether cos (φ) is within the above-described predetermined range. 
     When the angle calculated is determined to satisfy the predetermined condition (Yes in S 9923 ), the three-dimensional data decoding device decodes the encoded three-dimensional points included in the tile (S 9924 ). 
     On the other hand, when the angle calculated is determined not to satisfy the predetermined condition (No in S 9923 ), the three-dimensional data decoding device does not decode the encoded three-dimensional points included in the tile (S 9925 ). 
     For example, the three-dimensional data decoding device sequentially decodes encoded three-dimensional points in the order of the data included in a bitstream (encoded data) including three-dimensional points encoded by a three-dimensional data encoding device. Here, in the case where three-dimensional data represents a three-dimensional map, for example, when a user consults the map, the user often wants to see a map of a particular direction from the current position of the user. In such a case, if the three-dimensional data decoding device sequentially decodes the encoded three-dimensional points in the order of the data included in the bitstream, and sequentially displays images representing three-dimensional points on a display device or the like in the order of decoding, it may take a long time to display the part that the user wants to check on the display device. In addition, depending on the current position of the user, there may be three-dimensional points that need not be decoded. In view of this, in the three-dimensional data decoding method according to the present disclosure, the line of sight direction of a camera, a user, or the like, is assumed using the positional relationship between a tile and any two points such as a position of the camera, the user, or the like, and a focal point position of the camera, the user, or the like, and an encoded tile located in the line of sight direction is decoded. Accordingly, an encoded tile located in a region that is likely to be desired by the user can be decoded. Specifically, the three-dimensional data decoding method according to the present disclosure can appropriately select and decode a desired encoded tile (i.e., three-dimensional points) among a plurality of encoded tiles. 
     Furthermore, for example, the determining of whether the angle calculated satisfies the predetermined condition (S 9923 ) includes determining whether the angle calculated is less than a predetermined angle. The decoding of the encoded three-dimensional points included in the tile (S 9924  or S 9925 ) includes: decoding the encoded three-dimensional points included in the tile (S 9924 ) when the angle calculated is less than the predetermined angle (Yes in S 9923 ); and not decoding the encoded three-dimensional points included in the tile (S 9925 ) when the angle calculated is greater than or equal to the predetermined angle (No in S 9923 ). In other words, the aforementioned predetermined condition may be whether the angle calculated is less than a predetermined angle. 
     Accordingly, a tile located in the range determined by the user can be appropriately decoded according to the view angle of the camera, or the like, by appropriately setting the predetermined angle. 
     Furthermore, for example, the decoding of the encoded three-dimensional points included in the tile (S 9924 ) includes: determining a resolution of the encoded three-dimensional points based on the angle; and decoding the encoded three-dimensional points according to the resolution determined. 
     For example, there is a high possibility that the center portion of the user&#39;s field of view is more important for the user than the outer edge portion of the user&#39;s field of view. Therefore, for example, according to the calculated angle, tiles are decoded such that the resolution of a tile located in the center portion of the field of view of the camera, the user, or the like is increased, and the resolution of a tile located in the outer edge portion of the field of view of the camera, the user, or the like is decreased. Accordingly, the processing amount can be reduced by decreasing the resolution of a tile that is highly likely to be relatively unimportant for the user, while increasing the resolution of a tile that can be relatively important for the user. 
     Furthermore, for example, the first base point indicates a position of a camera, and the second base point indicates a focal point position of the camera. 
     Alternatively, for example, the second base point indicates a position of a camera, and the first base point indicates a focal point position of the camera. 
     Accordingly, for example, it is possible to decode a tile in a position determined by the position of a camera and the shooting direction of the camera in the case of actual shooting by the camera. For this reason, for example, it is possible to appropriately select and decode a tile for generating a three-dimensional image (three-dimensional points) according to an image obtained from the camera. 
     Furthermore, for example, the calculating of the angle (S 9922 ) includes calculating the angle by calculating an inner product of a vector from the first base point to the predetermined position in the tile and a vector from the first base point to the second base point. 
     Accordingly, the angle can be calculated using a simple process. 
     Furthermore, for example, the predetermined position in the tile is a position identified by smallest coordinate values in the tile in a coordinate space in which the tile is located, a center position of the tile, or a center of gravity of the tile. 
     Specifically, for the position of the tile, it is desirable to adopt a position that is included in the tile and can be easily calculated. 
     Furthermore, for example, the three-dimensional data decoding device includes a processor and memory, and, using the memory, the processor performs the above-described processes. The memory may store a control program for performing the above-described processes. Furthermore, the memory may store a method of calculating a new predicted value, information on the number of predetermined prediction modes, predetermined predicted values to be allocated to the prediction modes, etc. 
     A three-dimensional data encoding device, a three-dimensional data decoding device, and the like according to the embodiments of the present disclosure have been described above, but the present disclosure is not limited to these embodiments. 
     Note that each of the processors included in the three-dimensional data encoding device, the three-dimensional data decoding device, and the like according to the above embodiments is typically implemented as a large-scale integrated (LSI) circuit, which is an integrated circuit (IC). These may take the form of individual chips, or may be partially or entirely packaged into a single chip. 
     Such IC is not limited to an LSI, and thus may be implemented as a dedicated circuit or a general-purpose processor. Alternatively, a field programmable gate array (FPGA) that allows for programming after the manufacture of an LSI, or a reconfigurable processor that allows for reconfiguration of the connection and the setting of circuit cells inside an LSI may be employed. 
     Moreover, in the above embodiments, the structural components may be implemented as dedicated hardware or may be realized by executing a software program suited to such structural components. Alternatively, the structural components may be implemented by a program executor such as a CPU or a processor reading out and executing the software program recorded in a recording medium such as a hard disk or a semiconductor memory. 
     The present disclosure may also be implemented as a three-dimensional data encoding method, a three-dimensional data decoding method, or the like executed by the three-dimensional data encoding device, the three-dimensional data decoding device, and the like. 
     Also, the divisions of the functional blocks shown in the block diagrams are mere examples, and thus a plurality of functional blocks may be implemented as a single functional block, or a single functional block may be divided into a plurality of functional blocks, or one or more functions may be moved to another functional block. Also, the functions of a plurality of functional blocks having similar functions may be processed by single hardware or software in a parallelized or time-divided manner. 
     Also, the processing order of executing the steps shown in the flowcharts is a mere illustration for specifically describing the present disclosure, and thus may be an order other than the shown order. Also, one or more of the steps may be executed simultaneously (in parallel) with another step. 
     A three-dimensional data encoding device, a three-dimensional data decoding device, and the like according to one or more aspects have been described above based on the embodiments, but the present disclosure is not limited to these embodiments. The one or more aspects may thus include forms achieved by making various modifications to the above embodiments that can be conceived by those skilled in the art, as well forms achieved by combining structural components in different embodiments, without materially departing from the spirit of the present disclosure. 
     INDUSTRIAL APPLICABILITY 
     The present disclosure is applicable to a three-dimensional data encoding device and a three-dimensional data decoding device.