Patent Publication Number: US-2023154057-A1

Title: Three-dimensional data encoding method, three-dimensional data decoding method, three-dimensional data encoding device, 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/JP2021/028440 filed on Jul. 30, 2021, claiming the benefit of priority of U.S. Provisional Patent Application No. 63/059,521 filed on Jul. 31, 2020, the entire contents of which are hereby incorporated by reference. 
    
    
     BACKGROUND 
     1. Technical Field 
     The present disclosure relates to a three-dimensional data encoding method, a three-dimensional data decoding method, a three-dimensional data encoding device, 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 (see, for example, Patent Literature (PTL) 1 (International Publication WO 2014/020663)). 
     SUMMARY 
     There has been a demand for performing a decoding process effectively in encoding and decoding three-dimensional data. 
     The present disclosure has an object to provide a three-dimensional data encoding method, a three-dimensional data decoding method, a three-dimensional data encoding device, or a three-dimensional data decoding device that is capable of performing a decoding process effectively. 
     A three-dimensional data encoding method according to one aspect of the present disclosure including: obtaining three-dimensional points; generating a data unit including one or more prediction trees, using the three-dimensional points; and generating a bitstream including the data unit and control information, wherein the control information includes tree information about the one or more prediction trees, and the tree information includes number information indicating a total number of prediction trees included in the data unit. 
     A three-dimensional data decoding method according to one aspect of the present disclosure including: obtaining a bitstream including control information and a data unit that includes one or more prediction trees; and decoding one of the one or more prediction trees included in the data unit, using the control information, to calculate three-dimensional points constituting the one of the one or more prediction trees, wherein the control information includes tree information about the one or more prediction trees, and the tree information includes number information indicating a total number of prediction trees included in the data unit. 
     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. 
     The present disclosure provides a three-dimensional data encoding method, a three-dimensional data decoding method, a three-dimensional data encoding device, or a three-dimensional data decoding device that is capable of performing a decoding process effectively. 
    
    
     
       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 diagram illustrating an example of a transmitting order of NAL units according to Embodiment 2; 
         FIG.  29    is a flowchart of processing performed by a three-dimensional data encoding device according to Embodiment 2; 
         FIG.  30    is a flowchart of processing performed by a three-dimensional data decoding device according to Embodiment 2; 
         FIG.  31    is a flowchart of multiplexing processing according to Embodiment 2; 
         FIG.  32    is a flowchart of demultiplexing processing according to Embodiment 2; 
         FIG.  33    is a block diagram of a first encoder according to Embodiment 3; 
         FIG.  34    is a block diagram of a first decoder according to Embodiment 3; 
         FIG.  35    is a block diagram of a divider according to Embodiment 3; 
         FIG.  36    is a diagram illustrating an example of dividing slices and tiles according to Embodiment 3; 
         FIG.  37    is a diagram illustrating dividing pattern examples of slices and tiles according to Embodiment 3; 
         FIG.  38    is a diagram illustrating an example of dependency according to Embodiment 3; 
         FIG.  39    is a diagram illustrating a data decoding order according to Embodiment 3; 
         FIG.  40    is a flowchart of encoding processing according to Embodiment 3; 
         FIG.  41    is a block diagram of a combiner according to Embodiment 3; 
         FIG.  42    is a diagram illustrating a structure example of encoded data and a NAL unit according to Embodiment 3; 
         FIG.  43    is a flowchart of encoding processing according to Embodiment 3; 
         FIG.  44    is a flowchart of decoding processing according to Embodiment 3; 
         FIG.  45    is a flowchart of encoding processing according to Embodiment 3; and 
         FIG.  46    is a flowchart of decoding processing according to Embodiment 3; 
         FIG.  47    is a diagram illustrating an example of a prediction tree used in a three-dimensional data encoding method according to Embodiment 4; 
         FIG.  48    is a flowchart illustrating an example of a three-dimensional data encoding method according to Embodiment 4; 
         FIG.  49    is a flowchart illustrating an example of a three-dimensional data decoding method according to Embodiment 4; 
         FIG.  50    is a diagram for describing a method of generating a prediction tree according to Embodiment 4; 
         FIG.  51    is a diagram for describing a first example of prediction modes according to Embodiment 4; 
         FIG.  52    is a diagram illustrating a second example of a table that indicates a predicted value calculated in each prediction mode according to Embodiment 4; 
         FIG.  53    is a diagram illustrating a specific example of the second example of the table that indicates a predicted value calculated in each prediction mode according to Embodiment 4; 
         FIG.  54    is a diagram illustrating a third example of the table that indicates a predicted value calculated in each prediction mode according to Embodiment 4; 
         FIG.  55    is a diagram illustrating a fourth example of the table that indicates a predicted value calculated in each prediction mode according to Embodiment 4; 
         FIG.  56    is a diagram illustrating a fifth example of the table that indicates a predicted value calculated in each prediction mode according to Embodiment 4; 
         FIG.  57    is a diagram illustrating a sixth example of the table that indicates a predicted value calculated in each prediction mode according to Embodiment 4; 
         FIG.  58    is a diagram illustrating a seventh example of the table that indicates a predicted value calculated in each prediction mode according to Embodiment 4; 
         FIG.  59    is a diagram illustrating a first example of a binarization table in a case where a prediction mode value is binarized and encoded according to Embodiment 4; 
         FIG.  60    is a diagram illustrating a second example of the binarization table in the case where a prediction mode value is binarized and encoded according to Embodiment 4; 
         FIG.  61    is a diagram illustrating a third example of the binarization table in the case where a prediction mode value is binarized and encoded according to Embodiment 4; 
         FIG.  62    is a diagram for describing an example of encoding of binary data in a binarization table in the case where a prediction mode value is binarized and encoded according to Embodiment 4; 
         FIG.  63    is a flowchart illustrating an example of encoding of a prediction mode value according to Embodiment 4; 
         FIG.  64    is a flowchart illustrating an example of decoding of a prediction mode value according to Embodiment 4; 
         FIG.  65    is a diagram illustrating another example of the table that indicates a predicted value calculated in each prediction mode according to Embodiment 4; 
         FIG.  66    is a diagram for describing an example of encoding of binary data in a binarization table in the case where a prediction mode value is binarized and encoded according to Embodiment 4; 
         FIG.  67    is a flowchart illustrating another example of encoding of a prediction mode value according to Embodiment 4; 
         FIG.  68    is a flowchart illustrating another example of decoding of a prediction mode value according to Embodiment 4; 
         FIG.  69    is a flowchart illustrating an example of a process of determining whether or not to fix the prediction mode value according to condition A in encoding according to Embodiment 4; 
         FIG.  70    is a flowchart illustrating an example of a process of determining whether to set the prediction mode value at a fixed value or decode the prediction mode value according to condition A in decoding according to Embodiment 4; 
         FIG.  71    is a diagram illustrating an example of a syntax of a header of geometry information according to Embodiment 4; 
         FIG.  72    is a diagram illustrating an example of a syntax of geometry information according to Embodiment 4; 
         FIG.  73    is a diagram illustrating another example of the syntax of geometry information according to Embodiment 4; 
         FIG.  74    is a diagram for describing a method of encoding and decoding point cloud data with low delay according to Embodiment 5; 
         FIG.  75    is a diagram for describing an example of a method of generating a prediction tree according to Embodiment 5; 
         FIG.  76    is a diagram for describing a three-dimensional polar coordinate system according to Embodiment 5; 
         FIG.  77    is a diagram for describing a method of projecting a three-dimensional point obtained by a measuring instrument onto a two-dimensional image according to Embodiment 5; 
         FIG.  78    is a diagram illustrating an example of sensor parameters and a two-dimensional image of a three-dimensional point cloud according to Embodiment 5; 
         FIG.  79    is a diagram illustrating an example of a point cloud obtained by LiDAR according to Embodiment 5; 
         FIG.  80    is a diagram for describing an example of a method of dividing a point cloud according to Embodiment 5; 
         FIG.  81    is a diagram illustrating an example of division of point clouds into groups according to Embodiment 5; 
         FIG.  82    is a diagram for describing an example process in the case where prediction trees are combined or divided according to Embodiment 5; 
         FIG.  83    is a diagram in which point clouds are grouped and surrounded by a line on a prediction tree basis according to Embodiment 5; 
         FIG.  84    is a flowchart illustrating an example of a method of encoding a prediction tree according to Embodiment 5; 
         FIG.  85    is a flowchart illustrating an example of a method of decoding a prediction tree using signaled geometry information according to Embodiment 5; 
         FIG.  86    is a diagram illustrating an example of a syntax of tree information according to Embodiment 5; 
         FIG.  87    is a diagram illustrating an example of a bounding box according to Embodiment 5; 
         FIG.  88    shows an example of a data structure of encoded data according to Embodiment 5; 
         FIG.  89    is a flowchart illustrating an example of a process of storing into a system format according to Embodiment 5; 
         FIG.  90    is a diagram illustrating an example of a partial decoding list according to Embodiment 5; 
         FIG.  91    is a flowchart illustrating an example of a process of partially extracting a prediction tree from a system format according to Embodiment 5; 
         FIG.  92    is a flowchart illustrating a three-dimensional data encoding process according to Embodiment 5; 
         FIG.  93    is a flowchart illustrating a three-dimensional data decoding process according to Embodiment 5; 
         FIG.  94    is a block diagram of a three-dimensional data creation device according to Embodiment 6; 
         FIG.  95    is a flowchart of a three-dimensional data creation method according to Embodiment 6; 
         FIG.  96    is a diagram showing a structure of a system according to Embodiment 6; 
         FIG.  97    is a block diagram of a client device according to Embodiment 6; 
         FIG.  98    is a block diagram of a server according to Embodiment 6; 
         FIG.  99    is a flowchart of a three-dimensional data creation process performed by the client device according to Embodiment 6; 
         FIG.  100    is a flowchart of a sensor information transmission process performed by the client device according to Embodiment 6; 
         FIG.  101    is a flowchart of a three-dimensional data creation process performed by the server according to Embodiment 6; 
         FIG.  102    is a flowchart of a three-dimensional map transmission process performed by the server according to Embodiment 6; 
         FIG.  103    is a diagram showing a structure of a variation of the system according to Embodiment 6; 
         FIG.  104    is a diagram showing a structure of the server and client devices according to Embodiment 6; 
         FIG.  105    is a diagram illustrating a configuration of a server and a client device according to Embodiment 6; 
         FIG.  106    is a flowchart of a process performed by the client device according to Embodiment 6; 
         FIG.  107    is a diagram illustrating a configuration of a sensor information collection system according to Embodiment 6; 
         FIG.  108    is a diagram illustrating an example of a system according to Embodiment 6; 
         FIG.  109    is a diagram illustrating a variation of the system according to Embodiment 6; 
         FIG.  110    is a flowchart illustrating an example of an application process according to Embodiment 6; 
         FIG.  111    is a diagram illustrating the sensor range of various sensors according to Embodiment 6; 
         FIG.  112    is a diagram illustrating a configuration example of an automated driving system according to Embodiment 6; 
         FIG.  113    is a diagram illustrating a configuration example of a bitstream according to Embodiment 6; 
         FIG.  114    is a flowchart of a point cloud selection process according to Embodiment 6; 
         FIG.  115    is a diagram illustrating a screen example for point cloud selection process according to Embodiment 6; 
         FIG.  116    is a diagram illustrating a screen example of the point cloud selection process according to Embodiment 6; and 
         FIG.  117    is a diagram illustrating a screen example of the point cloud selection process according to Embodiment 6. 
     
    
    
     DETAILED DESCRIPTION OF THE EMBODIMENTS 
     A three-dimensional data encoding method according to one aspect of the present disclosure includes: obtaining three-dimensional points; generating a data unit including one or more prediction trees, using the three-dimensional points; and generating a bitstream including the data unit and control information. The control information includes tree information about the one or more prediction trees, and the tree information includes number information indicating a total number of prediction trees included in the data unit. 
     According to this method, since the number information indicating the number of prediction trees included in a data unit is indicated, the three-dimensional data decoding device can effectively perform the decoding process for the data unit using the number information. 
     For example, the tree information may further include maximum number information indicating a maximum value of a total number of three-dimensional points that a single prediction tree can include. 
     According to this method, since the maximum number information is indicated, the three-dimensional data decoding device can effectively perform the decoding process for the data unit using the maximum number information. 
     For example, the tree information may further include one or more identification information items each identifying a corresponding one of the one or more prediction trees. 
     According to this method, since one or more prediction trees can be easily identified, the three-dimensional data decoding device can easily select a desired prediction tree using the identification information. 
     For example, the tree information may further include one or more position information items each indicating a position of a corresponding one of the one or more prediction trees. 
     According to this method, since position information of one or more prediction trees can be easily identified, the three-dimensional data decoding device can easily select a desired prediction tree using the position information. 
     For example, each of the one or more position information items may include position coordinates of a root node included in the corresponding one of the one or more prediction trees. 
     For example, each of the one or more position information items may include an origin and a size of a bounding box surrounding a three-dimensional point cloud constituting the corresponding one of the one or more prediction trees. 
     For example, the tree information may further include one or more data position information items each indicating a position of a corresponding one of the one or more prediction trees, the position being a position from a top of the data unit in the data unit. 
     According to this method, since the data positions of one or more prediction trees can be easily identified, the three-dimensional data decoding device can easily extract data of a desired prediction tree using the data position information. 
     For example, the control information may be a footer of the data unit. 
     A three-dimensional data decoding method according to one aspect of the present disclosure includes: obtaining a bitstream including control information and a data unit that includes one or more prediction trees; and decoding one of the one or more prediction trees included in the data unit, using the control information, to calculate three-dimensional points constituting the one of the one or more prediction trees. The control information includes tree information about the one or more prediction trees, and the tree information includes number information indicating a total number of prediction trees included in the data unit. 
     According to this method, since the number information indicating the number of prediction trees included in a data unit is indicated, the three-dimensional data decoding device can effectively perform the decoding process for the data unit using the number information. 
     For example, the tree information may further include maximum number information indicating a maximum value of a total number of three-dimensional points that a single prediction tree can include. 
     According to this method, since the maximum number information is indicated, the three-dimensional data decoding device can effectively perform the decoding process for the data unit using the maximum number information. 
     For example, the tree information may further include one or more identification information items each identifying a corresponding one of the one or more prediction trees. 
     According to this method, since one or more prediction trees can be easily identified, the three-dimensional data decoding device can easily select a desired prediction tree using the identification information. 
     For example, the tree information may further include one or more position information items each indicating a position of a corresponding one of the one or more prediction trees. 
     According to this method, since position information of one or more prediction trees can be easily identified, the three-dimensional data decoding device can easily select a desired prediction tree using the position information. 
     For example, each of the one or more position information items may include position coordinates of a root node included in the corresponding one of the one or more prediction trees. 
     For example, each of the one or more position information items may include an origin and a size of a bounding box surrounding a three-dimensional point cloud constituting the corresponding one of the one or more prediction trees. 
     For example, the tree information may further include one or more data position information items each indicating a position of a corresponding one of the one or more prediction trees, the position being a position from a top of the data unit in the data unit. 
     According to this method, since the data positions of one or more prediction trees can be easily identified, the three-dimensional data decoding device can easily extract data of a desired prediction tree using the data position information. 
     For example, the control information may be a footer of the data unit. 
     A three-dimensional data encoding device according to one aspect of the present disclosure includes a processor and memory. Using the memory, the processor obtains three-dimensional points; generating a data unit including one or more prediction trees, using the three-dimensional points; and generates a bitstream including the data unit and control information. The control information includes tree information about the one or more prediction trees, and the tree information includes number information indicating a total number of prediction trees included in the data unit. 
     According to this method, since the number information indicating the number of prediction trees included in a data unit is indicated, the three-dimensional data decoding device can effectively perform the decoding process for the data unit using the number information. 
     A three-dimensional data decoding device according to one aspect of the present disclosure includes a processor and memory. Using the memory, the processor obtains a bitstream including control information and a data unit that includes one or more prediction trees; and decodes one of the one or more prediction trees included in the data unit, using the control information, to calculate three-dimensional points constituting the one of the one or more prediction trees. The control information includes tree information about the one or more prediction trees, and the tree information includes number information indicating a total number of prediction trees included in the data unit. 
     According to this method, since the number information indicating the number of prediction trees included in a data unit is indicated, the three-dimensional data decoding device can effectively perform the decoding process for the data unit using the number information. 
     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 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 VXL1, VXL2, and VXL3 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 A0 to A7). Nodes A0 to A7 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 (RANT). Specifically, transformed-attribute-information decoder A 112  decodes each attribute value by applying inverse RANT 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 disclosure 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 pcc_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 pcc_codec_type is codec 1 (Coded1: first encoding method), values κ 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 pcc_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. 
     Next, an order of transmission of data will be described. In the following, restrictions on the order of transmission of NAL units will be described. 
     Multiplexer  4802  transmits NAL units on a GOF basis or on an AU basis. Multiplexer  4802  arranges the GOF header at the top of a GOF, and arranges the AU header at the top of an AU. 
     In order to allow the decoding device to decode the next AU and the following AUs even when data is lost because of a packet loss or the like, multiplexer  4802  may arrange a sequence parameter set (SPS) in each AU. 
     When there is a dependence relationship for decoding between encoded data, the decoding device decodes the data of the entity to be referred to and then decodes the data of the referring entity. In order to allow the decoding device to perform decoding in the order of reception without rearranging the data, multiplexer  4802  first transmits the data of the entity to be referred to. 
       FIG.  28    is a diagram showing examples of the order of transmission of NAL units.  FIG.  28    shows three examples, that is, geometry information-first order, parameter-first order, and data-integrated order. 
     The geometry information-first order of transmission is an example in which information relating to geometry information is transmitted together, and information relating to attribute information is transmitted together. In the case of this order of transmission, the transmission of the information relating to the geometry information ends earlier than the transmission of the information relating to the attribute information. 
     For example, according to this order of transmission is used, when the decoding device does not decode attribute information, the decoding device may be able to have an idle time since the decoding device can omit decoding of attribute information. When the decoding device is required to decode geometry information early, the decoding device may be able to decode geometry information earlier since the decoding device obtains encoded data of the geometry information earlier. 
     Note that, although in  FIG.  28    the attribute X SPS and the attribute Y SPS are integrated and shown as the attribute SPS, the attribute X SPS and the attribute Y SPS may be separately arranged. 
     In the parameter set-first order of transmission, a parameter set is first transmitted, and data is then transmitted. 
     As described above, as far as the restrictions on the order of transmission of NAL units are met, multiplexer  4802  can transmit NAL units in any order. For example, order identification information may be defined, and multiplexer  4802  may have a function of transmitting NAL units in a plurality of orders. For example, the order identification information for NAL units is stored in the stream PS. 
     The three-dimensional data decoding device may perform decoding based on the order identification information. The three-dimensional data decoding device may indicate a desired order of transmission to the three-dimensional data encoding device, and the three-dimensional data encoding device (multiplexer  4802 ) may control the order of transmission according to the indicated order of transmission. 
     Note that multiplexer  4802  can generate encoded data having a plurality of functions merged to each other as in the case of the data-integrated order of transmission, as far as the restrictions on the order of transmission are met. For example, as shown in  FIG.  28   , the GOF header and the AU header may be integrated, or AXPS and AYPS may be integrated. In such a case, an identifier that indicates data having a plurality of functions is defined in pcc_nal_unit_type. 
     In the following, variations of this embodiment will be described. There are levels of PSs, such as a frame-level PS, a sequence-level PS, and a PCC sequence-level PS. Provided that the PCC sequence level is a higher level, and the frame level is a lower level, parameters can be stored in the manner described below. 
     The value of a default PS is indicated in a PS at a higher level. If the value of a PS at a lower level differs from the value of the PS at a higher level, the value of the PS is indicated in the PS at the lower level. Alternatively, the value of the PS is not described in the PS at the higher level but is described in the PS at the lower level. Alternatively, information indicating whether the value of the PS is indicated in the PS at the lower level, at the higher level, or at both the levels is indicated in both or one of the PS at the lower level and the PS at the higher level. Alternatively, the PS at the lower level may be merged with the PS at the higher level. If the PS at the lower level and the PS at the higher level overlap with each other, multiplexer  4802  may omit transmission of one of the PSs. 
     Note that encoder  4801  or multiplexer  4802  may divide data into slices or tiles and transmit each of the divided slices or tiles as divided data. The divided data includes information for identifying the divided data, and a parameter used for decoding of the divided data is included in the parameter set. In this case, an identifier that indicates that the data is data relating to a tile or slice or data storing a parameter is defined in pcc_nal_unit_type. 
     In the following, a process relating to order identification information will be described.  FIG.  29    is a flowchart showing a process performed by the three-dimensional data encoding device (encoder  4801  and multiplexer  4802 ) that involves the order of transmission of NAL units. 
     First, the three-dimensional data encoding device determines the order of transmission of NAL units (geometry information-first or parameter set-first) (S 4801 ). For example, the three-dimensional data encoding device determines the order of transmission based on a specification from a user or an external device (the three-dimensional data decoding device, for example). 
     If the determined order of transmission is geometry information-first (if “geometry information-first” in S 4802 ), the three-dimensional data encoding device sets the order identification information included in the stream PS to geometry information-first (S 4803 ). That is, in this case, the order identification information indicates that the NAL units are transmitted in the geometry information-first order. The three-dimensional data encoding device then transmits the NAL units in the geometry information-first order (S 4804 ). 
     On the other hand, if the determined order of transmission is parameter set-first (if “parameter set-first” in S 4802 ), the three-dimensional data encoding device sets the order identification information included in the stream PS to parameter set-first (S 4805 ). That is, in this case, the order identification information indicates that the NAL units are transmitted in the parameter set-first order. The three-dimensional data encoding device then transmits the NAL units in the parameter set-first order (S 4806 ). 
       FIG.  30    is a flowchart showing a process performed by the three-dimensional data decoding device that involves the order of transmission of NAL units. First, the three-dimensional data decoding device analyzes the order identification information included in the stream PS (S 4811 ). 
     If the order of transmission indicated by the order identification information is geometry information-first (if “geometry information-first” in S 4812 ), the three-dimensional data decoding device decodes the NAL units based on the determination that the order of transmission of the NAL units is geometry information-first (S 4813 ). 
     On the other hand, if the order of transmission indicated by the order identification information is parameter set-first (if “parameter set-first” in S 4812 ), the three-dimensional data decoding device decodes the NAL units based on the determination that the order of transmission of the NAL units is parameter set-first (S 4814 ). 
     For example, if the three-dimensional data decoding device does not decode attribute information, in step S 4813 , the three-dimensional data decoding device does not obtain the entire NAL units but can obtain a part of a NAL unit relating to the geometry information and decode the obtained NAL unit to obtain the geometry information. 
     Next, a process relating to generation of an AU and a GOF will be described.  FIG.  31    is a flowchart showing a process performed by the three-dimensional data encoding device (multiplexer  4802 ) that relates to generation of an AU and a GOF in multiplexing of NAL units. 
     First, the three-dimensional data encoding device determines the type of the encoded data (S 4821 ). Specifically, the three-dimensional data encoding device determines whether the encoded data to be processed is AU-first data, GOF-first data, or other data. 
     If the encoded data is GOF-first data (if “GOF-first” in S 4822 ), the three-dimensional data encoding device generates NAL units by arranging a GOF header and an AU header at the top of the encoded data belonging to the GOF (S 4823 ). 
     If the encoded data is AU-first data (if “AU-first” in S 4822 ), the three-dimensional data encoding device generates NAL units by arranging an AU header at the top of the encoded data belonging to the AU (S 4824 ). 
     If the encoded data is neither GOF-first data nor AU-first data (if “other than GOF-first and AU-first” in S 4822 ), the three-dimensional data encoding device generates NAL units by arranging the encoded data to follow the AU header of the AU to which the encoded data belongs (S 4825 ). 
     Next, a process relating to access to an AU and a GOF will be described.  FIG.  32    is a flowchart showing a process performed by the three-dimensional data decoding device that involves accessing to an AU and a GOF in demultiplexing of a NAL unit. 
     First, the three-dimensional data decoding device determines the type of the encoded data included in the NAL unit by analyzing nal_unit_type in the NAL unit (S 4831 ). Specifically, the three-dimensional data decoding device determines whether the encoded data included in the NAL unit is AU-first data, GOF-first data, or other data. 
     If the encoded data included in the NAL unit is GOF-first data (if “GOF-first” in S 4832 ), the three-dimensional data decoding device determines that the NAL unit is a start position of random access, accesses the NAL unit, and starts the decoding process (S 4833 ). 
     If the encoded data included in the NAL unit is AU-first data (if “AU-first” in S 4832 ), the three-dimensional data decoding device determines that the NAL unit is AU-first, accesses the data included in the NAL unit, and decodes the AU (S 4834 ). 
     If the encoded data included in the NAL unit is neither GOF-first data nor AU-first data (if “other than GOF-first and AU-first” in S 4832 ), the three-dimensional data decoding device does not process the NAL unit. 
     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.  33    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.  33    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.  34    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.  34    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.  35    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.  36    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.  36    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.  37    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 decoding device. Alternatively, the three-dimensional 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.  38    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. G s1  indicates the geometry information of slice number 1, and G s2  indicates the geometry information of slice number 2. G s1t1  indicates the geometry information of slice number 1 and tile number 1, G s1t2  indicates the geometry information of slice number 1 and tile number 2, G s2t1  indicates the geometry information of slice number 2 and tile number 1, and G s2t2  indicates the geometry information of slice number 2 and tile number 2. Similarly, A s1  indicates the attribute information of slice number 1, and A s1  indicates the attribute information of slice number 2. A s1t1  indicates the attribute information of slice number 1 and tile number 1, A s1t2  indicates the attribute information of slice number 1 and tile number 2, A s2t1  indicates the attribute information of slice number 2 and tile number 1, and A s2t2  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. D s1t1  indicates the dependency information of attribute information A s1t1 , and D s2t1  indicates the dependency information of attribute information A s2t1 . 
     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.  39    is a diagram illustrating an example of the data decoding order. In the example of  FIG.  39   , 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.  40    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.  39   , 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.  41    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.  42    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.  43    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.  44    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. 
     As described above, the three-dimensional data encoding device according to the present embodiment performs the processing illustrated in  FIG.  45   . First, the three-dimensional data encoding device performs dividing into a plurality of divided data (for example, tiles) included in a plurality of subspaces (for example, slices) divided from a current space in which a plurality of three-dimensional points are included, each of the plurality of divided data including one or more three-dimensional points. Here, the divided data is one or more data aggregates that are included in a subspace, and includes one or more three-dimensional points. Additionally, the divided data is also spaces, and may include a space that does not include a three-dimensional point. Furthermore, a plurality of divided data may be included in one subspace, or one divided data may be included in one subspace. Note that a plurality of subspaces may be set to a current space, or one subspace may be set to the current space. 
     Next, the three-dimensional data encoding device generates a plurality of encoded data corresponding to a plurality of divided data, respectively, by encoding each of the plurality of divided data (S 4931 ). The three-dimensional data encoding device generates a bit stream including the plurality of encoded data and a plurality of control information (for example, the headers illustrated in  FIG.  42   ) (referred to also as signaling information) for the plurality of respective encoded data (S 4932 ). In each of the plurality of control information, a first identifier (for example, slice_idx) indicating the subspace corresponding to the encoding data corresponding to the control information, and a second identifier (for example, tile_idx) indicating the divided data corresponding to the encoding data corresponding to the control information are stored. 
     According to this, the three-dimensional data decoding device that decodes a bit stream generated by the three-dimensional data encoding device can easily restore a current space by combining the data of a plurality of divided data by using the first identifier and the second identifier. Therefore, the processing amount in the three-dimensional data decoding device can be reduced. 
     For example, in the encoding, the three-dimensional data encoding device encodes the geometry information and attribute information of a three-dimensional point(s) included in each of the plurality of divided data. Each of a plurality of encoded data includes the encoded data of geometry information, and the encoded data of attribute information. Each of a plurality of control information includes the control information of the encoded data of geometry information, and the control information of the encoded data of attribute information. The first identifier and the second identifier are stored in the control information of the encoded data of geometry information. 
     For example, in a bit stream, each of a plurality of control information is located ahead of the encoded data corresponding to the control information. 
     Additionally, a current space in which a plurality of three-dimensional points are included is set as one or more subspaces, one or more divided data including one or more three-dimensional points are included in the subspaces, the three-dimensional data encoding device generates a plurality of encoded data corresponding to the plurality of respective divided data by encoding each of the divided data, and generates a bit stream including the plurality of encoded data and a plurality of control information for the plurality of respective encoded data, and the first identifier indicating the subspace corresponding to the encoded data corresponding to the control information, and the second identifier indicating the divided data corresponding to the encoded data corresponding to the control information may be stored in each of the plurality of control information. 
     For example, the three-dimensional data encoding device includes a processor and a memory, and the processor performs the above-described processing by using the memory. 
     Additionally, the three-dimensional data decoding device according to the present embodiment performs the processing illustrated in  FIG.  46   . First, the three-dimensional data decoding device obtains the first identifier (for example, slice_idx) and the second identifier (for example, tile_idx) from a bitstream, the bitstream including a plurality of encoded data and a plurality of control information (for example, the headers illustrated in  FIG.  42   ) corresponding to the plurality of respective encoded data, the first identifier and the second identifier being included in the plurality of control information, the plurality of encoded data being generated by encoding each of a plurality of divided data (for example, tiles), the plurality of divided data being included in a plurality of subspaces (for example, slices) obtained by dividing a current space including a plurality of three-dimensional points, the plurality of divided data each including one or more three-dimensional points, the first identifier indicating a subspace corresponding to the encoded data corresponding to the control information, the second identifier indicating the divided data corresponding to the encoded data corresponding to the control information (S 4941 ). Next, the three-dimensional data decoding device restores a plurality of divided data by decoding the plurality of encoded data (S 4942 ). Next, the three-dimensional data decoding device restores the current space by combining the plurality of divided data by using the first identifier and the second identifier (S 4943 ). For example, the three-dimensional data decoding device restores the plurality of subspaces by combining the plurality of divided data by using the second identifier, and restores the current space (the plurality of three-dimensional points) by combining the plurality of subspaces by using the first identifier. Note that the three-dimensional data decoding device may obtain the encoded data of a desired subspace or divided data from a bit stream by using at least one of the first identifier and the second identifier, and may selectively decode or preferentially decode the obtained encoded data. 
     According to this, the three-dimensional data decoding device can easily restore the current space by combining the data of the plurality of divided data by using the first identifier and the second identifier. Therefore, the processing amount in the three-dimensional data decoding device can be reduced. 
     For example, each of the plurality of encoded data is generated by encoding the geometry information and attribute information of the three-dimensional point(s) included in the corresponding divided data, and includes the encoded data of the geometry information, and the encoded data of the attribute information. Each of the plurality of control information includes the control information of the encoded data of the geometry information, and the control information of the encoded data of the attribute information. The first identifier and the second identifier are stored in the control information of the encoded data of the geometry information. 
     For example, in a bit stream, the control information is located ahead of the corresponding encoded data. 
     For example, the three-dimensional data decoding device includes a processor and a memory, and the processor performs the processes described above using the memory. 
     Embodiment 4 
     According to a three-dimensional data encoding device according to Embodiment 4, geometry information of a plurality of three-dimensional points is encoded using a prediction tree generated based on the geometry information. 
       FIG.  47    is a diagram illustrating an example of a prediction tree used in the three-dimensional data encoding method according to Embodiment 4.  FIG.  48    is a flowchart illustrating an example of the three-dimensional data encoding method according to Embodiment 4.  FIG.  49    is a flowchart illustrating an example of a three-dimensional data decoding method according to Embodiment 4. 
     As shown in  FIG.  47    and  FIG.  48   , in the three-dimensional data encoding method, a prediction tree is generated using a plurality of three-dimensional points, and node information included in each node in the prediction tree is then encoded. In this way, a bitstream including encoded node information is obtained. Each item of node information is information concerning one node of the prediction tree, for example. Each item of node information includes geometry information of one node, an index of the one node, the number of child nodes of the one node, a prediction mode used for encoding the geometry information of the one node, and a prediction residual. 
     As shown in  FIG.  47    and  FIG.  49   , in the three-dimensional data decoding device, each item of encoded node information included in the bitstream is decoded, and then the geometry information is decoded while generating the prediction tree. 
     Next, a method of generating a prediction tree will be described with reference to  FIG.  50   . 
       FIG.  50    is a diagram for describing a method of generating a prediction tree according to Embodiment 4. 
     In the method of generating a prediction tree, as shown in Part (a) of  FIG.  50   , the three-dimensional data encoding device first adds point 0 as an initial point of the prediction tree. Geometry information of point 0 is represented by coordinates including three elements (x0, y0, z0). The geometry information of point 0 may be represented by coordinates of the three-dimensional Cartesian coordinate system or coordinates of the polar coordinate system. 
     child_count is incremented by 1 each time one child node is added to the node for which the child_count is set. Once the generation of the prediction tree is completed, child_count of each node indicates the number of child nodes of the node, and is added to the bitstream. pred_mode indicates the prediction mode for predicting values of the geometry information of each node. Details of the prediction mode will be described later. 
     As shown in Part (b) of  FIG.  50   , the three-dimensional data encoding device then adds point 1 to the prediction tree. In this step, the three-dimensional data encoding device may search the point cloud already added to the prediction tree for a point nearest to point 1 and add point 1 as a child node of the nearest point. Geometry information of point 1 is represented by coordinates including three elements (x1, y1, z1). The geometry information of point 1 may be represented by coordinates of the three-dimensional Cartesian coordinate system or coordinates of the polar coordinate system. In the case of  FIG.  50   , point 0 is the nearest point of point 1, and point 1 is added as a child node of point 0. The three-dimensional data encoding device then increments by 1 the value indicated by child_count of point 0. 
     Note that the predicted value of the geometry information of each node may be calculated when adding the node to the prediction tree. For example, in the case of Part (b) of  FIG.  50   , the three-dimensional data encoding device may add point 1 as a child node of point 0 and calculate the geometry information of point 0 as a predicted value. In that case, pred_mode=1 may be set. pred_mode is prediction mode information (prediction mode value) indicating a prediction mode. After calculating the predicted value, the three-dimensional data encoding device may calculate residual_value (prediction residual) of point 1. Here, residual_value is a difference value obtained by subtracting the predicted value calculated in the prediction mode indicated by pred_mode from the geometry information of the node. As described, according to the three-dimensional data encoding method, the difference value with respect to the predicted value, rather than the geometry information itself, is encoded, so that the encoding efficiency can be improved. 
     As shown in Part (c) of  FIG.  50   , the three-dimensional data encoding device then adds point 2 to the prediction tree. In this step, the three-dimensional data encoding device may search the point cloud already added to the prediction tree for a point nearest to point 2 and add point 2 as a child node of the nearest point. Geometry information of point 2 is represented by coordinates including three elements (x2, y2, z2). The geometry information of point 2 may be represented by coordinates of the three-dimensional Cartesian coordinate system or coordinates of the polar coordinate system. In the case of  FIG.  50   , point 1 is the nearest point of point 2, and point 2 is added as a child node of point 1. The three-dimensional data encoding device then increments by 1 the value indicated by child_count of point 1. 
     As shown in Part (d) of  FIG.  50   , the three-dimensional data encoding device then adds point 3 to the prediction tree. In this step, the three-dimensional data encoding device may search the point cloud already added to the prediction tree for a point nearest to point 3 and add point 3 as a child node of the nearest point. Geometry information of point 3 is represented by coordinates including three elements (x3, y3, z3). The geometry information of point 3 may be represented by coordinates of the three-dimensional Cartesian coordinate system or coordinates of the polar coordinate system. In the case of  FIG.  50   , point 0 is the nearest point of point 3, and point 3 is added as a child node of point 0. The three-dimensional data encoding device then increments by 1 the value indicated by child_count of point 0. 
     In this way, the three-dimensional data encoding device adds all points to the prediction tree and ends the generation of the prediction tree. When the generation of the prediction tree ends, any node having child_count=0 is a leaf of the prediction tree. After the generation of the prediction tree ends, the three-dimensional data encoding device encodes child_count, pred_mode, and residual_value of each node selected in the depth-first order from the root node. Selecting a node in the depth-first order means that the three-dimensional data encoding device selects, as a node subsequent to a node selected, a child node that has not been selected yet of the one or more child nodes of the selected node. When the selected node has no child node, the three-dimensional data encoding device selects a child node that has not been selected yet of the parent node of the selected node. 
     Note that the order of encoding is not limited to the depth-first order, but may be the width-first order, for example. When selecting a node in the width-first order, the three-dimensional data encoding device selects, as a node subsequent to a node selected, a node that has not been selected yet of the one or more nodes at the same depth (layer) as the selected node. When there is no node at the same depth as the selected node, the three-dimensional data encoding device selects a node that has not been selected yet of the one or more nodes at the subsequent depth. 
     Note that points 0 to 3 are examples of three-dimensional points. 
     Note that although child_count, pred_mode, and residual_value are calculated when adding each point to the prediction tree in the three-dimensional data encoding method described above, the present invention is not necessarily limited to this, and they may be calculated after the generation of the prediction tree ends. 
     The three-dimensional data encoding device to which a plurality of three-dimensional points are input may re-order the input three-dimensional points in ascending or descending Morton order and process the three-dimensional points in the latter order. This allows the three-dimensional data encoding device to efficiently search for the nearest point of the three-dimensional point to be processed and improve the encoding efficiency. The three-dimensional data encoding device need not re-order the three-dimensional points and may process the three-dimensional points in the order of input. For example, the three-dimensional data encoding device may generate a prediction tree without a branch in the order of input of a plurality of three-dimensional points. Specifically, the three-dimensional data encoding device may add an input three-dimensional point subsequent to a predetermined three-dimensional point in the order of input of a plurality of three-dimensional points as a child node of the predetermined three-dimensional point. 
     Next, a first example of prediction modes will be described with reference to  FIG.  51   .  FIG.  51    is a diagram for describing a first example of prediction modes according to Embodiment 4.  FIG.  51    is a diagram illustrating a part of a prediction tree. 
     As shown below, eight prediction modes may be set. As an example, a case where a predicted value for point c is calculated as shown in  FIG.  51    will be described. In the prediction tree, as shown, the parent node of point c is point p0, the grandparent node of point c is point p1, and the great grandparent node of point c is point p2. Note that point c, point p0, point p1, and point p2 are examples of three-dimensional points. 
     A prediction mode whose prediction mode value is 0 (referred to as prediction mode 0, hereinafter) may be set without prediction. That is, in prediction mode 0, the three-dimensional data encoding device may calculate geometry information of input point c as a predicted value of point c. 
     A prediction mode whose prediction mode value is 1 (referred to as prediction mode 1, hereinafter) may be set for a differential prediction with respect to point p0. That is, the three-dimensional data encoding device may calculate geometry information of point p0, which is the parent node of point c, as a predicted value of point c. 
     A prediction mode whose prediction mode value is 2 (referred to as prediction mode 2, hereinafter) may be set for a linear prediction based on point p0 and point p1. That is, the three-dimensional data encoding device may calculate, as a predicted value of point c, a prediction result of a linear prediction based on geometry information of point p0, which is the parent node of point c, and geometry information of point p1, which is the grandparent node of point c. 
       Predicted value=2× p 0− p 1  (Equation T1)
 
     In Equation T1, p0 denotes geometry information of point p0, and p1 denotes geometry information of point p1. 
     A prediction mode whose prediction mode value is 3 (referred to as prediction mode 3, hereinafter) may be set for a parallelogram prediction based on point p0, point p1, and point p2. That is, the three-dimensional data encoding device may calculate, as a predicted value of point c, a prediction result of a parallelogram prediction based on geometry information of point p0, which is the parent node of point c, geometry information of point p1, which is the grandparent node of point c, and geometry information of point p2, which is the great grandparent node of point c. Specifically, the three-dimensional data encoding device calculates a predicted value of point c in prediction mode 3 according to the following equation T2. 
       Predicted value= p 0+ p 1− p 2  (Equation T2)
 
     In Equation T2, p0 denotes geometry information of point p0, p1 denotes geometry information of point p1, and p2 denotes geometry information of point p2. 
     A prediction mode whose prediction mode value is 4 (referred to as prediction mode 4, hereinafter) may be set for a differential prediction with respect to point p1. That is, the three-dimensional data encoding device may calculate geometry information of point p1, which is the grandparent node of point c, as a predicted value of point c. 
     A prediction mode whose prediction mode value is 5 (referred to as prediction mode 5, hereinafter) may be set for a differential prediction with respect to point p2. That is, the three-dimensional data encoding device may calculate geometry information of point p2, which is the great grandparent node of point c, as a predicted value of point c. 
     A prediction mode whose prediction mode value is 6 (referred to as prediction mode 6, hereinafter) may be set for an average of geometry information of any two or more of point p0, point p1, and point p2. That is, the three-dimensional data encoding device may calculate, as a predicted value of point c, an average value of any two or more of geometry information of point p0, which is the parent node of point c, geometry information of point p1, which is the grandparent node of point c, and geometry information of point p2, which is the great grandparent node of point c. For example, when the three-dimensional data encoding device uses geometry information of point p0 and geometry information of point p1 for calculation of a predicted value, the three-dimensional data encoding device calculates a predicted value of point c in prediction mode 6 according to the following Equation T3. 
       Predicted value=( p 0+ p 1)/2  (Equation T3)
 
     In Equation T3, p0 denotes geometry information of point p0, and p1 denotes geometry information of point p1. 
     A prediction mode whose prediction mode value is 7 (referred to as prediction mode 7, hereinafter) may be set for a non-linear prediction based on distance d0 between point p0 and point p1 and distance d1 between point p2 and point p1. That is, the three-dimensional data encoding device may calculate, as a predicted value of point c, a prediction result of a non-linear prediction based on distance d0 and distance d1. 
     Note that the prediction method assigned to each prediction mode is not limited to the example described above. The eight prediction modes described above and the eight prediction methods described above need not be combined in the manner described above, and can be combined in any manner. For example, when prediction modes are encoded by an entropy encoding, such as arithmetic encoding, a prediction method of high frequency of use may be assigned to prediction mode 0. In this way, the encoding efficiency can be improved. The three-dimensional data encoding device may can also improve the encoding efficiency by dynamically changing the assignment of prediction modes according to the frequency of use of the prediction modes while performing the encoding process. For example, the three-dimensional data encoding device may count the frequency of use of each prediction mode in the encoding and assign a prediction mode indicated by a smaller value to a prediction method of a higher frequency of use. In this way, the encoding efficiency can be improved. Note that M denotes a prediction mode count indicating the number of prediction modes, and in the example described above, there are eight prediction modes 0 to 7, and therefore, M=8. 
     As predicted values (px, py, pz) of geometry information (x, y, z) of a three-dimensional point, the three-dimensional data encoding device may calculate predicted values used for calculation of geometry information of a three-dimensional point to be encoded based on geometry information of a three-dimensional point that is at a short distance from the three-dimensional point to be encoded among peripheral three-dimensional points of the three-dimensional point to be encoded. The three-dimensional data encoding device may add prediction mode information (pred_mode) for each three-dimensional point so that a predicted value to be calculated can be selected according to the prediction mode. 
     For example, when the total number of prediction modes is M, it is possible that geometry information of nearest three-dimensional point p0 is assigned to prediction mode 0, . . . , and geometry information of three-dimensional point p2 is assigned to prediction mode M−1, and the prediction mode used for prediction is added for each three-dimensional point to the bitstream. 
     Note that prediction mode count M may be added to the bitstream. The value of prediction mode count M need not be added to the bitstream, and may be defined by profile, level or the like of a standard. The value of prediction mode count M calculated from number N of three-dimensional points used for prediction may also be used. For example, prediction mode count M may be calculated according to M=N+1. 
       FIG.  52    is a diagram illustrating a second example of a table that indicates a predicted value calculated in each prediction mode according to Embodiment 4. 
     The table shown in  FIG.  52    is an example in the case where number N of three-dimensional points used for prediction is 4, and prediction mode count M=5. 
     In the second example, a predicted value of geometry information of point c is calculated based on geometry information of at least any one of point p0, point p1, and point p2. The prediction mode is added for each three-dimensional point to be encoded. The predicted value is calculated according to the prediction mode added. 
       FIG.  53    is a diagram illustrating a specific example of the second example of the table that indicates a predicted value calculated in each prediction mode according to Embodiment 4. 
     For example, the three-dimensional data encoding device may select prediction mode 1 and encode geometry information (x, y, z) of a three-dimensional point to be encoded based on predicted values (p0x, p0y, p0z), respectively. In that case, “1”, which is a prediction mode value indicating selected prediction mode 1, is added to the bitstream. 
     As described, when selecting a prediction mode for calculating a predicted value of each of three elements included in the geometry information of the three-dimensional point to be encoded, the three-dimensional data encoding device may select a prediction mode common to the three elements. 
       FIG.  54    is a diagram illustrating a third example of the table that indicates a predicted value calculated in each prediction mode according to Embodiment 4. 
     The table shown in  FIG.  54    is an example in the case where number N of three-dimensional points used for prediction is 2, and prediction mode count M=5. 
     In the third example, a predicted value of geometry information of point c is calculated based on geometry information of at least any one of point p0 and point p1. The prediction mode is added for each three-dimensional point to be encoded. The predicted value is calculated according to the prediction mode added. 
     Note that, when the number of peripheral three-dimensional points of point c (neighboring point count) is less than 3, as in the third example, any prediction mode to which no predicted value has been assigned may be set as “not available”. When a prediction mode set as “not available” occurs, another prediction method may be assigned to the prediction mode. For example, geometry information of point p2 may be assigned to the prediction mode as a predicted value. Alternatively, a predicted value assigned to another prediction mode may be assigned to the prediction mode. For example, geometry information of point p1, which is assigned to prediction mode 4, may be assigned to prediction mode 3, which is set as “not available”. In that case, geometry information of point p2 may be re-assigned to prediction mode 4. In this way, when a prediction mode set as “not available” occurs, the encoding efficiency can be improved by re-assigning a prediction method. 
     Note that when geometry information has three elements, such as in the case of the three-dimensional Cartesian coordinate system or the polar coordinate system, predicted values may be calculated in different modes for the three elements. For example, when the three elements are represented by x, y, and z of coordinates (x, y, z) in the three-dimensional Cartesian coordinate system, the predicted value of each of the three elements may be calculated in a prediction mode selected for the element. For example, prediction mode values of prediction mode pred_mode_x for calculating a predicted value of element x (that is, x coordinate), prediction mode pred_mode_y for calculating a predicted value of element y (that is, y coordinate), and prediction mode pred_mode_z for calculating a predicted value of element z (that is, z coordinate) may be selected. In that case, as the prediction mode values indicating the prediction modes of the elements, the values in the tables in  FIG.  55    to  FIG.  57    described later may be used, and these prediction mode values may be added to the bitstream. Note that although coordinates in the three-dimensional Cartesian coordinate system have been described above as an example of geometry information, the description holds true for coordinates in the polar coordinate system. 
     As described, when selecting a prediction mode for calculating a predicted value of each of three elements included in the geometry information of the three-dimensional point to be encoded, the three-dimensional data encoding device may select a different prediction mode for each of three elements. 
     Predicted values of two or more of a plurality of elements of geometry information may be calculated in a common prediction mode. For example, when the three elements are represented by x, y, and z of coordinates (x, y, z) in the three-dimensional Cartesian coordinate system, a prediction mode value of prediction mode pred_mode_x for calculating a predicted value of element x and prediction mode pred_mode_yz for calculating predicted values of elements y and z may be selected. In that case, as the prediction mode values indicating the prediction modes of the elements, the values in the tables in  FIG.  55    and  FIG.  58    described later may be used, and these prediction mode values may be added to the bitstream. 
     As described, when selecting a prediction mode for calculating a predicted value of each of three elements included in the geometry information of the three-dimensional point to be encoded, the three-dimensional data encoding device may select a common prediction mode for two of the three elements and select a different prediction mode than the prediction mode for the two elements for the remaining one element. 
       FIG.  55    is a diagram illustrating a fourth example of the table that indicates a predicted value calculated in each prediction mode. Specifically, the fourth example is an example in the case where geometry information used for a predicted value is the value of element x of geometry information of a peripheral three-dimensional point. 
     As shown in  FIG.  55   , a predicted value calculated in prediction mode pred_mode_x represented by a prediction mode value of “0” is 0. A predicted value calculated in prediction mode pred_mode_x represented by a prediction mode value of “1” is p0x, which is the x coordinate of point p0. A predicted value calculated in prediction mode pred_mode_x represented by a prediction mode value of “2” is (2×p0x−p1x), which is the prediction result of the linear prediction based on the x coordinate of point p0 and the x coordinate of point p1. A predicted value calculated in prediction mode pred_mode_x represented by a prediction mode value of “3” is (p0x+p1x−p2x), which is the prediction result of the parallelogram prediction based on the x coordinate of point p0, the x coordinate of point p1, and the x coordinate of point p2. A predicted value calculated in prediction mode pred_mode_x represented by a prediction mode value of “4” is p1x, which is the x coordinate of point p1. 
     Note that when prediction mode pred_mode_x represented by a prediction mode value of “1” in the table of  FIG.  55    is selected, for example, the x coordinate of the geometry information of the three-dimensional point to be encoded may be encoded using predicted value p0x. In that case, “1” as the prediction mode value is added to the bitstream. 
       FIG.  56    is a diagram illustrating a fifth example of the table that indicates a predicted value calculated in each prediction mode. Specifically, the fifth example is an example in the case where geometry information used for a predicted value is the value of element y of geometry information of a peripheral three-dimensional point. 
     As shown in  FIG.  56   , a predicted value calculated in prediction mode pred_mode_y represented by a prediction mode value of “0” is 0. A predicted value calculated in prediction mode pred_mode_y represented by a prediction mode value of “1” is p0y, which is the y coordinate of point p0. A predicted value calculated in prediction mode pred_mode_y represented by a prediction mode value of “2” is (2×p0y−p1y), which is the prediction result of the linear prediction based on the y coordinate of point p0 and they coordinate of point p1. A predicted value calculated in prediction mode pred_mode_y represented by a prediction mode value of “3” is (p0y+p1y−p2y), which is the prediction result of the parallelogram prediction based on the y coordinate of point p0, the y coordinate of point p1, and the y coordinate of point p2. A predicted value calculated in prediction mode pred_mode_y represented by a prediction mode value of “4” is p1y, which is the y coordinate of point p1. 
     Note that when prediction mode pred_mode_y represented by a prediction mode value of “1” in the table of  FIG.  56    is selected, for example, the y coordinate of the geometry information of the three-dimensional point to be encoded may be encoded using predicted value p0y. In that case, “1” as the prediction mode value is added to the bitstream. 
       FIG.  57    is a diagram illustrating a sixth example of the table that indicates a predicted value calculated in each prediction mode. Specifically, the sixth example is an example in the case where geometry information used for a predicted value is the value of element z of geometry information of a peripheral three-dimensional point. 
     As shown in  FIG.  57   , a predicted value calculated in prediction mode pred_mode_z represented by a prediction mode value of “0” is 0. A predicted value calculated in prediction mode pred_mode_z represented by a prediction mode value of “1” is p0z, which is the z coordinate of point p0. A predicted value calculated in prediction mode pred_mode_z represented by a prediction mode value of “2” is (2×p0z−p1z), which is the prediction result of the linear prediction based on the z coordinate of point p0 and the z coordinate of point p1. A predicted value calculated in prediction mode pred_mode_z represented by a prediction mode value of “3” is (p0z+p1z−p2z), which is the prediction result of the parallelogram prediction based on the z coordinate of point p0, the z coordinate of point p1, and the z coordinate of point p2. A predicted value calculated in prediction mode pred_mode_z represented by a prediction mode value of “4” is p1z, which is the z coordinate of point p1. 
     Note that when prediction mode pred_mode_z represented by a prediction mode value of “1” in the table of  FIG.  57    is selected, for example, the z coordinate of the geometry information of the three-dimensional point to be encoded may be encoded using predicted value p0z. In that case, “1” as the prediction mode value is added to the bitstream. 
       FIG.  58    is a diagram illustrating a seventh example of the table that indicates a predicted value calculated in each prediction mode. Specifically, the seventh example is an example in the case where geometry information used for a predicted value are the values of element y and element z of geometry information of a peripheral three-dimensional point. 
     As shown in  FIG.  58   , predicted values calculated in prediction mode pred_mode_yz represented by a prediction mode value of “0” are 0. Predicted values calculated in prediction mode pred_mode_yz represented by a prediction mode value of “1” are (p0y, p0z), which are the y coordinate and z coordinate of point p0. Predicted values calculated in prediction mode pred_mode_yz represented by a prediction mode value of “2” are (2×p0y−p1y, 2×p0z−p1z), which are the prediction result of the linear prediction based on the y coordinate and z coordinate of point p0 and the y coordinate and z coordinate of point p1. Predicted values calculated in prediction mode pred_mode_yz represented by a prediction mode value of “3” are (p0y+p1y−p2y, p0z+p1z−p2z), which are the prediction result of the parallelogram prediction based on the y coordinate and z coordinate of point p0, the y coordinate and z coordinate of point p1, and the y coordinate and z coordinate of point p2. Predicted values calculated in prediction mode pred_mode_yz represented by a prediction mode value of “4” are (ply, p1z), which are the y coordinate and z coordinate of point p1. 
     Note that when prediction mode pred_mode_yz represented by a prediction mode value of “1” in the table of  FIG.  58    is selected, for example, the y coordinate and z coordinate of the geometry information of the three-dimensional point to be encoded may be encoded using predicted values (p0y, p0z). In that case, “1” as the prediction mode value is added to the bitstream. 
     In the tables of the fourth to seventh examples, the correspondence between the prediction modes and the prediction methods for calculating predicted values are the same as the correspondence in the table of the second example described above. 
     The prediction mode in the encoding may be selected by RD optimization. For example, cost cost(P) in the case where certain prediction mode P is selected may be calculated, and prediction mode P for which cost(P) is at the minimum may be selected. Cost cost(P) may be calculated from prediction residual residual_value(P) in the case where the predicted value in prediction mode P is used, number of bits bit(P) required for encoding prediction mode P, and a X, value, which is an adjustment parameter, according to equation D1. 
       cost( P )=abs(residual_value( P ))+λ×bit( P )  (Equation D1)
 
     abs(x) denotes an absolute value of x. 
     Instead of abs(x), the square of x may be used. 
     By using above equation D1, a prediction mode can be selected by considering the balance between the magnitude of the prediction residual and the number of bits required for encoding the prediction mode. Note that the adjustment parameter λ may be set to be different values according to the value of a quantization scale. For example, it is possible that when the quantization scale is small (when the bit rate is high), the λ value is decreased so that a prediction mode in which prediction residual residual_value(P) is small is selected and the prediction precision is improved as far as possible, while when the quantization scale is large (when the bit rate is low), the λ value is increased so that an appropriate prediction mode is selected by considering number of bits bit(P) required for encoding prediction mode P. 
     Note that the case where the quantization scale is small means a case where the quantization scale is smaller than a first quantization scale, for example. The case where the quantization scale is large means a case where the quantization scale is larger than a second quantization scale that is larger than or equal to the first quantization scale. The λ value may be set to be smaller as the quantization scale is smaller. 
     Prediction residual residual_value(P) is calculated by subtracting the predicted value in prediction mode P from the geometry information of the three-dimensional point to be encoded. Note that instead of reflecting prediction residual residual_value(P) in the cost calculation, prediction residual residual_value(P) may be quantized, inverse-quantized, and added to the predicted value to determine a decoded value, and the difference (encoding error) between the original geometry information of the three-dimensional point and the decoded value obtained using prediction mode P may be reflected in the cost value. This allows a prediction mode with a small encoding error to be selected. 
     When a prediction mode is binarized and then encoded, for example, number of bits bit(P) required for encoding prediction mode P may be the bit count after the binarization. 
     For example, when prediction mode count M=5, as shown in  FIG.  59   , a prediction mode value representing a prediction mode with a truncated unary code having a maximum value of 5 based on prediction mode count M may be binarized In that case, number of bits bit(P) required for encoding the prediction mode value is 1 when the prediction mode value is “0”, 2 when the prediction mode value is “1”, 3 when the prediction mode value is “2”, and 4 when the prediction mode value is “3” or “4”. By using the truncated unary code, the bit count decreases as the value of the prediction mode value decreases. Therefore, the code amount can be reduced for a prediction mode value representing a prediction mode that is likely to be selected, for example, a prediction mode in which a predicted value with which cost(P) is likely to be at the minimum is calculated, such as the predicted value of 0 calculated when the prediction mode value is “0” or the geometry information of three-dimensional point p0 calculated as a predicted value when the prediction mode value is “1”, that is, the geometry information of a three-dimensional point that is at a small distance from the three-dimensional point to be encoded. 
     As described, the three-dimensional data encoding device may encode the prediction mode value representing selected prediction mode with the prediction mode count. Specifically, the three-dimensional data encoding device may encode a prediction mode value with a truncated unary code whose maximum value is the prediction mode count. 
     When the maximum value of the prediction mode count is not determined, as shown in  FIG.  60   , a prediction mode value representing a prediction mode may be binarized with a unary code. When the probabilities of occurrence of the prediction modes are close to each other, as shown in  FIG.  61   , a prediction mode value representing a prediction mode may be binarized with a fixed code to reduce the code amount. 
     As the value of number of bits bit(P) required for encoding the prediction mode value representing prediction mode P, binary data of the prediction mode value representing prediction mode P may be arithmetically encoded, and the code amount of the arithmetically encoded binary data may be used. In that case, the cost can be calculated with more precise required bit count bit(P), so that a prediction mode can be more properly selected. 
     Note that  FIG.  59    is a diagram illustrating a first example of a binarization table in the case where a prediction mode value is binarized and encoded according to Embodiment 4. Specifically, the first example is an example in which prediction mode count M=5, and a prediction mode value is binarized with a truncated unary code. 
     Note that  FIG.  60    is a diagram illustrating a second example of the binarization table in the case where a prediction mode value is binarized and encoded according to Embodiment 4. Specifically, the second example is an example in which prediction mode count M=5, and a prediction mode value is binarized with a unary code. 
     Note that  FIG.  61    is a diagram illustrating a third example of the binarization table in the case where a prediction mode value is binarized and encoded according to Embodiment 4. Specifically, the third example is an example in which prediction mode count M=5, and a prediction mode value is binarized with a fixed code. 
     The prediction mode value representing the prediction mode (pred_mode) may be binarized and then arithmetically encoded before being added to the bitstream. The prediction mode value may be binarized with a truncated unary code using the value of prediction mode count M as described above, for example. In that case, the maximum bit count after the binarization of the prediction mode value is M−1. 
     The binary data resulting from the binarization may be arithmetically encoded using an encoding table. In that case, the encoding efficiency may be improved by encoding the binary data using a different encoding table for each bit. Furthermore, in order to reduce the number of encoding tables, the leading one bit of the binary data may be encoded using encoding table A for the leading bit, and each bit of the remaining bits of the binary data may be encoded using encoding table B for the remaining bits. For example, when encoding binary data “1110” whose prediction mode value is “3” shown in  FIG.  62   , the leading one bit “1” may be encoded using encoding table A, and each bit of the remaining bits “110” may be encoded using encoding table B. 
     Note that  FIG.  62    is a diagram for describing an example of encoding of binary data in a binarization table in the case where a prediction mode value is binarized and encoded according to Embodiment 4. The binarization table in  FIG.  62    is an example in the case where prediction mode count M=5, and a prediction mode value is binarized with a truncated unary code. 
     In this way, the encoding efficiency can be improved by using a different encoding table depending on the position of the bit in the binary data, while reducing the number of encoding tables. Note that, when encoding the remaining bits, each bit may be arithmetically encoded using a different encoding table, or each bit may be decoded using a different encoding table based on the result of the arithmetic encoding. 
     When a prediction mode value is binarized and encoded with a truncated unary code using prediction mode count M, prediction mode count M used for the truncated unary code may be added to the header or the like of the bitstream, in order that the prediction mode can be identified from the binary data decoded on the decoder side. The header of the bitstream is a sequence parameter set (SPS), a geometry parameter set (GPS), or a slice header, for example. Maximum possible value MaxM of the prediction mode count may be defined by a standard or the like, and the value of MaxM−M (M&lt;=MaxM) may be added to the header. Prediction mode count M need not be added to the stream, and may be defined by profile or level of a standard or the like. 
     Note that the prediction mode value binarized with a truncated unary code can be arithmetically encoded by using different encoding tables for the leading bit part and the remaining part as described above. 
     Note that the probabilities of occurrence of 0 and 1 in each encoding table may be updated according to the value of the binary data that has actually occurred. The probabilities of occurrence of 0 and 1 in one of the encoding tables may be fixed. By reducing the number of updates of the probabilities of occurrence in this way, the processing amount can be reduced. For example, it is possible that the probabilities of occurrence for the leading bit part is updated, while the probabilities of occurrence for the remaining bit part is fixed. 
       FIG.  63    is a flowchart illustrating an example of encoding of a prediction mode value according to Embodiment 4.  FIG.  64    is a flowchart illustrating an example of decoding of a prediction mode value according to Embodiment 4. 
     As shown in  FIG.  63   , in encoding of a prediction mode value, the prediction mode value is first binarized with a truncated unary code using prediction mode count M (S 9701 ). 
     The binary data of the truncated unary code is then arithmetically encoded (S 9702 ). In this way, the binary data is included in the bitstream as a prediction mode. 
     As shown in  FIG.  64   , in decoding of a prediction mode value, a bitstream is first arithmetically decoded using prediction mode count M to generate binary data of a truncated unary code (S 9711 ). 
     A prediction mode value is then calculated from the binary data of the truncated unary code (S 9712 ). 
     Although an example where a prediction mode value representing a prediction mode (pred_mode) is binarized with a truncated unary code using the value of prediction mode count M has been shown as a method of binarizing a prediction mode value representing a prediction mode (pred_mode), the present invention is not necessarily limited to this. For example, a prediction mode value may be binarized with a truncated unary code using number L (L&lt;=M) of prediction modes to which a predicted value is assigned. For example, when prediction mode count M=5, if there is one peripheral three-dimensional point available for prediction of a certain three-dimensional point to be encoded, two prediction modes may be “available”, and the remaining three prediction modes may be “not available” as shown in  FIG.  65   . For example, as shown in  FIG.  65   , when prediction modes M=5, there may be one peripheral three-dimensional point of the three-dimensional point to be encoded that is available for prediction, and no predicted value may be assigned to the prediction modes represented by prediction mode values of “2”, “3”, and “4”. 
     In that case, as shown in  FIG.  66   , if the prediction mode value is binarized with a truncated unary code whose maximum value is number L of prediction modes to which a predicted value is assigned, the bit count after the binarization may be able to be reduced compared with the case where the prediction mode value is binarized with a truncated unary code using prediction mode count M. In this case, for example, L=3, and therefore, the bit count can be reduced by binarizing the prediction mode value with a truncated unary code whose maximum value is 3. In this way, by binarizing the prediction mode value with a truncated unary code whose maximum value is number L of prediction modes to which a predicted value is assigned, the bit count after the binarization of the prediction mode value can be reduced. 
     The binary data resulting from the binarization may be arithmetically encoded using an encoding table. In that case, the encoding efficiency may be improved by encoding the binary data using a different encoding table for each bit. Furthermore, in order to reduce the number of encoding tables, the leading one bit of the binary data may be encoded using encoding table A for the leading bit, and each bit of the remaining bits of the binary data may be encoded using encoding table B for the remaining bits. For example, when encoding binary data “1” whose prediction mode value is “1” shown in  FIG.  66   , the leading one bit “1” is encoded using encoding table A. There is no remaining bit, and therefore, further encoding is not needed. If there is any remaining bit, the remaining bit may be encoded using encoding table B. 
     Note that  FIG.  66    is a diagram for describing an example of encoding of binary data in a binarization table in the case where a prediction mode value is binarized and encoded according to Embodiment 4. The binarization table in  FIG.  66    is an example in the case where a prediction mode value is binarized with a truncated unary code, provided with number L of prediction modes to which a predicted value is assigned is 2. 
     In this way, the encoding efficiency can be improved by using a different encoding table depending on the position of the bit in the binary data, while reducing the number of encoding tables. Note that, when encoding the remaining bits, each bit may be arithmetically encoded using a different encoding table, or each bit may be decoded using a different encoding table based on the result of the arithmetic encoding. 
     When a prediction mode value is binarized and encoded with a truncated unary code using number L of prediction modes to which a predicted value is assigned, a prediction mode may be decoded on the decoder side by assigning a predicted value to a prediction mode in the same manner as in the encoding to calculate number L and using calculated number L to decode the prediction mode, in order that the prediction mode can be identified from the binary data decoded on the decoder side. 
     Note that the prediction mode value binarized with a truncated unary code can be arithmetically encoded by using different encoding tables for the leading bit part and the remaining part as described above. Note that the probabilities of occurrence of 0 and 1 in each encoding table may be updated according to the value of the binary data that has actually occurred. The probabilities of occurrence of 0 and 1 in one of the encoding tables may be fixed. By reducing the number of updates of the probabilities of occurrence in this way, the processing amount can be reduced. For example, it is possible that the probabilities of occurrence for the leading bit part is updated, while the probabilities of occurrence for the remaining bit part is fixed. 
       FIG.  67    is a flowchart illustrating another example of encoding of a prediction mode value according to Embodiment 4.  FIG.  68    is a flowchart illustrating another example of decoding of a prediction mode value according to Embodiment 4. 
     As shown in  FIG.  67   , in encoding of a prediction mode value, number L of prediction modes to which a predicted value is assigned is first calculated (S 9721 ). 
     The prediction mode value is then binarized with a truncated unary code using number L (S 9722 ). 
     The binary data of the truncated unary code is then arithmetically encoded (S 9723 ). 
     As shown in  FIG.  68   , in decoding of a prediction mode value, number L of prediction modes to which a predicted value is assigned is first calculated (S 9731 ). 
     The bitstream is then arithmetically decoded using number L to generate binary data of a truncated unary code (S 9732 ). 
     A prediction mode value is then calculated from the binary data of the truncated unary code (S 9733 ). 
     The prediction mode value need not be added to every geometry information. For example, it is possible that when a certain condition is satisfied, the prediction mode is fixed, and no prediction mode value is added to the bitstream, while when the certain condition is not satisfied, a prediction mode is selected, and a prediction mode value is added to the bitstream. For example, it is possible that when condition A is satisfied, the prediction mode value is fixed at “2”, and the predicted value is calculated by linear prediction based on peripheral three-dimensional points, and when condition A is not satisfied, one prediction mode is selected from among a plurality of prediction modes, and the prediction mode value representing the selected prediction mode is added to the bitstream. 
     Certain condition A may be that distance d0 between point p1 and point p0 and distance d1 between point p2 and point p1 are calculated, and absolute difference value distdiff=|d0−d1| is less than threshold Thfix. When the absolute difference value is less than threshold Thfix, the three-dimensional data encoding device determines that the difference between the predicted value of the linear prediction and the geometry information of the point to be processed is small, fixes the prediction mode value at “2”, and encodes no prediction mode value. In this way, the three-dimensional data encoding device can generate an appropriate predicted value while reducing the code amount required for encoding the prediction mode. Note that when the absolute difference value is greater than or equal to threshold Thfix, the three-dimensional data encoding device may select a prediction mode and encode the prediction mode value representing the selected prediction mode. 
     Note that threshold Thfix may be added to the header or the like of the bitstream, and the encoder may be able to encode by changing the value of threshold Thfix. For example, in encoding at high bit rate, the encoder may set the value of threshold Thfix to be smaller than in encoding at low bit rate and add the value of threshold Thfix to the header, thereby increasing the cases where encoding is performed by selecting a prediction mode, so that the prediction residual is minimized. In encoding at low bit rate, the encoder sets the value of threshold Thfix to be greater than in encoding at high bit rate, adds the value of threshold Thfix to the header, and perform the encoding with a fixed prediction mode. In this way, by increasing the cases where encoding is performed with a fixed prediction mode in encoding at low bit rate, the encoding efficiency can be improved while reducing the bit amount involved with the encoding of the prediction mode. Threshold Thfix need not be added to the bitstream, and may be defined by profile or level of a standard. 
     N peripheral three-dimensional points of the three-dimensional point to be encoded that are used for prediction are N three-dimensional points encoded and decoded the distance from the three-dimensional point to be encoded is less than threshold THd. The maximum value of N may be added to the bitstream as NumNeighborPoint. The value of N need not always agree with the value of NumNeighborPoint, such as when the number of peripheral three-dimensional points encoded and decoded is less than the value of NumNeighborPoint. 
     Although an example has been shown where the prediction mode value is fixed at “2” when absolute difference value distdiff used for prediction is less than threshold Thfix[i], the present invention is not necessarily limited to this, and the prediction mode value may be fixed at any of “0” to “M−1”. The prediction mode value fixed may be added to the bitstream. 
       FIG.  69    is a flowchart illustrating an example of a process of determining whether or not to fix the prediction mode value according to condition A in encoding according to Embodiment 4.  FIG.  70    is a flowchart illustrating an example of a process of determining whether to set the prediction mode value at a fixed value or decode the prediction mode value according to condition A in decoding according to Embodiment 4. 
     As shown in  FIG.  69   , the three-dimensional data encoding device first calculates distance d0 between point p1 and point p0 and distance d1 between point p2 and point p1, and calculates absolute difference value distdiff=|d0−d1| (S 9741 ). 
     The three-dimensional data encoding device then determines whether absolute difference value distdiff is less than threshold Thfix or not (S 9742 ). Note that threshold Thfix may be encoded and added to the header or the like of the stream. 
     When absolute difference value distdiff is less than threshold Thfix (if Yes in S 9742 ), the three-dimensional data encoding device determines the prediction mode value to be “2” (S 9743 ). 
     On the other hand, when absolute difference value distdiff is greater than or equal to threshold Thfix (if No in S 9742 ), the three-dimensional data encoding device sets one prediction mode from among a plurality of prediction modes (S 9744 ). 
     The three-dimensional data encoding device then arithmetically encodes the prediction mode value representing the set prediction mode (S 9745 ). Specifically, the three-dimensional data encoding device arithmetically encodes the prediction mode value by performing steps S 9701  and S 9702  described above with reference to  FIG.  63   . Note that the three-dimensional data encoding device may arithmetically encode prediction mode pred_mode after binarizing the prediction mode with a truncated unary code using the number of prediction modes to which a predicted value is assigned. That is, the three-dimensional data encoding device may arithmetically encode the prediction mode value by performing steps S 9721  to S 9723  described above with reference to  FIG.  67   . 
     The three-dimensional data encoding device calculates a predicted value in the prediction mode determined in step S 9743  or the prediction mode set in step S 9745 , and outputs the calculated predicted value (S 9746 ). When using the prediction mode value determined in step S 9743 , the three-dimensional data encoding device calculates the predicted value in the prediction mode represented by the prediction mode value of “2” by linear prediction based on the geometry information of N peripheral three-dimensional points. 
     As shown in  FIG.  70   , the three-dimensional data decoding device first calculates distance d0 between point p1 and point p0 and distance d1 between point p2 and point p1, and calculates absolute difference value distdiff=|d0−d1| (S 9751 ). 
     The three-dimensional data decoding device then determines whether absolute difference value distdiff is less than threshold Thfix or not (S 9752 ). Note that threshold Thfix may be set by decoding the header or the like of the stream. 
     When absolute difference value distdiff is less than threshold Thfix (if Yes in S 9752 ), the three-dimensional data decoding device determines the prediction mode value to be “2” (S 9753 ). 
     On the other hand, when absolute difference value distdiff is greater than or equal to threshold Thfix (if No in S 9752 ), the three-dimensional data decoding device decodes the prediction mode value from the bitstream (S 9754 ). 
     The three-dimensional data decoding device calculates a predicted value in the prediction mode represented by the prediction mode value determined in step S 9753  or the prediction mode value decoded in step S 9754 , and outputs the calculated predicted value (S 9755 ). When using the prediction mode value determined in step S 9753 , the three-dimensional data decoding device calculates the predicted value in the prediction mode represented by the prediction mode value of “2” by linear prediction based on the geometry information of N peripheral three-dimensional points. 
       FIG.  71    is a diagram illustrating an example of a syntax of a header of geometry information. NumNeighborPoint, NumPredMode, Thfix, QP, and unique_point_per_leaf in the syntax in  FIG.  71    will be sequentially described. 
     NumNeighborPoint denotes an upper limit of the number of peripheral points used for generation of a predicted value of geometry information of a three-dimensional point. When number M of peripheral points is less than NumNeighborPoint (M&lt;NumNeighborPoint), a predicted value may be calculated using the M peripheral points in the predicted value calculation process. 
     NumPredMode denotes total number M of prediction modes used for prediction of geometry information. Note that maximum possible value MaxM of the prediction mode count may be defined by a standard or the like. The three-dimensional data encoding device may add the value of (MaxM−M) (0&lt;M&lt;=MaxM) to the header as NumPredMode, and binarize and encode (MaxM−1) with a truncated unary code. Prediction mode count NumPredMode need not be added to the bitstream, and the value of NumPredMode may be defined by profile or level of a standard or the like. The prediction mode count may be defined as NumNeighborPoint+NumPredMode. 
     Thfix is a threshold for determining whether to fix the prediction mode or not. Distance d0 between point p1 and point p0 and distance d1 between point p2 and point p1 used for prediction are calculated, and the prediction mode is fixed to be α if absolute difference value distdiff=|d0−d1| is less than threshold Thfix[i]. α is a prediction mode for calculating a predicted value based on a linear prediction, and is “2” in the embodiment described here. Note that Thfix need not be added to the bitstream, and the value may be defined by profile or level of a standard or the like. 
     QP denotes a quantization parameter used for quantizing geometry information. The three-dimensional data encoding device may calculate a quantization step from the quantization parameter, and quantize geometry information using the calculated quantization step. 
     unique_point_per_leaf is information that indicates whether a duplicated point (a point having the same geometry information as another point) is included in the bitstream or not. When unique_point_per_leaf=1, it shows that there are no duplicated points in the bitstream. When unique_point_per_leaf=0, it shows that there is one or more duplicated points in the bitstream. 
     Note that although the determination of whether to fix the prediction mode or not has been described as being performed using the absolute difference value between distance d0 and distance d1 in this embodiment, the present invention is not limited to this, and the determination may be made in any manner. For example, the determination may be performed by calculating distance d0 between point p1 and point p0. Specifically, it may be determined that point p1 cannot be used for prediction and the prediction mode value may be fixed at “1” (a predicted value of p0) when distance d0 is greater than a threshold, and a prediction mode may be set otherwise. In this way, the encoding efficiency can be improved while reducing the overhead. 
     NumNeighborPoint, NumPred Mode, Thfix, and unique_point_per_leaf described above may be entropy-encoded and added to the header. For example, these values may be binarized and arithmetically encoded. These values may be encoded with a fixed length, in order to reduce the processing amount. 
       FIG.  72    is a diagram illustrating an example of a syntax of geometry information. NumOfPoint, child_count, pred_mode, and residual_value[j] in the syntax in  FIG.  72    will be sequentially described. 
     NumOfPoint denotes the total number of three-dimensional points included in a bitstream. 
     child count denotes the number of child nodes of an i-th three-dimensional point (node[i]). 
     pred_mode denotes a prediction mode for encoding or decoding geometry information of the i-th three-dimensional point. pred_mode assumes a value from 0 to M−1 (M denotes the total number of prediction modes). When pred_mode is not in the bitstream (when the condition that distdiff&gt;=Thfix[i] &amp;&amp; NumPredMode&gt;1 is not satisfied), pred_mode may be estimated to be fixed value α. α is a prediction mode for calculating a predicted value based on a linear prediction, and is “2” in the embodiment described here. Note that a is not limited to “2”, and any value of 0 to M−1 may be set as an estimated value. An estimated value in the case where pred_mode is not in the bitstream may be additionally added to the header or the like. pred_mode may be binarized and arithmetically encoded with a truncated unary code using the number of prediction modes to which a predicted value is assigned. 
     Note that when NumPredMode=1, that is, when the prediction mode count is 1, the three-dimensional data encoding device need not encode a prediction mode value representing a prediction mode and may generate a bitstream that includes no prediction mode value. When the three-dimensional data decoding device obtains a bitstream that includes no prediction mode value, the three-dimensional data decoding device may calculate a predicted value of a particular prediction mode in the predicted value calculation. The particular prediction mode is a previously determined prediction mode. 
     residual_value[j] denotes encoded data of a prediction residual between geometry information and a predicted value thereof. residual_value[0] may represent element x of the geometry information, residual_value[1] may represent element y of the geometry information, and residual_value[2] may represent element z of the geometry information. 
       FIG.  73    is a diagram illustrating another example of the syntax of geometry information. The example in  FIG.  73    is a modification of the example in  FIG.  72   . 
     As shown in  FIG.  73   , pred_mode may denote a prediction mode for each of three elements of geometry information (x, y, z). That is, pred_mode[0] denotes a prediction mode for element x, pred_mode[1] denotes a prediction mode for element y, and pred_mode[2] denotes a prediction mode for element z. pred_mode[0], pred_mode[1], and pred_mode[2] may be added to the bitstream. 
     Embodiment 5 
     A method of encoding and decoding point cloud data with low delay will be described with reference to  FIG.  74   .  FIG.  74    is a diagram for describing the method of encoding and decoding point cloud data with low delay. 
     Encoding point cloud data with low delay means immediately and sequentially outputting bitstreams generated by encoding point cloud data. Three-dimensional data encoding device  12602  obtains three-dimensional points from measuring instrument  12601  (such as LiDAR), and three-dimensional data encoding device  12602  starts encoding three-dimensional points at regular intervals or each time three-dimensional data encoding device  12602  obtains a predetermined number of three-dimensional points. A bitstream generated by encoding point cloud data is transformed into a data packet, which is transmitted from transmitter  12603  to receiver  12604 . Three-dimensional data encoding device  12602  starts encoding the subsequent plurality of obtained three-dimensional points in parallel with the processing of transmitting the data packet. That is, the processings in measuring instrument  12601 , three-dimensional data encoding device  12602 , and transmitter  12603  are performed in parallel. These processings are performed in real time based on the specifications of measuring instrument  12601 , three-dimensional data encoding device  12602 , and transmitter  12603 . 
     Decoding point cloud data with low delay means decoding the first several data packets, which are packetized bitstreams, immediately after receiving the several data packets. Receiver  12604  receives a plurality of data packets. Three-dimensional data decoding device  12605  decodes the plurality of data packets received by receiver  12604 . The processing of decoding the data packets by three-dimensional data decoding device  12605  and the processing of receiving the subsequent data packets by receiver  12604  are performed in parallel. Three-dimensional point cloud obtained by the decoding by three-dimensional data decoding device  12605  may be retained in information processing device  12606 , such as a computer or a server, or may be displayed on a display of information processing device  12606 . If more three-dimensional point clouds are obtained by decoding, the resolution of the three-dimensional point clouds can be raised by combining the obtained three-dimensional point clouds. 
     The point cloud data processing with low delay can be applied to at least one of the processing on the encoding side (that is, the processings in measuring instrument  12601 , three-dimensional data encoding device  12602 , and transmitter  12603 ) and the processing on the decoding side (that is, the processings in receiver  12604 , three-dimensional data decoding device  12605 , and information processing device  12606 ). 
     Next, a method of generating a prediction tree will be described with reference to  FIG.  75   .  FIG.  75    is a diagram for describing an example of the method of generating a prediction tree. 
     The three-dimensional data encoding device starts generating a prediction tree by setting a root point (root node). When a new point is input, the three-dimensional data encoding device links the input new point to an already input, existing node as a child node of the existing node. In this way, the three-dimensional data encoding device generates a prediction tree structure. The prediction tree structure may also be referred to simply as a prediction tree. In this process, when the input new point is far away from all existing points, that is, when the new point is at a predetermined distance or more from all existing points, the three-dimensional data encoding device may set the new point as a root node, which belongs to no other nodes. That is, the three-dimensional data encoding device may set a root node and generate a new prediction tree. 
     The encoding efficiency depends on the arrangements of three-dimensional points or the position of the start point (root node). Therefore, in order to improve the encoding efficiency, the way of rearrangement of three-dimensional points and the way of setting of the start point are important. 
     In the encoding using the prediction tree, the encoding can be expected to be made more efficient by the user setting a particular root point. For example, the user may determine regions that are highly correlated in advance, and set a root point so that peripheries of the regions are effectively covered. If the user can set a particular root node, a particular prediction tree can be more preferentially controlled than other prediction trees. 
     In the encoding using the prediction tree, the size of the prediction tree may be limited. The fewer the prediction trees, the higher the compressibility is. However, when a plurality of prediction trees are generated, encoding and decoding can occur in parallel for a plurality of prediction trees, so that the processing speed can be increased. By limiting the size of the prediction tree, the sizes of the prediction trees can be made more uniform. Therefore, the processing loads of the parallel processing can be distributed. 
     The prediction tree ID may be implemented, along with root point information, in order to allow easy discrimination of a prediction tree. By implementing the prediction tree ID, random access search can be more easily performed using the prediction tree ID. 
     In the prediction tree shown in  FIG.  75   , the number of children that can be linked to each point is limited to three. However, the number of children that can be linked to one three-dimensional point can be increased at a cost. 
       FIG.  76    is a diagram for describing a three-dimensional polar coordinate system. 
     Each three-dimensional point of a three-dimensional point cloud can be transformed from three-dimensional Cartesian coordinates (X, Y, Z) into three-dimensional polar coordinates (R, α, β) with respect to reference point C1 of measuring instrument  12601  according to the following equation X1 using sensor parameters (α, β) of measuring instrument  12601 . 
     As shown in part (c) of  FIG.  76   , R of the three-dimensional polar coordinates indicates the distance from reference point C1 of measuring instrument  12601  to three-dimensional point P1. 
     As shown in part (b) of  FIG.  76   , α of the three-dimensional polar coordinates indicates an angle (horizontal angle) of three-dimensional point P1 with respect to a particular horizontal direction (such as a positive direction along the Y axis), which is an angular component about a vertical axis passing through reference point C1 of measuring instrument  12601  for determining an angle of emission of laser light from measuring instrument  12601  when measuring three-dimensional point P1. That is, the three-dimensional polar coordinate a indicates an angle about the vertical axis (Z axis) between the positive direction along the Y axis and the direction from reference point C1 to three-dimensional point P1. 
     As shown in part (a) of  FIG.  76   , β of the three-dimensional polar coordinates indicates an angle (vertical angle) of three-dimensional point P1 with respect to a particular direction along a plane (horizontal plane) perpendicular to a horizontal axis passing through reference point C1 of measuring instrument  12601  (such as a positive direction along the Y axis), which is an angular component about the horizontal axis for determining an angle of emission of laser light from measuring instrument  12601  when measuring three-dimensional point P1. That is, the three-dimensional polar coordinate a indicates an angle between the positive direction along the Y axis and the direction from reference point C1 to three-dimensional point P1. That is, the three-dimensional polar coordinate β indicates an angle about the horizontal axis (X axis) between the positive direction along the Y axis and the direction from reference point C1 to three-dimensional point P1. 
     As described above, each of a plurality of directions in which measuring instrument  12601  emits laser light is defined by the angle β with respect to a reference plane (XY plane) passing through measuring instrument  12601  and the angle α with respect to a reference direction (a positive direction along the Y axis) about an axis (Z axis) that passes through measuring instrument  12601  and is perpendicular to the reference plane. 
     
       
         
           
             
               
                 
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     As described above, a three-dimensional point cloud obtained by measuring instrument  12601  may be indicated by three-dimensional polar coordinates with respect to reference point C1 of measuring instrument  12601 . Each three-dimensional point of the three-dimensional point cloud may be indicated by (β, α, R, L). L denotes a reflectance obtained when point P1 is obtained. 
     Equation X1 can be rewritten as the following equation X2 with respect to X, Y, and Z. 
       [Math. 2] 
         X=R  cos β sin α
 
         Y=R  cos β cos α
 
         Z=R  sin β  (Equation X2)
 
       FIG.  77    is a diagram for describing a method of projecting a three-dimensional point obtained by measuring instrument  12601  onto a two-dimensional image. 
     A three-dimensional point cloud including n layers (n denotes a natural number) each including k points, such as the one shown in part (a) of  FIG.  77   , can be represented by two-dimensional image  12610 , such as the one shown in part (b) of  FIG.  77   . A pixel value Rij (i and j denote a natural number) in two-dimensional image  12610  indicates a distance measured with laser light emitted at a vertical angle βi and a horizontal angle αj. A vertical position in a vertical direction of each pixel of two-dimensional image  12610  represents the angle β, and a horizontal position in a horizontal direction of each pixel of two-dimensional image  12610  represents the angle α. In this way, for each of a plurality of three-dimensional points, the three-dimensional data encoding device can transform the three-dimensional Cartesian coordinates of the three-dimensional point into three-dimensional polar coordinates indicated by the angles α and β and the distance R, and generate two-dimensional image  12610  using the transformation result. 
       FIG.  78    is a diagram illustrating an example of sensor parameters and a two-dimensional image of a three-dimensional point cloud. 
     As shown in part (a) of  FIG.  78   , the sensor parameters include n angle values β (n denotes a natural number). As shown in part (b) of FIG.  78 , the sensor parameters include k angle values α (k denotes a natural number). As shown in part (c) of  FIG.  78   , each of a plurality of pixels forming a two-dimensional image has a pixel value that indicates the distance R and the reflectance L measured when laser light is emitted in an emission direction determined by α and β associated with the pixel. The sensor parameters α and β may be separately encoded as shown in parts (a) and (b) of  FIG.  78    or may be integrated into a two-dimensional array and then encoded. 
     The sensor parameters need not include n angle values β nor include k angle values α. For example, when measuring instrument  12601  emits laser light at regular angular intervals, the sensor parameters may include a value (angle value) indicating the regular angular interval. That is, the sensor parameters may include one angle value α and one angle value β. 
     Furthermore, based on the sensor parameters α and β, items of data having the same value of α or β may be grouped to set a plurality of items of divisional data. 
     Next, a low-delay encoding method based on division into slices will be described. 
     Encoded data generated by encoding point cloud data or a bitstream including such encoded data is transmitted in units of packets after the encoding is completed. That is, in encoding, the time between the time of starting encoding and the time of starting transmission can be reduced by reducing the time between the time of starting encoding and the time of ending encoding (that is, the time in which one packet is generated). 
     For example, when transmitting encoded data (or a bitstream) using an IP protocol, it can typically be considered that the maximum MTU value of an IP packet is 1500 bytes. In this case, by limiting the size of the encoded data stored in one packet to be equal to or less than 1500 bytes, the time of buffering the encoded data yet to be transmitted can be reduced, and the buffering delay can be reduced. When encoding a three-dimensional point cloud, the three-dimensional data encoding device may control the encoding so that the total size of geometry information and attribute information is equal to or less than a predetermined number of bytes or may control the encoding so that the size of each of geometry information and attribute information is equal to or less than a predetermined number of bytes. 
       FIG.  79    is a diagram illustrating an example of a point cloud obtained by LiDAR (rotating laser scan). 
     When encoding such point cloud data with low delay, the three-dimensional data encoding device may encode the point cloud data by grouping point clouds in the order in which data is scanned (that is, in the order in which data is measured). Specifically, when LiDAR measures point clouds by rotating and scanning in the order from top to bottom, the three-dimensional data encoding device divides the point clouds into predetermined data units (layers) in the order from top to bottom, and encodes each divisional data unit. For example, the three-dimensional data encoding device may group the point clouds into the predetermined number of layers (layers) in  FIG.  77   , that is, divide the point clouds into the first to n-th layers, and encode the data units on a group basis. In other words, the point clouds may be grouped on the basis of the sensor parameter β, and the data units may be encoded on a group basis. Each data unit is mapped onto a slice of G-PCC, and encoded on a slice basis. 
     For example, the region specified by the box in  FIG.  79    is the data unit located at the top of the plurality of data units included in the point cloud data, and is the first point cloud data obtained. The data unit is the first data unit to be encoded, and then the point cloud data of the data unit immediately below the data unit is encoded. 
       FIG.  80    is a diagram for describing an example of a method of dividing a point cloud. 
     As shown in a three-dimensional point cloud map in  FIG.  80   , a point cloud may be divided into a plurality of blocks each having a rectangular shape in plan view, in order to achieve encoding of point cloud data with low delay. Each slice (data unit) represents a cube, and can be independently transmitted (encoded) and decoded. A plurality of slices may be merged into a large map. 
     For example, the three-dimensional data encoding device recognize objects based on sensor information, such as a two-dimensional image or a three-dimensional point cloud, obtained with a drone or a camera at a high position, sequentially obtains three-dimensional point clouds starting with the three-dimensional point cloud of an intended object, and encode the three-dimensional point clouds in the order in which the three-dimensional point clouds are obtained. Alternatively, the three-dimensional data encoding device may determine areas in advance, sequentially obtain three-dimensional point clouds starting with the area including an intended object, and encode the three-dimensional point clouds in the order in which the three-dimensional point clouds are obtained. 
     For example, when monitoring automobiles, the three-dimensional data encoding device may recognize the models or license numbers of automobiles if the parking fee or toll varies with the model of the automobile or if the toll varies with the load capacity, or the three-dimensional data encoding device may recognize the size of an automobile as an object in order to compare the size of the automobile with an available parking space in the parking lot to determine whether the automobile can be parked in the space. 
     In encoding of three-dimensional data, the compression efficiency may be able to be improved by grouping data of point clouds highly correlated in terms of distribution, unevenness, or geometry of points, dividing the data into slices, and applying a prediction tree encoding or octree encoding on a slice basis. 
     Encoding of a three-dimensional point cloud (point cloud compression) using a prediction tree is one of methods of compressing a sparse point cloud. The compressibility may be able to be improved by performing a type of prediction, detecting a pattern, and applying the result to a prediction algorithm. 
       FIG.  81    is a diagram illustrating an example of division of point clouds into groups. 
     The three-dimensional data encoding device may group point clouds having similar three-dimensional shapes and encode the groups by using one prediction tree. The three-dimensional data encoding device may use different prediction trees for encoding different groups. That is, the three-dimensional data encoding device may encode each group using a different prediction tree. In the example shown in  FIG.  81   , groups 1 (group  12611  to group  12614 ) are formed by a point cloud arranged along the X axis (lateral direction: horizontal direction), and groups 2 (group  12621  to  12623 ) are formed by a point cloud arranged along the Y axis (longitudinal direction: vertical direction). 
     In addition to the grouping based on the patterns, groups 1 may be further segmented into groups 1a to 1d based on the positions of the classified point clouds and the size of the prediction tree. Similarly, groups 2 may also be further segmented into groups 2a to 2c based on the positions of the classified point clouds and the size of the prediction tree. The three-dimensional data encoding device can improve the compressibility by encoding the segmented groups using a different prediction tree for each group or by encoding the segmented groups using one integrated prediction tree as required. 
     The encoding efficiency can be improved if all points in the prediction tree have the same prediction pattern. To this end, it is important where to set the starting point (root point) of the prediction tree. The starting point of the prediction tree may be determined using an arbitrary starting point algorithm. 
     The tendency of the three-dimensional point clouds may be analyzed in advance, and the three-dimensional point clouds may be grouped based on the tendency. Alternatively, when performing slice division and encoding in real time, the encoding may be started at an arbitrary point, the tendency of the point clouds may be analyzed while encoding the point clouds, and the point clouds may be divided into slices when the tendency has changed. The tendency of the point clouds may be analyzed by comparing the center of gravity or dispersion of geometry information or color information of an encoded point with that of a newly obtained point, for example. 
     In generation of prediction trees, the three-dimensional data encoding device may combine or divide prediction trees based on the number of points (nodes) included in the prediction trees after generating the prediction trees in a predetermined manner.  FIG.  82    is a diagram for describing an example process in the case where prediction trees are combined or divided. 
     For example, when point cloud data in space  12634  is divided into prediction trees for group  12631  and group  12632 , and the number of points of at least one of the groups is smaller than a predetermined number, the three-dimensional data encoding device may combine group  12631  and group  12632  and generate a prediction tree for group  12633 . When combining group  12631  and group  12632 , the three-dimensional data encoding device combines the root node of any one of the groups with one of the nodes of the other group. For example, the three-dimensional data encoding device may compare the distances between the root node of group  12631  and a predetermined number of nodes (nodes stored in a buffer, for example) of group  12632  and the distances between the root node of group  12632  and a predetermined number of nodes (nodes stored in a buffer, for example) of group  12631 , and combine the nodes the distance between which is the shortest. For example, the three-dimensional data encoding device may combine group  12631  and group  12632  to generate group  12633  in such a manner that the root node of group  12631  becomes a child node of a node of group  12632 . 
     When a prediction tree for group  12633  is first generated, if the number of points of group  12633  is greater than a predetermined number, the three-dimensional data encoding device may divide the prediction tree into a prediction tree for group  12631  and a prediction tree for group  12632 . In this case, the three-dimensional data encoding device may divide group  12633  into group  12631  and group  12632  by decupling the nodes the distance between which is the greatest. 
     The combination and the division are not limited to the methods described herein, and can also be performed by analyzing the geometry information, the point distribution, or the attribute information. 
     By combining or dividing prediction trees in this way, the numbers of points included in the groups can be made more uniform, so that the reduction of the encoding efficiency due to a too small number of points or the increase of the processing amount due to a too large number of points can be prevented. 
     Next, a method of decoding encoded data obtained by encoding a prediction tree will be described. 
     In predictive encoding, an encoded prediction tree can be independently decoded. Therefore, the three-dimensional data decoding device can decode a particular prediction tree alone among the encoded data. 
     For example,  FIG.  83    is a diagram in which point clouds are grouped and surrounded by a line on a prediction tree basis. 
     One slice may include one tree structure (prediction tree) or may include a plurality of tree structures (prediction trees). Note that each slice is represented by a point cloud surrounded by a line in  FIG.  83   . 
     The three-dimensional data decoding device may determine a point cloud close to automobile  12640  to be a point cloud with high priority, and preferentially decode the slice (data unit) of the point cloud to those of point clouds with low priority. The three-dimensional data decoding device may determine a point cloud away from automobile  12640  to be a point cloud with low priority, and determine not to decode the slice (data unit) of the point cloud. 
     Thus, the three-dimensional data encoding device may encode geometry information of each prediction tree in advance so that the three-dimensional data encoding device can determine whether to decode a prediction tree based on the geometry information of the prediction tree. 
     The geometry information of the prediction tree may be geometry information of the root node of the prediction tree or geometry information of the end point of the prediction tree. Alternatively, the geometry information of the prediction tree may be the origin (minimum point) and the size (height, width, depth, or maximum values thereof) of a bounding box for the point cloud of the entire prediction tree. 
       FIG.  84    is a flowchart illustrating an example of a method of encoding a prediction tree. 
     The three-dimensional data encoding device generates one or more prediction trees, and encodes each of the one or more prediction trees (S 12601 ). 
     The three-dimensional data encoding device then signals geometry information of the prediction trees (S 12602 ). A specific example of the signaling will be described later. The geometry information is signaled for each of the one or more prediction tree. 
       FIG.  85    is a flowchart illustrating an example of a method of decoding a prediction tree using signaled geometry information. 
     The three-dimensional data decoding device obtains encoded data and geometry information of each prediction tree, and analyze the geometry information (S 12611 ). 
     The three-dimensional data decoding device selects a prediction tree to be decoded using the geometry information obtained by the analysis (S 12612 ). 
     The three-dimensional data decoding device decodes the selected prediction tree (S 12613 ). 
       FIG.  86    is a diagram illustrating an example of a syntax of tree information. 
     Tree information (tree_info) includes the number of prediction trees included in a data unit. The tree information also includes information indicating the coordinates of the root node of each prediction tree. 
     max_num_points_per_tree is maximum number information indicating the maximum value of the number of three-dimensional points that can be included in one prediction tree. num_tree_root_points indicates the number of items of geometry information of root nodes of prediction trees included in a data unit. num_tree_root_points may be the same as the number of prediction trees included in the data unit. tree_id is one or more items of identification information for identifying the one or more prediction trees. tree_id is provided for each prediction tree. 
     The tree information may include geometry information indicating the positions of the one or more prediction trees. The geometry information may be represented by root_x, root_y, and root_z. root_x, root_y, and root_z indicate position coordinates of the root node. Specifically, root_x, root_y, and root_z indicate position coordinates of the root node of the prediction tree indicated by tree_id. 
     In this way, the tree information may include the geometry information of the root node as the geometry information indicating the position of the prediction tree. The geometry information indicating the position of the prediction tree included in the tree information is not limited to the position coordinates of the root node of the prediction tree but may be information including any of minimum coordinates  12643  (or origin) of bounding box  12642  formed by three-dimensional point cloud  12641  forming the prediction tree and the size (height, width, or depth) of bounding box  12642 , such as that shown in  FIG.  87   . Bounding box  12642  is a virtual box surrounding three-dimensional point cloud  12641  forming a corresponding prediction tree. 
     The geometry information indicating the position of the prediction tree included in the tree information may be information using predetermined one or more three-dimensional points forming the prediction tree and may be a representative value, such as the center of gravity or median, of the geometry information of the predetermined one or more three-dimensional points. The geometry information indicating the position of the prediction tree included in the tree information may be geometry information of the end node of the prediction tree. 
       FIG.  88    shows an example of a data structure of encoded data. 
     The tree information (tree_info( )) may be stored in a header of the data unit or in a footer of the data unit. Note that the data unit may include one or more prediction trees (tree structures) generated using a plurality of three-dimensional points. That is, the three-dimensional data encoding device obtains a plurality of three-dimensional points, generates a data unit using the plurality of three-dimensional points, and generates a bitstream including the data unit and the footer of the data unit. The footer includes the tree information concerning one or more prediction trees. 
     The tree information may be stored in SEI or metadata that is different from the data unit. In that case, the tree information may include information of the prediction trees included in a plurality of data units altogether. That is, the tree information may be included in control information included in the bitstream. 
     When the tree information is known before encoding, the three-dimensional data encoding device may store the tree information in the data unit header. In that case, the three-dimensional data decoding device receives the data unit header before receiving the data of the data unit, so that the three-dimensional data decoding device can determine whether or not to decode the prediction tree in the data unit to be subsequently received based on the tree information included in the data unit header. 
     When determining a prediction tree while obtaining three-dimensional points, the three-dimensional data encoding device can derive the position coordinates of the root node or bounding information of the prediction tree after encoding. In that case, the three-dimensional data encoding device may store the tree information (tree_info) in the footer of the data unit after encoding the prediction tree. 
     In this way, the three-dimensional data encoding device can encode, with low delay, one or more three-dimensional points obtained while generating the prediction tree and deliver geometry information of the prediction tree determined in the course of the encoding. That is, the three-dimensional data encoding device can deliver encoded data obtained by encoding one or more three-dimensional points included in a prediction tree without waiting for completion of the determination of the geometry information of the prediction tree. Therefore, the three-dimensional data encoding device can perform encoding with low delay. 
     The three-dimensional data encoding device may indicate data position information for each prediction tree that indicates the data position of the top of the prediction tree. For example, the tree information may include data position information indicating the position of each of one or more prediction trees, which is the position (data position) on the data unit with respect to the top of the data unit. Therefore, a system layer in the subsequent stage to the encoding or the three-dimensional data decoding device can efficiently decode a prediction tree partially extracted from a plurality of prediction trees. 
     The tree information (tree_info( )) stored in the metadata, SEI, data unit header, or data unit footer in the bitstream may be stored in a system format. 
       FIG.  89    is a flowchart illustrating an example of a process of storing into a system format. 
     The device analyzes geometry information of each prediction tree signaled (S 12621 ). 
     The device then creates geometry information list for prediction trees, and generates a partial decoding table (S 12622 ). For example, the device generates, as a partial decoding list (partial decoding table), a list of geometry information of each prediction tree (or tree information including the geometry information) and tree_byte indicating the byte position of the data of each prediction tree. 
     As shown in  FIG.  90   , the partial decoding list is information in which geometry information of each prediction tree (or tree information including the geometry information) and data position information (such as tree_byte) indicating the byte position of the data of the prediction tree are associated with each other. In the partial decoding list, tree_id may further be associated with the geometry information of each prediction tree (or tree information including the geometry information) and the data position information (such as tree_byte) indicating the byte position of the data of the prediction tree. When tree_byte is indicated in the bitstream as the data position information, the partial decoding list may store tree_byte. When tree_byte is not indicated in the bitstream as the data position information, the bitstream may be decoded to measure the size. 
       FIG.  91    is a flowchart illustrating an example of a process of partially extracting a prediction tree from a system format. 
     The three-dimensional data decoding device determines geometry information or a region to be decoded (S 12631 ). 
     The three-dimensional data decoding device then analyzes the partial decoding table and identifies a prediction tree to be decoded through comparison with tree_info (S 12632 ). 
     The three-dimensional data decoding device then extracts data of the identified prediction tree using tree_byte (S 12633 ). 
     As stated above, the three-dimensional data encoding device according to the present embodiment performs the process shown in  FIG.  92   . The three-dimensional data encoding device obtains three-dimensional points (S 12641 ). Next, the three-dimensional data encoding device generates a data unit including one or more prediction trees, using the three-dimensional points (S 12642 ). Then, the three-dimensional data encoding device generates a bitstream including the data unit and control information (S 12643 ). The control information includes tree information about the one or more prediction trees. The tree information includes number information indicating a total number of prediction trees included in the data unit. 
     Accordingly, since the number information indicating the number of prediction trees included in a data unit is indicated, the three-dimensional data decoding device can effectively perform the decoding process for the data unit using the number information. 
     For example, the tree information further includes maximum number information indicating a maximum value of a total number of three-dimensional points that a single prediction tree can include. Accordingly, since the maximum number information is indicated, the three-dimensional data decoding device can effectively perform the decoding process for the data unit using the maximum number information. 
     For example, the tree information further includes one or more identification information items each identifying a corresponding one of the one or more prediction trees. Accordingly, since one or more prediction trees can be easily identified, the three-dimensional data decoding device can easily select a desired prediction tree using the identification information. 
     For example, the tree information further includes one or more position information items each indicating a position of a corresponding one of the one or more prediction trees. Accordingly, since position information of one or more prediction trees can be easily identified, the three-dimensional data decoding device can easily select a desired prediction tree using the position information. 
     For example, each of the one or more position information items includes position coordinates of a root node included in the corresponding one of the one or more prediction trees. 
     For example, each of the one or more position information items includes an origin and a size of a bounding box surrounding a three-dimensional point cloud constituting the corresponding one of the one or more prediction trees. 
     For example, the tree information further includes one or more data position information items each indicating a position of a corresponding one of the one or more prediction trees, the position being a position from a top of the data unit in the data unit. Accordingly, since the data positions of one or more prediction trees can be easily identified, the three-dimensional data decoding device can easily extract data of a desired prediction tree using the data position information. 
     For example, the control information is a footer of the data unit. 
     For example, the three-dimensional data encoding device includes a processor and memory, and the processor performs the above process using the memory. 
     The three-dimensional data decoding device according to the present embodiment performs the process shown in  FIG.  93   . The three-dimensional data decoding device obtains a bitstream including control information and a data unit that includes one or more prediction trees (S 12651 ). Next, the three-dimensional data decodes device decoding one of the one or more prediction trees included in the data unit, using the control information, to calculate three-dimensional points constituting the one of the one or more prediction trees (S 12652 ). The control information includes tree information about the one or more prediction trees. The tree information includes number information indicating a total number of prediction trees included in the data unit. 
     Accordingly, since the number information indicating the number of prediction trees included in a data unit is indicated, the three-dimensional data decoding device can effectively perform the decoding process for the data unit using the number information. 
     For example, the tree information further includes maximum number information indicating a maximum value of a total number of three-dimensional points that a single prediction tree can include. Accordingly, since the maximum number information is indicated, the three-dimensional data decoding device can effectively perform the decoding process for the data unit using the maximum number information. 
     For example, the tree information further includes one or more identification information items each identifying a corresponding one of the one or more prediction trees. Accordingly, since one or more prediction trees can be easily identified, the three-dimensional data decoding device can easily select a desired prediction tree using the identification information. 
     For example, the tree information further includes one or more position information items each indicating a position of a corresponding one of the one or more prediction trees. Accordingly, since position information of one or more prediction trees can be easily identified, the three-dimensional data decoding device can easily select a desired prediction tree using the position information. 
     For example, each of the one or more position information items includes position coordinates of a root node included in the corresponding one of the one or more prediction trees. 
     For example, each of the one or more position information items includes an origin and a size of a bounding box surrounding a three-dimensional point cloud constituting the corresponding one of the one or more prediction trees. 
     For example, the tree information further includes one or more data position information items each indicating a position of a corresponding one of the one or more prediction trees, the position being a position from a top of the data unit in the data unit. Accordingly, since the data positions of one or more prediction trees can be easily identified, the three-dimensional data decoding device can easily extract data of a desired prediction tree using the data position information. 
     For example, the control information is a footer of the data unit. 
     For example, the three-dimensional data decoding device includes a processor and memory, and the processor performs the above process using the memory. 
     Embodiment 6 
     The following describes the structure of three-dimensional data creation device  810  according to the present embodiment.  FIG.  94    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.  95    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.  96    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.  97    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.  98    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.  99    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.  100    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.  101    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.  102    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.  103    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.  104    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.  105    is a diagram illustrating a configuration of a system according to the present embodiment. The system illustrated in  FIG.  105    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.  106    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.  107    is a diagram illustrating a configuration of the sensor information collection system according to the present embodiment. As illustrated in  FIG.  107   , 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.  97   . 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 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 keypoints, 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 LoD0 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 LoD0 to LoD(M−1) required by the three-dimensional data decoding device. 
       FIG.  108    is a diagram illustrating the foregoing use case. In the example shown in  FIG.  108   , 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 LoD0. 
     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.  108   , 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 LoD0 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 LoD0 to LoD(M−1) required by the three-dimensional data decoding device is encoded. 
       FIG.  109    is a diagram illustrating the foregoing use case. In the example shown in  FIG.  109   , 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 LoD0. 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.  109   , 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.  110    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 (S 7303 ). 
     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.  111    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.  112    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,  73528 , and  7355 , point cloud data synthesizer  7353 , large data accumulator  7354 , comparator  7356 , and encoder  7357 . Edge  7360  includes sensors  7361 A and  73618 , point cloud data generators  7362 A and  73628 , synchronizer  7363 , encoders  7364 A and  73648 , 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  73628  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  73628 . 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  73618 . 
     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.  113    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.  113    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.  113   , 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 A1, 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.  114    is a flowchart of a point cloud selection process performed by this application.  FIG.  115    to  FIG.  117    are diagrams illustrating screen examples of the point cloud selection process. 
     As illustrated in  FIG.  115   , 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.  116    illustrates an example in the case where button  8663  for sensor 1 is pressed, and the point cloud of sensor 1 is presented.  FIG.  117    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. 
     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. 
     Although only some exemplary embodiments of the present disclosure have been described in detail above, those skilled in the art will readily appreciate that many modifications are possible in the exemplary embodiments without materially departing from the novel teachings and advantages of the present disclosure. Accordingly, all such modifications are intended to be included within the scope 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.