Patent Publication Number: US-2022238018-A1

Title: Three-dimensional information processing method and three-dimensional information processing device

Description:
BACKGROUND 
     1. Technical Field 
     The present disclosure relates to a three-dimensional information processing method and a three-dimensional information processing 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 vison 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 group in a three-dimensional space (for example, see “Octree-Based Progressive Geometry Coding of Point Clouds”, Eurographics Symposium on Point-Based Graphics (2006)). In the point cloud scheme, the positions and colors of a point group 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 group 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 MPEG-4 AVC and 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. 
     SUMMARY 
     A three-dimensional information processing method or a three-dimensional information processing device that processes such three-dimensional information is awaited to appropriately cope with an abnormality regarding three-dimensional position information in the event of its occurrence. 
     The present disclosure aims to provide a three-dimensional information processing method or a three-dimensional information processing device capable of appropriately coping with an abnormality regarding three-dimensional position information in the event of its occurrence. 
     The three-dimensional information processing method according to one aspect of the present disclosure includes: obtaining, via a communication channel, map data that includes first three-dimensional position information; generating second three-dimensional position information from information detected by a sensor; judging whether one of the first three-dimensional position information and the second three-dimensional position information is abnormal by performing, on one of the first three-dimensional position information and the second three-dimensional position information, a process of judging whether an abnormality is present; determining a coping operation to cope with the abnormality when one of the first three-dimensional position information and the second three-dimensional position information is judged to be abnormal; and executing a control that is required to perform the coping operation. 
     Note 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 an any combination of a system, a method, an integrated circuit, a computer program, and a recording medium. 
     The present disclosure is capable of providing a three-dimensional information processing method or a three-dimensional information processing device that copes with an abnormality regarding three-dimensional position information in the event of its occurrence. 
    
    
     
       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 showing the structure of encoded three-dimensional data according to Embodiment 1; 
         FIG. 2  is a diagram showing an example of prediction structures among SPCs that belong to the lowermost layer in a GOS according to Embodiment 1; 
         FIG. 3  is a diagram showing an example of prediction structures among layers according to Embodiment 1; 
         FIG. 4  is a diagram showing an example order of encoding GOSs according to Embodiment 1; 
         FIG. 5  is a diagram showing an example order of encoding GOSs according to Embodiment 1; 
         FIG. 6  is a block diagram of a three-dimensional data encoding device according to Embodiment 1; 
         FIG. 7  is a flowchart of encoding processes according to Embodiment 1; 
         FIG. 8  is a block diagram of a three-dimensional data decoding device according to Embodiment 1; 
         FIG. 9  is a flowchart of decoding processes according to Embodiment 1; 
         FIG. 10  is a diagram showing an example of meta information according to Embodiment 1; 
         FIG. 11  is a diagram showing an example structure of a SWLD according to Embodiment 2; 
         FIG. 12  is a diagram showing example operations performed by a server and a client according to Embodiment 2; 
         FIG. 13  is a diagram showing example operations performed by the server and a client according to Embodiment 2; 
         FIG. 14  is a diagram showing example operations performed by the server and the clients according to Embodiment 2; 
         FIG. 15  is a diagram showing example operations performed by the server and the clients according to Embodiment 2; 
         FIG. 16  is a block diagram of a three-dimensional data encoding device according to Embodiment 2; 
         FIG. 17  is a flowchart of encoding processes according to Embodiment 2; 
         FIG. 18  is a block diagram of a three-dimensional data decoding device according to Embodiment 2; 
         FIG. 19  is a flowchart of decoding processes according to Embodiment 2; 
         FIG. 20  is a diagram showing an example structure of a WLD according to Embodiment 2; 
         FIG. 21  is a diagram showing an example octree structure of the WLD according to Embodiment 2; 
         FIG. 22  is a diagram showing an example structure of a SWLD according to Embodiment 2; 
         FIG. 23  is a diagram showing an example octree structure of the SWLD according to Embodiment 2; 
         FIG. 24  is a schematic diagram showing three-dimensional data being transmitted/received between vehicles according to Embodiment 3; 
         FIG. 25  is a diagram showing an example of three-dimensional data transmitted between vehicles according to Embodiment 3; 
         FIG. 26  is a block diagram of a three-dimensional data creation device according to Embodiment 3; 
         FIG. 27  is a flowchart of the processes of creating three-dimensional data according to Embodiment 3; 
         FIG. 28  is a block diagram of a three-dimensional data transmission device according to Embodiment 3; 
         FIG. 29  is a flowchart of the processes of transmitting three-dimensional data according to Embodiment 3; 
         FIG. 30  is a block diagram of a three-dimensional data creation device according to Embodiment 3; 
         FIG. 31  is a flowchart of the processes of creating three-dimensional data according to Embodiment 3; 
         FIG. 32  is a block diagram of a three-dimensional data transmission device according to Embodiment 3; 
         FIG. 33  is a flowchart of the processes of transmitting three-dimensional data according to Embodiment 3; 
         FIG. 34  is a block diagram of a three-dimensional information processing device according to Embodiment 4; 
         FIG. 35  is a flowchart of a three-dimensional information processing method according to Embodiment 4; 
         FIG. 36  is a flowchart of a three-dimensional information processing method according to Embodiment 4; 
         FIG. 37  illustrates a configuration of a video information processing system; 
         FIG. 38  illustrates one example of a notification screen displayed when a camera is activated; 
         FIG. 39  illustrates an overall configuration of a content providing system that implements content distribution services; 
         FIG. 40  illustrates an overall configuration of a digital broadcasting system; 
         FIG. 41  illustrates one example of a smartphone; and 
         FIG. 42  is a block diagram illustrating an example of a configuration of a smartphone. 
     
    
    
     DETAILED DESCRIPTION OF THE EMBODIMENTS 
     While the use of encoded data such as that of a point cloud in an actual device or service requires random access to a desired spatial position or object, there has been no functionality for random access in encoded three-dimensional data, nor an encoding method therefor. 
     The present disclosure describes 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 capable of providing random access functionality for encoded three-dimensional data. 
     The three-dimensional data encoding method according to one aspect of the present disclosure is a three-dimensional data encoding method for encoding three-dimensional data, the method including: dividing the three-dimensional data into first processing units, each being a random access unit and being associated with three-dimensional coordinates; and encoding each of the first processing units to generate encoded data. 
     This enables random access on a first processing unit basis. The three-dimensional data encoding method is thus capable of providing random access functionality for encoded three-dimensional data. 
     For example, the three-dimensional data encoding method may include generating first information indicating the first processing units and the three-dimensional coordinates associated with each of the first processing units, and the encoded data may include the first information. 
     For example, the first information may further indicate at least one of an object, a time, and a data storage location that are associated with each of the first processing units. 
     For example, in the dividing, each of the first processing units may be further divided into second processing units, and in the encoding, each of the second processing units may be encoded. 
     For example, in the encoding, a current second processing unit among the second processing units included in a current first processing unit among the first processing units may be encoded by referring to another of the second processing units included in the current first processing unit. 
     With this, the encoding efficiency is increased by referring to another second processing unit. 
     For example, in the encoding, one of three types may be selected as a type of the current second processing unit, and the current second processing unit may be encoded in accordance with the type that has been selected, the three types being a first type in which another of the second processing units is not referred to, a second type in which another of the second processing units is referred to, and a third type in which other two of the second processing units are referred to. 
     For example, in the encoding, a frequency of selecting the first type may be changed in accordance with the number, or sparseness and denseness of objects included in the three-dimensional data. 
     This enables an adequate setting of random accessibility and encoding efficiency, which are in a tradeoff relationship. 
     For example, in the encoding, a size of the first processing units may be determined in accordance with the number, or sparseness and denseness of objects or dynamic objects included in the three-dimensional data. 
     This enables an adequate setting of random accessibility and encoding efficiency, which are in a tradeoff relationship. 
     For example, each of the first processing units may be spatially divided in a predetermined direction to have layers, each including at least one of the second processing units, and in the encoding, each of the second processing units may be encoded by referring to another of the second processing units included in an identical layer of the each of the second processing units or included in a lower layer of the identical layer. 
     This achieves an increased random accessibility to an important layer in a system, while preventing a decrease in the encoding efficiency. 
     For example, in the dividing, among the second processing units, a second processing unit including only a static object and a second processing unit including only a dynamic object may be assigned to different ones of the first processing units. 
     This enables easy control of dynamic objects and static objects. 
     For example, in the encoding, dynamic objects may be individually encoded, and encoded data of each of the dynamic objects may be associated with a second processing unit, among the second processing units, that includes only a static object. 
     This enables easy control of dynamic objects and static objects. 
     For example, in the dividing, each of the second processing units may be further divided into third processing units, and in the encoding, each of the third processing units may be encoded. 
     For example, each of the third processing units may include at least one voxel, which is a minimum unit in which position information is associated. 
     For example, each of the second processing units may include a keypoint group derived from information obtained by a sensor. 
     For example, the encoded data may include information indicating an encoding order of the first processing units. 
     For example, the encoded data may include information indicating a size of the first processing units. 
     For example, in the encoding, the first processing units may be encoded in parallel. 
     Also, the three-dimensional data decoding method according another aspect of the present disclosure is a three-dimensional data decoding method for decoding three-dimensional data, the method including: decoding each encoded data of first processing units, each being a random access unit and being associated with three-dimensional coordinates, to generate three-dimensional data of the first processing units. 
     This enables random access on a first processing unit basis. The three-dimensional data decoding method is thus capable of providing random access functionality for encoded three-dimensional data. 
     Also, the three-dimensional data encoding device according to still another aspect of the present disclosure is a three-dimensional data encoding device that encodes three-dimensional data that may include: a divider that divides the three-dimensional data into first processing units, each being a random access unit and being associated with three-dimensional coordinates; and an encoder that encodes each of the first processing units to generate encoded data. 
     This enables random access on a first processing unit basis. The three-dimensional data encoding device is thus capable of providing random access functionality for encoded three-dimensional data. 
     Also, the three-dimensional data decoding device according to still another aspect of the present disclosure is a three-dimensional data decoding device that decodes three-dimensional data that may include: a decoder that decodes each encoded data of first processing units, each being a random access unit and being associated with three-dimensional coordinates, to generate three-dimensional data of the first processing units. 
     This enables random access on a first processing unit basis. The three-dimensional data decoding device is thus capable of providing random access functionality for encoded three-dimensional data. 
     Note that the present disclosure, which is configured to divide a space for encoding, enables quantization, prediction, etc. of such space, and thus is effective also for the case where no random access is performed. 
     Also, the three-dimensional data encoding method according to one aspect of the present disclosure includes: extracting, from first three-dimensional data, second three-dimensional data having an amount of a feature greater than or equal to a threshold; and encoding the second three-dimensional data to generate first encoded three-dimensional data. 
     According to this three-dimensional data encoding method, first encoded three-dimensional data is generated that is obtained by encoding data having an amount of a feature greater than or equal to the threshold. This reduces the amount of encoded three-dimensional data compared to the case where the first three-dimensional data is encoded as it is. The three-dimensional data encoding method is thus capable of reducing the amount of data to be transmitted. 
     For example, the three-dimensional data encoding method may further include encoding the first three-dimensional data to generate second encoded three-dimensional data. 
     This three-dimensional data encoding method enables selective transmission of the first encoded three-dimensional data and the second encoded three-dimensional data, in accordance, for example, with the intended use, etc. 
     For example, the second three-dimensional data may be encoded by a first encoding method, and the first three-dimensional data may be encoded by a second encoding method different from the first encoding method. 
     This three-dimensional data encoding method enables the use of an encoding method suitable for each of the first three-dimensional data and the second three-dimensional data. 
     For example, of intra prediction and inter prediction, the inter prediction may be more preferentially performed in the first encoding method than in the second encoding method. 
     This three-dimensional data encoding method enables inter prediction to be more preferentially performed on the second three-dimensional data in which adjacent data items are likely to have low correlation. 
     For example, the first encoding method and the second encoding method may represent three-dimensional positions differently. 
     This three-dimensional data encoding method enables the use of a more suitable method to represent three-dimensional positions of three-dimensional data in consideration of the difference in the number of data items included. 
     For example, at least one of the first encoded three-dimensional data and the second encoded three-dimensional data may include an identifier indicating whether the at least one of the first encoded three-dimensional data and the second encoded three-dimensional data is encoded three-dimensional data obtained by encoding the first three-dimensional data or encoded three-dimensional data obtained by encoding part of the first three-dimensional data. 
     This enables the decoding device to readily judge whether the obtained encoded three-dimensional data is the first encoded three-dimensional data or the second encoded three-dimensional data. 
     For example, in the encoding of the second three-dimensional data, the second three-dimensional data may be encoded in a manner that the first encoded three-dimensional data has a smaller data amount than a data amount of the second encoded three-dimensional data. 
     This three-dimensional data encoding method enables the first encoded three-dimensional data to have a smaller data amount than the data amount of the second encoded three-dimensional data. 
     For example, in the extracting, data corresponding to an object having a predetermined attribute may be further extracted from the first three-dimensional data as the second three-dimensional data. 
     This three-dimensional data encoding method is capable of generating the first encoded three-dimensional data that includes data required by the decoding device. 
     For example, the three-dimensional data encoding method may further include sending, to a client, one of the first encoded three-dimensional data and the second encoded three-dimensional data in accordance with a status of the client. 
     This three-dimensional data encoding method is capable of sending appropriate data in accordance with the status of the client. 
     For example, the status of the client may include one of a communication condition of the client and a traveling speed of the client. 
     For example, the three-dimensional data encoding method may further include sending, to a client, one of the first encoded three-dimensional data and the second encoded three-dimensional data in accordance with a request from the client. 
     This three-dimensional data encoding method is capable of sending appropriate data in accordance with the request from the client. 
     Also, the three-dimensional data decoding method according to another aspect of the present disclosure includes: decoding, by a first decoding method, first encoded three-dimensional data obtained by encoding second three-dimensional data having an amount of a feature greater than or equal to a threshold, the second three-dimensional data having been extracted from first three-dimensional data; and decoding, by a second decoding method, second encoded three-dimensional data obtained by encoding the first three-dimensional data, the second decoding method being different from the first decoding method. 
     This three-dimensional data decoding method enables selective reception of the first encoded three-dimensional data obtained by encoding data having an amount of a feature greater than or equal to the threshold and the second encoded three-dimensional data, in accordance, for example, with the intended use, etc. The three-dimensional data decoding method is thus capable of reducing the amount of data to be transmitted. Such three-dimensional data decoding method further enables the use of a decoding method suitable for each of the first three-dimensional data and the second three-dimensional data. 
     For example, of intra prediction and inter prediction, the inter prediction may be more preferentially performed in the first decoding method than in the second decoding method. 
     This three-dimensional data decoding method enables inter prediction to be more preferentially performed on the second three-dimensional data in which adjacent data items are likely to have low correlation. 
     For example, the first decoding method and the second decoding method may represent three-dimensional positions differently. 
     This three-dimensional data decoding method enables the use of a more suitable method to represent three-dimensional positions of three-dimensional data in consideration of the difference in the number of data items included. 
     For example, at least one of the first encoded three-dimensional data and the second encoded three-dimensional data may include an identifier indicating whether the at least one of the first encoded three-dimensional data and the second encoded three-dimensional data is encoded three-dimensional data obtained by encoding the first three-dimensional data or encoded three-dimensional data obtained by encoding part of the first three-dimensional data, and the identifier may be referred to in identifying between the first encoded three-dimensional data and the second encoded three-dimensional data. 
     This enables judgment to be readily made of whether the obtained encoded three-dimensional data is the first encoded three-dimensional data or the second encoded three-dimensional data. 
     For example, the three-dimensional data decoding method may further include: notifying a server of a status of a client; and receiving one of the first encoded three-dimensional data and the second encoded three-dimensional data from the server, in accordance with the status of the client. 
     This three-dimensional data decoding method is capable of receiving appropriate data in accordance with the status of the client. 
     For example, the status of the client may include one of a communication condition of the client and a traveling speed of the client. 
     For example, the three-dimensional data decoding method may further include: making a request of a server for one of the first encoded three-dimensional data and the second encoded three-dimensional data; and receiving one of the first encoded three-dimensional data and the second encoded three-dimensional data from the server, in accordance with the request. 
     This three-dimensional data decoding method is capable of receiving appropriate data in accordance with the intended use. 
     Also, the three-dimensional data encoding device according to still another aspect of the present disclosure include: an extractor that extracts, from first three-dimensional data, second three-dimensional data having an amount of a feature greater than or equal to a threshold; and a first encoder that encodes the second three-dimensional data to generate first encoded three-dimensional data. 
     This three-dimensional data encoding device generates first encoded three-dimensional data by encoding data having an amount of a feature greater than or equal to the threshold. This reduces the amount data compared to the case where the first three-dimensional data is encoded as it is. The three-dimensional data encoding device is thus capable of reducing the amount of data to be transmitted. 
     Also, the three-dimensional data decoding device according to still another aspect of the present disclosure includes: a first decoder that decodes, by a first decoding method, first encoded three-dimensional data obtained by encoding second three-dimensional data having an amount of a feature greater than or equal to a threshold, the second three-dimensional data having been extracted from first three-dimensional data; and a second decoder that decodes, by a second decoding method, second encoded three-dimensional data obtained by encoding the first three-dimensional data, the second decoding method being different from the first decoding method. 
     This three-dimensional data decoding devices enables selective reception of the first encoded three-dimensional data obtained by encoding data having an amount of a feature greater than or equal to the threshold and the second encoded three-dimensional data, in accordance, for example, with the intended use, etc. The three-dimensional data decoding device is thus capable of reducing the amount of data to be transmitted. Such three-dimensional data decoding device further enables the use of a decoding method suitable for each of the first three-dimensional data and the second three-dimensional data. 
     Also, the three-dimensional data creation method according to one aspect of the present disclosure includes: creating first three-dimensional data from information detected by a sensor; receiving encoded three-dimensional data that is obtained by encoding second three-dimensional data; decoding the encoded three-dimensional data that has been received to obtain the second three-dimensional data; and merging the first three-dimensional data with the second three-dimensional data to create third three-dimensional data. 
     Such three-dimensional data creation method is capable of creating detailed three-dimensional data by use of the created first three-dimensional data and the received second three-dimensional data. 
     For example, in the merging, the first three-dimensional data may be merged with the second three-dimensional data to create the third three-dimensional data that is denser than the first three-dimensional data and the second three-dimensional data. 
     For example, the second three-dimensional data may be three-dimensional data that is generated by extracting, from fourth three-dimensional data, data having an amount of a feature greater than or equal to a threshold. 
     Such three-dimensional data creation method reduces the amount of three-dimensional data to be transmitted. 
     For example, the three-dimensional data creation method may further include searching for a transmission device that transmits the encoded three-dimensional data, and in the receiving, the encoded three-dimensional data may be received from the transmission device that has been searched out. 
     Such three-dimensional data creation method is, for example, capable of searching for a transmission device having necessary three-dimensional data. 
     For example, the three-dimensional data creation method may further include: determining a request range that is a range of a three-dimensional space, three-dimensional data of which is requested; and transmitting information indicating the request range to the transmission device, wherein the second three-dimensional data may include the three-dimensional data of the request range. 
     Such three-dimensional data creation method is capable of receiving necessary three-dimensional data, while reducing the amount of three-dimensional data to be transmitted. 
     For example, in the determining, a spatial range that includes an occlusion region undetectable by the sensor may be determined as the request range. 
     The three-dimensional data transmission method according to another aspect of the present disclosure includes: creating fifth three-dimensional data from information detected by a sensor; extracting part of the fifth three-dimensional data to create sixth three-dimensional data; encoding the sixth three-dimensional data to generate encoded three-dimensional data; and transmitting the encoded three-dimensional data. 
     Such three-dimensional data transmission method is capable of transmitting self-created three-dimensional data to another device, while reducing the amount of three-dimensional data to be transmitted. 
     For example, in the creating, the fifth three-dimensional data may be created by creating seventh three-dimensional data from the information detected by the sensor, and by extracting data having an amount of a feature greater than or equal to a threshold from the seventh three-dimensional data. 
     Such three-dimensional data transmission method reduces the amount of three-dimensional data to be transmitted. 
     For example, the three-dimensional data transmission method may further include: receiving, from a reception device, information indicating a request range that is a range of a three-dimensional space, three-dimensional data of which is requested, wherein in the extracting, the sixth three-dimensional data may be created by extracting the three-dimensional data of the request range from the fifth three-dimensional data, and in the transmitting, the encoded three-dimensional data may be transmitted to the reception device. 
     Such three-dimensional data transmission method reduces the amount of three-dimensional data to be transmitted. 
     Also, the three-dimensional data creation device according to still another aspect of the present disclosure includes: a creator that creates first three-dimensional data from information detected by a sensor; a receiver that receives encoded three-dimensional data that is obtained by encoding second three-dimensional data; a decoder that decodes the encoded three-dimensional data that has been received to obtain the second three-dimensional data; and a merger that merges the first three-dimensional data with the second three-dimensional data to create third three-dimensional data. 
     Such three-dimensional data creation device is capable of creating detailed third three-dimensional data by use of the created first three-dimensional data and the received second three-dimensional data. 
     Also, the three-dimensional data transmission device according to still another aspect of the present disclosure includes: a creator that creates fifth three-dimensional data from information detected by a sensor; an extractor that extracts part of the fifth three-dimensional data to create sixth three-dimensional data; an encoder that encodes the sixth three-dimensional data to generate encoded three-dimensional data; and a transmitter that transmits the encoded three-dimensional data. 
     Such three-dimensional data transmission device is capable of transmitting self-created three-dimensional data to another device, while reducing the amount of three-dimensional data to be transmitted. 
     Also, the three-dimensional information processing method according one aspect of the present disclosure includes: obtaining, via a communication channel, map data that includes first three-dimensional position information; generating second three-dimensional position information from information detected by a sensor; judging whether one of the first three-dimensional position information and the second three-dimensional position information is abnormal by performing, on one of the first three-dimensional position information and the second three-dimensional position information, a process of judging whether an abnormality is present; determining a coping operation to cope with the abnormality when one of the first three-dimensional position information and the second three-dimensional position information is judged to be abnormal; and executing a control that is required to perform the coping operation. 
     Such three-dimensional information processing method is capable of detecting an abnormality regarding one of the first three-dimensional position information and the second three-dimensional position information, and performing a coping operation therefor. 
     For example, the first three-dimensional position information may include a plurality of random access units, each of which is an assembly of at least one subspace and is individually decodable, the at least one subspace having three-dimensional coordinates information and serving as a unit in which each of the plurality of random access units is encoded. 
     Such three-dimensional information processing method is capable of reducing the data amount of the first three-dimensional position information to be obtained. 
     For example, the first three-dimensional position information may be data obtained by encoding keypoints, each of which has an amount of a three-dimensional feature greater than or equal to a predetermined threshold. 
     Such three-dimensional information processing method is capable of reducing the data amount of the first three-dimensional position information to be obtained. 
     For example, the judging may include judging whether the first three-dimensional position information is obtainable via the communication channel, and when the first three-dimensional position information is unobtainable via the communication channel, judging the first three-dimensional position information to be abnormal. 
     Such three-dimensional information processing method is capable of performing an appropriate coping operation in accordance with communication conditions, etc., when the first three-dimensional position information is unobtainable. 
     For example, the three-dimensional information processing method may further include: estimating a location of a mobile object having the sensor by use of the first three-dimensional position information and the second three-dimensional position information. The judging may include predicting whether the mobile object will enter an area in which communication conditions are poor. In the executing of the control, the mobile object may obtain the first three-dimensional position information before entering the area in which the communication conditions are poor, when the mobile object is predicted to enter the area. 
     Such three-dimensional information processing method is capable of obtaining the first three-dimensional position information in advance, when there is a possibility that the first three-dimensional position information may be unobtainable. 
     For example, the executing of the control may include obtaining, via the communication channel, third three-dimensional position information having a narrower range than a range of the first three-dimensional position information, when the first three-dimensional position information is unobtainable via the communication channel. 
     Such three-dimensional information processing method is capable of reducing the data amount of data to be obtained via a communication channel, thereby obtaining the three-dimensional position information even when communication conditions are poor. 
     For example, the three-dimensional information processing method may further include: estimating a location of a mobile object having the sensor by use of the first three-dimensional position information and the second three-dimensional position information. The executing of the control may include obtaining, via the communication channel, map data including two-dimensional position information, when the first three-dimensional position information is unobtainable via the communication channel, and estimating the location of the mobile object having the sensor by use of the two-dimensional position information and the second three-dimensional position information. 
     Such three-dimensional information processing method is capable of reducing the data amount of data to be obtained via a communication channel, thereby obtaining the three-dimensional position information even when communication conditions are poor. 
     For example, the three-dimensional information processing method may further include: performing automatic operation of the mobile object by use of the location having been estimated. The judging may further include judging whether to perform the automatic operation of the mobile object by use of the location of the mobile object, based on an environment in which the mobile object is traveling, the location having been estimated by use of the two-dimensional position information and the second three-dimensional position information. 
     Such three-dimensional information processing method is capable of judging whether to continue automatic operation, in accordance with an environment in which the mobile object is traveling. 
     For example, the three-dimensional information processing method may further include: performing automatic operation of the mobile object by use of the location having been estimated. The executing of the control may include switching a mode of the automatic operation to another based on an environment in which the mobile object is traveling. 
     Such three-dimensional information processing method is capable of setting an appropriate automatic operation mode, in accordance with an environment in which the mobile object is traveling. 
     For example, the judging may include judging whether the first three-dimensional position information has integrity, and when the first three-dimensional position information has no integrity, judging the first three-dimensional position information to be abnormal. 
     Such three-dimensional information processing method is capable of performing an appropriate coping operation, when, for example, the first three-dimensional position information is corrupt. 
     For example, the judging may include judging whether a data accuracy is higher than or equal to a reference value, and when the data accuracy is not higher than or equal to the reference value, judging the second three-dimensional position information to be abnormal, the data accuracy being an accuracy of the second three-dimensional position information having been generated. 
     Such three-dimensional information processing method is capable of performing an appropriate coping operation, when the accuracy of the second three-dimensional position information is low. 
     For example, the executing of the control may include generating fourth three-dimensional position information from information detected by an alternative sensor different from the sensor, when the data accuracy of the second three-dimensional position information having been generated is not higher than or equal to the reference value. 
     Such three-dimensional information processing method is capable of obtaining three-dimensional position information by use of an alternative sensor, when, for example, the sensor has trouble. 
     For example, the three-dimensional information processing method may further include: estimating a location of a mobile object having the sensor by use of the first three-dimensional position information and the second three-dimensional position information; and performing automatic operation of the mobile object by use of the location having been estimated. The executing of the control may include switching a mode of the automatic operation to another when the data accuracy of the second three-dimensional position information having been generated is not higher than or equal to the reference value. 
     Such three-dimensional information processing method is capable of performing an appropriate coping operation, when the accuracy of the second three-dimensional position information is low. 
     For example, the executing of the control may include calibrating an operation of the sensor, when the data accuracy of the second three-dimensional position information having been generated is not higher than or equal to the reference value. 
     Such three-dimensional information processing method is capable of increasing the accuracy of the second three-dimensional position information, when the accuracy of the second three-dimensional position information is low. 
     Also, the three-dimensional information processing device according to another aspect of the present disclosure includes: an obtainer that obtains, via a communication channel, map data that includes first three-dimensional position information; a generator that generates second three-dimensional position information from information detected by a sensor; a judgment unit that judges whether one of the first three-dimensional position information and the second three-dimensional position information is abnormal by performing, on one of the first three-dimensional position information and the second three-dimensional position information, a process of judging whether an abnormality is present; a determiner that determines a coping operation to cope with the abnormality when one of the first three-dimensional position information and the second three-dimensional position information is judged to be abnormal; and an operation controller that executes a control required to perform the coping operation. 
     Such three-dimensional information processing device is capable of detecting an abnormality regarding one of the first three-dimensional position information and the second three-dimensional position information, and performing a coping operation therefor. 
     Note 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 an any combination of a system, a method, an integrated circuit, a computer program, and a recording medium. 
     The following describes embodiments with reference to the drawings. Note that the following embodiments show exemplary embodiments of the present disclosure. The numerical values, shapes, materials, structural components, the arrangement and connection of the structural components, steps, the processing order of the steps, etc. shown in the following embodiments are mere examples, and thus are not intended to limit the present disclosure. Of the structural components described in the following embodiments, structural components not recited in any one of the independent claims that indicate the broadest concepts will be described as optional structural components. 
     Embodiment 1 
     First, the data structure of encoded three-dimensional data (hereinafter also referred to as encoded data) according to the present embodiment will be described.  FIG. 1  is a diagram showing the structure of encoded three-dimensional data according to the present embodiment. 
     In the present embodiment, a three-dimensional space is divided into spaces (SPCs), which correspond to pictures in moving picture encoding, and the three-dimensional data is encoded on a SPC-by-SPC basis. Each SPC is further divided into volumes (VLMs), which correspond to macroblocks, etc. in moving picture encoding, and predictions and transforms are performed on a VLM-by-VLM basis. Each volume includes a plurality of voxels (VXLs), each being a minimum unit in which position coordinates are associated. Note that prediction is a process of generating predictive three-dimensional data analogous to a current processing unit by referring to another processing unit, and encoding a differential between the predictive three-dimensional data and the current processing unit, as in the case of predictions performed on two-dimensional images. Such prediction includes not only spatial prediction in which another prediction unit corresponding to the same time is referred to, but also temporal prediction in which a prediction unit corresponding to a different time is referred to. 
     When encoding a three-dimensional space represented by point group data such as a point cloud, for example, the three-dimensional data encoding device (hereinafter also referred to as the encoding device) encodes the points in the point group or points included in the respective voxels in a collective manner, in accordance with a voxel size. Finer voxels enable a highly-precise representation of the three-dimensional shape of a point group, while larger voxels enable a rough representation of the three-dimensional shape of a point group. 
     Note that the following describes the case where three-dimensional data is a point cloud, but three-dimensional data is not limited to a point cloud, and thus three-dimensional data of any format may be employed. 
     Also note that voxels with a hierarchical structure may be used. In such a case, when the hierarchy includes n levels, whether a sampling point is included in the n−1th level or its lower levels (the lower levels of the n-th level) may be sequentially indicated. For example, when only the n-th level is decoded, and the n−1th level or its lower levels include a sampling point, the n-th level can be decoded on the assumption that a sampling point is included at the center of a voxel in the n-th level. 
     Also, the encoding device obtains point group data, using, for example, a distance sensor, a stereo camera, a monocular camera, a gyroscope sensor, or an inertial sensor. 
     As in the case of moving picture encoding, each SPC is classified into one of at least the three prediction structures that include: intra SPC (I-SPC), which is individually decodable; predictive SPC (P-SPC) capable of only a unidirectional reference; and bidirectional SPC (B-SPC) capable of bidirectional references. Each SPC includes two types of time information: decoding time and display time. 
     Furthermore, as shown in  FIG. 1 , a processing unit that includes a plurality of SPCs is a group of spaces (GOS), which is a random access unit. Also, a processing unit that includes a plurality of GOSs is a world (WLD). 
     The spatial region occupied by each world is associated with an absolute position on earth, by use of, for example, GPS, or latitude and longitude information. Such position information is stored as meta-information. Note that meta-information may be included in encoded data, or may be transmitted separately from the encoded data. 
     Also, inside a GOS, all SPCs may be three-dimensionally adjacent to one another, or there may be a SPC that is not three-dimensionally adjacent to another SPC. 
     Note that the following also describes processes such as encoding, decoding, and reference to be performed on three-dimensional data included in processing units such as GOS, SPC, and VLM, simply as performing encoding/to encode, decoding/to decode, referring to, etc. on a processing unit. Also note that three-dimensional data included in a processing unit includes, for example, at least one pair of a spatial position such as three-dimensional coordinates and an attribute value such as color information. 
     Next, the prediction structures among SPCs in a GOS will be described. A plurality of SPCs in the same GOS or a plurality of VLMs in the same SPC occupy mutually different spaces, while having the same time information (the decoding time and the display time). 
     A SPC in a GOS that comes first in the decoding order is an I-SPC. GOSs come in two types: closed GOS and open GOS. A closed GOS is a GOS in which all SPCs in the GOS are decodable when decoding starts from the first I-SPC. Meanwhile, an open GOS is a GOS in which a different GOS is referred to in one or more SPCs preceding the first I-SPC in the GOS in the display time, and thus cannot be singly decoded. 
     Note that in the case of encoded data of map information, for example, a WLD is sometimes decoded in the backward direction, which is opposite to the encoding order, and thus backward reproduction is difficult when GOSs are interdependent. In such a case, a closed GOS is basically used. 
     Each GOS has a layer structure in height direction, and SPCs are sequentially encoded or decoded from SPCs in the bottom layer. 
       FIG. 2  is a diagram showing an example of prediction structures among SPCs that belong to the lowermost layer in a GOS.  FIG. 3  is a diagram showing an example of prediction structures among layers. 
     A GOS includes at least one I-SPC. Of the objects in a three-dimensional space, such as a person, an animal, a car, a bicycle, a signal, and a building serving as a landmark, a small-sized object is especially effective when encoded as an I-SPC. When decoding a GOS at a low throughput or at a high speed, for example, the three-dimensional data decoding device (hereinafter also referred to as the decoding device) decodes only I-SPC(s) in the GOS. 
     The encoding device may also change the encoding interval or the appearance frequency of I-SPCs, depending on the degree of sparseness and denseness of the objects in a WLD. 
     In the structure shown in  FIG. 3 , the encoding device or the decoding device encodes or decodes a plurality of layers sequentially from the bottom layer (layer 1). This increases the priority of data on the ground and its vicinity, which involve a larger amount of information, when, for example, a self-driving car is concerned. 
     Regarding encoded data used for a drone, for example, encoding or decoding may be performed sequentially from SPCs in the top layer in a GOS in height direction. 
     The encoding device or the decoding device may also encode or decode a plurality of layers in a manner that the decoding device can have a rough grasp of a GOS first, and then the resolution is gradually increased. The encoding device or the decoding device may perform encoding or decoding in the order of layers 3, 8, 1, 9 . . . , for example. 
     Next, the handling of static objects and dynamic objects will be described. 
     A three-dimensional space includes scenes or still objects such as a building and a road (hereinafter collectively referred to as static objects), and objects with motion such as a car and a person (hereinafter collectively referred to as dynamic objects). Object detection is separately performed by, for example, extracting keypoints from point cloud data, or from video of a camera such as a stereo camera. In this description, an example method of encoding a dynamic object will be described. 
     A first method is a method in which a static object and a dynamic object are encoded without distinction. A second method is a method in which a distinction is made between a static object and a dynamic object on the basis of identification information. 
     For example, a GOS is used as an identification unit. In such a case, a distinction is made between a GOS that includes SPCs constituting a static object and a GOS that includes SPCs constituting a dynamic object, on the basis of identification information stored in the encoded data or stored separately from the encoded data. 
     Alternatively, a SPC may be used as an identification unit. In such a case, a distinction is made between a SPC that includes VLMs constituting a static object and a SPC that includes VLMs constituting a dynamic object, on the basis of the identification information thus described. 
     Alternatively, a VLM or a VXL may be used as an identification unit. In such a case, a distinction is made between a VLM or a VXL that includes a static object and a VLM or a VXL that includes a dynamic object, on the basis of the identification information thus described. 
     The encoding device may also encode a dynamic object as at least one VLM or SPC, and may encode a VLM or a SPC including a static object and a SPC including a dynamic object as mutually different GOSs. When the GOS size is variable depending on the size of a dynamic object, the encoding device separately stores the GOS size as meta-information. 
     The encoding device may also encode a static object and a dynamic object separately from each other, and may superimpose the dynamic object onto a world constituted by static objects. In such a case, the dynamic object is constituted by at least one SPC, and each SPC is associated with at least one SPC constituting the static object onto which the each SPC is to be superimposed. Note that a dynamic object may be represented not by SPC(s) but by at least one VLM or VXL. 
     The encoding device may also encode a static object and a dynamic object as mutually different streams. 
     The encoding device may also generate a GOS that includes at least one SPC constituting a dynamic object. The encoding device may further set the size of a GOS including a dynamic object (GOS_M) and the size of a GOS including a static object corresponding to the spatial region of GOS_M at the same size (such that the same spatial region is occupied). This enables superimposition to be performed on a GOS-by-GOS basis. 
     SPC(s) included in another encoded GOS may be referred to in a P-SPC or a B-SPC constituting a dynamic object. In the case where the position of a dynamic object temporally changes, and the same dynamic object is encoded as an object in a GOS corresponding to a different time, referring to SPC(s) across GOSs is effective in terms of compression rate. 
     The first method and the second method may be selected in accordance with the intended use of encoded data. When encoded three-dimensional data is used as a map, for example, a dynamic object is desired to be separated, and thus the encoding device uses the second method. Meanwhile, the encoding device uses the first method when the separation of a dynamic object is not required such as in the case where three-dimensional data of an event such as a concert and a sports event is encoded. 
     The decoding time and the display time of a GOS or a SPC are storable in encoded data or as meta-information. All static objects may have the same time information. In such a case, the decoding device may determine the actual decoding time and display time. Alternatively, a different value may be assigned to each GOS or SPC as the decoding time, and the same value may be assigned as the display time. Furthermore, as in the case of the decoder model in moving picture encoding such as Hypothetical Reference Decoder (HRD) compliant with HEVC, a model may be employed that ensures that a decoder can perform decoding without fail by having a buffer of a predetermined size and by reading a bitstream at a predetermined bit rate in accordance with the decoding times. 
     Next, the topology of GOSs in a world will be described. The coordinates of the three-dimensional space in a world are represented by the three coordinate axes (x axis, y axis, and z axis) that are orthogonal to one another. A predetermined rule set for the encoding order of GOSs enables encoding to be performed such that spatially adjacent GOSs are contiguous in the encoded data. In an example shown in  FIG. 4 , for example, GOSs in the x and z planes are successively encoded. After the completion of encoding all GOSs in certain x and z planes, the value of the y axis is updated. Stated differently, the world expands in the y axis direction as the encoding progresses. The GOS index numbers are set in accordance with the encoding order. 
     Here, the three-dimensional spaces in the respective worlds are previously associated one-to-one with absolute geographical coordinates such as GPS coordinates or latitude/longitude coordinates. Alternatively, each three-dimensional space may be represented as a position relative to a previously set reference position. The directions of the x axis, the y axis, and the z axis in the three-dimensional space are represented by directional vectors that are determined on the basis of the latitudes and the longitudes, etc. Such directional vectors are stored together with the encoded data as meta-information. 
     GOSs have a fixed size, and the encoding device stores such size as meta-information. The GOS size may be changed depending on, for example, whether it is an urban area or not, or whether it is inside or outside of a room. Stated differently, the GOS size may be changed in accordance with the amount or the attributes of objects with information values. Alternatively, in the same world, the encoding device may adaptively change the GOS size or the interval between I-SPCs in GOSs in accordance with the object density, etc. For example, the encoding device sets the GOS size to smaller and the interval between I-SPCs in GOSs to shorter, as the object density is higher. 
     In an example shown in  FIG. 5 , to enable random access with a finer granularity, a GOS with a high object density is partitioned into the regions of the third to tenth GOSs. Note that the seventh to tenth GOSs are located behind the third to sixth GOSs. 
     Next, the structure and the operation flow of the three-dimensional data encoding device according to the present embodiment will be described.  FIG. 6  is a block diagram of three-dimensional data encoding device  100  according to the present embodiment.  FIG. 7  is a flowchart of an example operation performed by three-dimensional data encoding device  100 . 
     Three-dimensional data encoding device  100  shown in  FIG. 6  encodes three-dimensional data  111 , thereby generating encoded three-dimensional data  112 . Such three-dimensional data encoding device  100  includes obtainer  101 , encoding region determiner  102 , divider  103 , and encoder  104 . 
     As shown in  FIG. 7 , first, obtainer  101  obtains three-dimensional data  111 , which is point group data (S 101 ). 
     Next, encoding region determiner  102  determines a current region for encoding from among spatial regions corresponding to the obtained point group data (S 102 ). For example, in accordance with the position of a user or a vehicle, encoding region determiner  102  determines, as the current region, a spatial region around such position. 
     Next, divider  103  divides the point group data included in the current region into processing units. The processing units here means units such as GOSs and SPCs described above. The current region here corresponds to, for example, a world described above. More specifically, divider  103  divides the point group data into processing units on the basis of a predetermined GOS size, or the presence/absence/size of a dynamic object (S 103 ). Divider  103  further determines the starting position of the SPC that comes first in the encoding order in each GOS. 
     Next, encoder  104  sequentially encodes a plurality of SPCs in each GOS, thereby generating encoded three-dimensional data  112  (S 104 ). 
     Note that although an example is described here in which the current region is divided into GOSs and SPCs, after which each GOS is encoded, the processing steps are not limited to this order. For example, steps may be employed in which the structure of a single GOS is determined, which is followed by the encoding of such GOS, and then the structure of the subsequent GOS is determined. 
     As thus described, three-dimensional data encoding device  100  encodes three-dimensional data  111 , thereby generating encoded three-dimensional data  112 . More specifically, three-dimensional data encoding device  100  divides three-dimensional data into first processing units (GOSs), each being a random access unit and being associated with three-dimensional coordinates, divides each of the first processing units (GOSs) into second processing units (SPCs), and divides each of the second processing units (SPCs) into third processing units (VLMs). Each of the third processing units (VLMs) includes at least one voxel (VXL), which is the minimum unit in which position information is associated. 
     Next, three-dimensional data encoding device  100  encodes each of the first processing units (GOSs), thereby generating encoded three-dimensional data  112 . More specifically, three-dimensional data encoding device  100  encodes each of the second processing units (SPCs) in each of the first processing units (GOSs). Three-dimensional data encoding device  100  further encodes each of the third processing units (VLMs) in each of the second processing units (SPCs). 
     When a current first processing unit (GOS) is a closed GOS, for example, three-dimensional data encoding device  100  encodes a current second processing unit (SPC) included in such current first processing unit (GOS) by referring to another second processing unit (SPC) included in the current first processing unit (GOS). Stated differently, three-dimensional data encoding device  100  refers to no second processing unit (SPC) included in a first processing unit (GOS) that is different from the current first processing unit (GOS). 
     Meanwhile, when a current first processing unit (GOS) is an open GOS, three-dimensional data encoding device  100  encodes a current second processing unit (SPC) included in such current first processing unit (GOS) by referring to another second processing unit (SPC) included in the current first processing unit (GOS) or a second processing unit (SPC) included in a first processing unit (GOS) that is different from the current first processing unit (GOS). 
     Also, three-dimensional data encoding device  100  selects, as the type of a current second processing unit (SPC), one of the following: a first type (I-SPC) in which another second processing unit (SPC) is not referred to; a second type (P-SPC) in which another single second processing unit (SPC) is referred to; and a third type in which other two second processing units (SPC) are referred to. Three-dimensional data encoding device  100  encodes the current second processing unit (SPC) in accordance with the selected type. 
     Next, the structure and the operation flow of the three-dimensional data decoding device according to the present embodiment will be described.  FIG. 8  is a block diagram of three-dimensional data decoding device  200  according to the present embodiment.  FIG. 9  is a flowchart of an example operation performed by three-dimensional data decoding device  200 . 
     Three-dimensional data decoding device  200  shown in  FIG. 8  decodes encoded three-dimensional data  211 , thereby generating decoded three-dimensional data  212 . Encoded three-dimensional data  211  here is, for example, encoded three-dimensional data  112  generated by three-dimensional data encoding device  100 . Such three-dimensional data decoding device  200  includes obtainer  201 , decoding start GOS determiner  202 , decoding SPC determiner  203 , and decoder  204 . 
     First, obtainer  201  obtains encoded three-dimensional data  211  (S 201 ). Next, decoding start GOS determiner  202  determines a current GOS for decoding (S 202 ). More specifically, decoding start GOS determiner  202  refers to meta-information stored in encoded three-dimensional data  211  or stored separately from the encoded three-dimensional data to determine, as the current GOS, a GOS that includes a SPC corresponding to the spatial position, the object, or the time from which decoding is to start. 
     Next, decoding SPC determiner  203  determines the type(s) (I, P, and/or B) of SPCs to be decoded in the GOS (S 203 ). For example, decoding SPC determiner  203  determines whether to (1) decode only I-SPC(s), (2) to decode I-SPC(s) and P-SPCs, or (3) to decode SPCs of all types. Note that the present step may not be performed, when the type(s) of SPCs to be decoded are previously determined such as when all SPCs are previously determined to be decoded. 
     Next, decoder  204  obtains an address location within encoded three-dimensional data  211  from which a SPC that comes first in the GOS in the decoding order (the same as the encoding order) starts. Decoder  204  obtains the encoded data of the first SPC from the address location, and sequentially decodes the SPCs from such first SPC (S 204 ). Note that the address location is stored in the meta-information, etc. 
     Three-dimensional data decoding device  200  decodes decoded three-dimensional data  212  as thus described. More specifically, three-dimensional data decoding device  200  decodes each encoded three-dimensional data  211  of the first processing units (GOSs), each being a random access unit and being associated with three-dimensional coordinates, thereby generating decoded three-dimensional data  212  of the first processing units (GOSs). Even more specifically, three-dimensional data decoding device  200  decodes each of the second processing units (SPCs) in each of the first processing units (GOSs). Three-dimensional data decoding device  200  further decodes each of the third processing units (VLMs) in each of the second processing units (SPCs). 
     The following describes meta-information for random access. Such meta-information is generated by three-dimensional data encoding device  100 , and included in encoded three-dimensional data  112  ( 211 ). 
     In the conventional random access for a two-dimensional moving picture, decoding starts from the first frame in a random access unit that is close to a specified time. Meanwhile, in addition to times, random access to spaces (coordinates, objects, etc.) is assumed to be performed in a world. 
     To enable random access to at least three elements of coordinates, objects, and times, tables are prepared that associate the respective elements with the GOS index numbers. Furthermore, the GOS index numbers are associated with the addresses of the respective first I-SPCs in the GOSs.  FIG. 10  is a diagram showing example tables included in the meta-information. Note that not all the tables shown in  FIG. 10  are required to be used, and thus at least one of the tables is used. 
     The following describes an example in which random access is performed from coordinates as a starting point. To access the coordinates (x2, y2, and z2), the coordinates-GOS table is first referred to, which indicates that the point corresponding to the coordinates (x2, y2, and z2) is included in the second GOS. Next, the GOS-address table is referred to, which indicates that the address of the first I-SPC in the second GOS is addr(2). As such, decoder  204  obtains data from this address to start decoding. 
     Note that the addresses may either be logical addresses or physical addresses of an HDD or a memory. Alternatively, information that identifies file segments may be used instead of addresses. File segments are, for example, units obtained by segmenting at least one GOS, etc. 
     When an object spans across a plurality of GOSs, the object-GOS table may show a plurality of GOSs to which such object belongs. When such plurality of GOSs are closed GOSs, the encoding device and the decoding device can perform encoding or decoding in parallel. Meanwhile, when such plurality of GOSs are open GOSs, a higher compression efficiency is achieved by the plurality of GOSs referring to each other. 
     Example objects include a person, an animal, a car, a bicycle, a signal, and a building serving as a landmark. For example, three-dimensional data encoding device  100  extracts keypoints specific to an object from a three-dimensional point cloud, etc., when encoding a world, and detects the object on the basis of such keypoints to set the detected object as a random access point. 
     As thus described, three-dimensional data encoding device  100  generates first information indicating a plurality of first processing units (GOSs) and the three-dimensional coordinates associated with the respective first processing units (GOSs). Encoded three-dimensional data  112  ( 211 ) includes such first information. The first information further indicates at least one of objects, times, and data storage locations that are associated with the respective first processing units (GOSs). 
     Three-dimensional data decoding device  200  obtains the first information from encoded three-dimensional data  211 . Using such first information, three-dimensional data decoding device  200  identifies encoded three-dimensional data  211  of the first processing unit that corresponds to the specified three-dimensional coordinates, object, or time, and decodes encoded three-dimensional data  211 . 
     The following describes an example of other meta-information. In addition to the meta-information for random access, three-dimensional data encoding device  100  may also generate and store meta-information as described below, and three-dimensional data decoding device  200  may use such meta-information at the time of decoding. 
     When three-dimensional data is used as map information, for example, a profile is defined in accordance with the intended use, and information indicating such profile may be included in meta-information. For example, a profile is defined for an urban or a suburban area, or for a flying object, and the maximum or minimum size, etc. of a world, a SPC or a VLM, etc. is defined in each profile. For example, more detailed information is required for an urban area than for a suburban area, and thus the minimum VLM size is set to small. 
     The meta-information may include tag values indicating object types. Each of such tag values is associated with VLMs, SPCs, or GOSs that constitute an object. For example, a tag value may be set for each object type in a manner, for example, that the tag value “0” indicates “person,” the tag value “1” indicates “car,” and the tag value “2” indicates “signal.” Alternatively, when an object type is hard to judge, or such judgment is not required, a tag value may be used that indicates the size or the attribute indicating, for example, whether an object is a dynamic object or a static object. 
     The meta-information may also include information indicating a range of the spatial region occupied by a world. 
     The meta-information may also store the SPC or V×L size as header information common to the whole stream of the encoded data or to a plurality of SPCs, such as SPCs in a GOS. 
     The meta-information may also include identification information on a distance sensor or a camera that has been used to generate a point cloud, or information indicating the positional accuracy of a point group in the point cloud. 
     The meta-information may also include information indicating whether a world is made only of static objects or includes a dynamic object. 
     The following describes variations of the present embodiment. 
     The encoding device or the decoding device may encode or decode two or more mutually different SPCs or GOSs in parallel. GOSs to be encoded or decoded in parallel can be determined on the basis of meta-information, etc. indicating the spatial positions of the GOSs. 
     When three-dimensional data is used as a spatial map for use by a car or a flying object, etc. in traveling, or for creation of such a spatial map, for example, the encoding device or the decoding device may encode or decode GOSs or SPCs included in a space that is identified on the basis of GPS information, the route information, the zoom magnification, etc. 
     The decoding device may also start decoding sequentially from a space that is close to the self-location or the traveling route. The encoding device or the decoding device may give a lower priority to a space distant from the self-location or the traveling route than the priority of a nearby space to encode or decode such distant place. To “give a lower priority” means here, for example, to lower the priority in the processing sequence, to decrease the resolution (to apply decimation in the processing), or to lower the image quality (to increase the encoding efficiency by, for example, setting the quantization step to larger). 
     When decoding encoded data that is hierarchically encoded in a space, the decoding device may decode only the bottom level in the hierarchy. 
     The decoding device may also start decoding preferentially from the bottom level of the hierarchy in accordance with the zoom magnification or the intended use of the map. 
     For self-location estimation or object recognition, etc. involved in the self-driving of a car or a robot, the encoding device or the decoding device may encode or decode regions at a lower resolution, except for a region that is lower than or at a specified height from the ground (the region to be recognized). 
     The encoding device may also encode point clouds representing the spatial shapes of a room interior and a room exterior separately. For example, the separation of a GOS representing a room interior (interior GOS) and a GOS representing a room exterior (exterior GOS) enables the decoding device to select a GOS to be decoded in accordance with a viewpoint location, when using the encoded data. 
     The encoding device may also encode an interior GOS and an exterior GOS having close coordinates so that such GOSs come adjacent to each other in an encoded stream. For example, the encoding device associates the identifiers of such GOSs with each other, and stores information indicating the associated identifiers into the meta-information that is stored in the encoded stream or stored separately. This enables the decoding device to refer to the information in the meta-information to identify an interior GOS and an exterior GOS having close coordinates The encoding device may also change the GOS size or the SPC size depending on whether a GOS is an interior GOS or an exterior GOS. For example, the encoding device sets the size of an interior GOS to smaller than the size of an exterior GOS. The encoding device may also change the accuracy of extracting keypoints from a point cloud, or the accuracy of detecting objects, for example, depending on whether a GOS is an interior GOS or an exterior GOS. 
     The encoding device may also add, to encoded data, information by which the decoding device displays objects with a distinction between a dynamic object and a static object. This enables the decoding device to display a dynamic object together with, for example, a red box or letters for explanation. Note that the decoding device may display only a red box or letters for explanation, instead of a dynamic object. The decoding device may also display more particular object types. For example, a red box may be used for a car, and a yellow box may be used for a person. 
     The encoding device or the decoding device may also determine whether to encode or decode a dynamic object and a static object as a different SPC or GOS, in accordance with, for example, the appearance frequency of dynamic objects or a ratio between static objects and dynamic objects. For example, when the appearance frequency or the ratio of dynamic objects exceeds a threshold, a SPC or a GOS including a mixture of a dynamic object and a static object is accepted, while when the appearance frequency or the ratio of dynamic objects is below a threshold, a SPC or GOS including a mixture of a dynamic object and a static object is unaccepted. 
     When detecting a dynamic object not from a point cloud but from two-dimensional image information of a camera, the encoding device may separately obtain information for identifying a detection result (box or letters) and the object position, and encode these items of information as part of the encoded three-dimensional data. In such a case, the decoding device superimposes auxiliary information (box or letters) indicating the dynamic object onto a resultant of decoding a static object to display it. 
     The encoding device may also change the sparseness and denseness of VXLs or VLMs in a SPC in accordance with the degree of complexity of the shape of a static object. For example, the encoding device sets VXLs or VLMs at a higher density as the shape of a static object is more complex. The encoding device may further determine a quantization step, etc. for quantizing spatial positions or color information in accordance with the sparseness and denseness of VXLs or VLMs. For example, the encoding device sets the quantization step to smaller as the density of VXLs or VLMs is higher. 
     As described above, the encoding device or the decoding device according to the present embodiment encodes or decodes a space on a SPC-by-SPC basis that includes coordinate information. 
     Furthermore, the encoding device and the decoding device perform encoding or decoding on a volume-by-volume basis in a SPC. Each volume includes a voxel, which is the minimum unit in which position information is associated. 
     Also, using a table that associates the respective elements of spatial information including coordinates, objects, and times with GOSs or using a table that associates these elements with each other, the encoding device and the decoding device associate any ones of the elements with each other to perform encoding or decoding. The decoding device uses the values of the selected elements to determine the coordinates, and identifies a volume, a voxel, or a SPC from such coordinates to decode a SPC including such volume or voxel, or the identified SPC. 
     Furthermore, the encoding device determines a volume, a voxel, or a SPC that is selectable in accordance with the elements, through extraction of keypoints and object recognition, and encodes the determined volume, voxel, or SPC, as a volume, a voxel, or a SPC to which random access is possible. 
     SPCs are classified into three types: I-SPC that is singly encodable or decodable; P-SPC that is encoded or decoded by referring to any one of the processed SPCs; and B-SPC that is encoded or decoded by referring to any two of the processed SPCs. 
     At least one volume corresponds to a static object or a dynamic object. A SPC including a static object and a SPC including a dynamic object are encoded or decoded as mutually different GOSs. Stated differently, a SPC including a static object and a SPC including a dynamic object are assigned to different GOSs. 
     Dynamic objects are encoded or decoded on an object-by-object basis, and are associated with at least one SPC including a static object. Stated differently, a plurality of dynamic objects are individually encoded, and the obtained encoded data of the dynamic objects is associated with a SPC including a static object. 
     The encoding device and the decoding device give an increased priority to I-SPC(s) in a GOS to perform encoding or decoding. For example, the encoding device performs encoding in a manner that prevents the degradation of I-SPCs (in a manner that enables the original three-dimensional data to be reproduced with a higher fidelity after decoded). The decoding device decodes, for example, only I-SPCs. 
     The encoding device may change the frequency of using I-SPCs depending on the sparseness and denseness or the number (amount) of the objects in a world to perform encoding. Stated differently, the encoding device changes the frequency of selecting I-SPCs depending on the number or the sparseness and denseness of the objects included in the three-dimensional data. For example, the encoding device uses I-SPCs at a higher frequency as the density of the objects in a world is higher. 
     The encoding device also sets random access points on a GOS-by-GOS basis, and stores information indicating the spatial regions corresponding to the GOSs into the header information. 
     The encoding devices uses, for example, a default value as the spatial size of a GOS. Note that the encoding device may change the GOS size depending on the number (amount) or the sparseness and denseness of objects or dynamic objects. For example, the encoding device sets the spatial size of a GOS to smaller as the density of objects or dynamic objects is higher or the number of objects or dynamic objects is greater. 
     Also, each SPC or volume includes a keypoint group that is derived by use of information obtained by a sensor such as a depth sensor, a gyroscope sensor, or a camera sensor. The coordinates of the keypoints are set at the central positions of the respective voxels. Furthermore, finer voxels enable highly accurate position information. 
     The keypoint group is derived by use of a plurality of pictures. A plurality of pictures include at least two types of time information: the actual time information and the same time information common to a plurality of pictures that are associated with SPCs (for example, the encoding time used for rate control, etc.). 
     Also, encoding or decoding is performed on a GOS-by-GOS basis that includes at least one SPC. 
     The encoding device and the decoding device predict P-SPCs or B-SPCs in a current GOS by referring to SPCs in a processed GOS. 
     Alternatively, the encoding device and the decoding device predict P-SPCs or B-SPCs in a current GOS, using the processed SPCs in the current GOS, without referring to a different GOS. 
     Furthermore, the encoding device and the decoding device transmit or receive an encoded stream on a world-by-world basis that includes at least one GOS. 
     Also, a GOS has a layer structure in one direction at least in a world, and the encoding device and the decoding device start encoding or decoding from the bottom layer. For example, a random accessible GOS belongs to the lowermost layer. A GOS that belongs to the same layer or a lower layer is referred to in a GOS that belongs to an upper layer. Stated differently, a GOS is spatially divided in a predetermined direction in advance to have a plurality of layers, each including at least one SPC. The encoding device and the decoding device encode or decode each SPC by referring to a SPC included in the same layer as the each SPC or a SPC included in a layer lower than that of the each SPC. 
     Also, the encoding device and the decoding device successively encode or decode GOSs on a world-by-world basis that includes such GOSs. In so doing, the encoding device and the decoding device write or read out information indicating the order (direction) of encoding or decoding as metadata. Stated differently, the encoded data includes information indicating the order of encoding a plurality of GOSs. 
     The encoding device and the decoding device also encode or decode mutually different two or more SPCs or GOSs in parallel. 
     Furthermore, the encoding device and the decoding device encode or decode the spatial information (coordinates, size, etc.) on a SPC or a GOS. 
     The encoding device and the decoding device encode or decode SPCs or GOSs included in an identified space that is identified on the basis of external information on the self-location or/and region size, such as GPS information, route information, or magnification. 
     The encoding device or the decoding device gives a lower priority to a space distant from the self-location than the priority of a nearby space to perform encoding or decoding. 
     The encoding device sets a direction at one of the directions in a world, in accordance with the magnification or the intended use, to encode a GOS having a layer structure in such direction. Also, the decoding device decodes a GOS having a layer structure in one of the directions in a world that has been set in accordance with the magnification or the intended use, preferentially from the bottom layer. 
     The encoding device changes the accuracy of extracting keypoints, the accuracy of recognizing objects, or the size of spatial regions, etc. included in a SPC, depending on whether an object is an interior object or an exterior object. Note that the encoding device and the decoding device encode or decode an interior GOS and an exterior GOS having close coordinates in a manner that these GOSs come adjacent to each other in a world, and associates their identifiers with each other for encoding and decoding. 
     Embodiment 2 
     When using encoded data of a point cloud in an actual device or service, it is desirable that necessary information be transmitted/received in accordance with the intended use to reduce the network bandwidth. However, there has been no such functionality in the structure of encoding three-dimensional data, nor an encoding method therefor. 
     The present embodiment describes a three-dimensional data encoding method and a three-dimensional data encoding device for providing the functionality of transmitting/receiving only necessary information in encoded data of a three-dimensional point cloud in accordance with the intended use, as well as a three-dimensional data decoding method and a three-dimensional data decoding device for decoding such encoded data. 
     A voxel (VXL) with a feature greater than or equal to a given amount is defined as a feature voxel (FVXL), and a world (WLD) constituted by FVXLs is defined as a sparse world (SWLD).  FIG. 11  is a diagram showing example structures of a sparse world and a world. A SWLD includes: FGOSs, each being a GOS constituted by FVXLs; FSPCs, each being a SPC constituted by FVXLs; and FVLMs, each being a VLM constituted by FVXLs. The data structure and prediction structure of a FGOS, a FSPC, and a FVLM may be the same as those of a GOS, a SPC, and a VLM. 
     A feature represents the three-dimensional position information on a VXL or the visible-light information on the position of a VXL. A large number of features are detected especially at a corner, an edge, etc. of a three-dimensional object. More specifically, such a feature is a three-dimensional feature or a visible-light feature as described below, but may be any feature that represents the position, luminance, or color information, etc. on a VXL. 
     Used as three-dimensional features are signature of histograms of orientations (SHOT) features, point feature histograms (PFH) features, or point pair feature (PPF) features. 
     SHOT features are obtained by dividing the periphery of a VXL, and calculating an inner product of the reference point and the normal vector of each divided region to represent the calculation result as a histogram. SHOT features are characterized by a large number of dimensions and high-level feature representation. 
     PFH features are obtained by selecting a large number of two point pairs in the vicinity of a VXL, and calculating the normal vector, etc. from each two point pair to represent the calculation result as a histogram. PFH features are histogram features, and thus are characterized by robustness against a certain extent of disturbance and also high-level feature representation. 
     PPF features are obtained by using a normal vector, etc. for each two points of VXLs. PPF features, for which all VXLs are used, has robustness against occlusion. 
     Used as visible-light features are scale-invariant feature transform (SIFT), speeded up robust features (SURF), or histogram of oriented gradients (HOG), etc. that use information on an image such as luminance gradient information. 
     A SWLD is generated by calculating the above-described features of the respective VXLs in a WLD to extract FVXLs. Here, the SWLD may be updated every time the WLD is updated, or may be regularly updated after the elapse of a certain period of time, regardless of the timing at which the WLD is updated. 
     A SWLD may be generated for each type of features. For example, different SWLDs may be generated for the respective types of features, such as SWLD1 based on SHOT features and SWLD2 based on SIFT features so that SWLDs are selectively used in accordance with the intended use. Also, the calculated feature of each FVXL may be held in each FVXL as feature information. 
     Next, the usage of a sparse world (SWLD) will be described. A SWLD includes only feature voxels (FVXLs), and thus its data size is smaller in general than that of a WLD that includes all VXLs. 
     In an application that utilizes features for a certain purpose, the use of information on a SWLD instead of a WLD reduces the time required to read data from a hard disk, as well as the bandwidth and the time required for data transfer over a network. For example, a WLD and a SWLD are held in a server as map information so that map information to be sent is selected between the WLD and the SWLD in accordance with a request from a client. This reduces the network bandwidth and the time required for data transfer. More specific examples will be described below. 
       FIG. 12  and  FIG. 13  are diagrams showing usage examples of a SWLD and a WLD. As  FIG. 12  shows, when client 1, which is a vehicle-mounted device, requires map information to use it for self-location determination, client 1 sends to a server a request for obtaining map data for self-location estimation (S 301 ). The server sends to client 1 the SWLD in response to the obtainment request (S 302 ). Client 1 uses the received SWLD to determine the self-location (S 303 ). In so doing, client 1 obtains VXL information on the periphery of client 1 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. Client 1 then estimates the self-location information from the obtained VXL information and the SWLD. Here, the self-location information includes three-dimensional position information, orientation, etc. of client 1. 
     As  FIG. 13  shows, when client 2, which is a vehicle-mounted device, requires map information to use it for rendering a map such as a three-dimensional map, client 2 sends to the server a request for obtaining map data for map rendering (S 311 ). The server sends to client 2 the WLD in response to the obtainment request (S 312 ). Client 2 uses the received WLD to render a map (S 313 ). In so doing, client 2 uses, for example, an image client 2 has captured by a visible-light camera, etc. and the WLD obtained from the server to create a rendering image, and renders such created image onto a screen of a car navigation system, etc. 
     As described above, the server sends to a client a SWLD when the features of the respective VXLs are mainly required such as in the case of self-location estimation, and sends to a client a WLD when detailed VXL information is required such as in the case of map rendering. This allows for an efficient sending/receiving of map data. 
     Note that a client may self-judge which one of a SWLD and a WLD is necessary, and request the server to send a SWLD or a WLD. Also, the server may judge which one of a SWLD and a WLD to send in accordance with the status of the client or a network. 
     Next, a method will be described of switching the sending/receiving between a sparse world (SWLD) and a world (WLD). 
     Whether to receive a WLD or a SWLD may be switched in accordance with the network bandwidth.  FIG. 14  is a diagram showing an example operation in such case. For example, when a low-speed network is used that limits the usable network bandwidth, such as in a long term evolution (LTE) environment, a client accesses the server over a low-speed network (S 321 ), and obtains the SWLD from the server as map information (S 322 ). Meanwhile, when a high-speed network is used that has an adequately broad network bandwidth, such as in a WiFi environment, a client accesses the server over a high-speed network (S 323 ), and obtains the WLD from the server (S 324 ). This enables the client to obtain appropriate map information in accordance with the network bandwidth such client is using. 
     More specifically, a client receives the SWLD over a LTE network when in outdoors, and obtains the WLD over a WiFi network when in indoors such as in a facility. This enables the client to obtain more detailed map information on indoor environment. 
     As described above, a client may request for a WLD or a SWLD in accordance with the bandwidth of a network such client is using. Alternatively, the client may send to the server information indicating the bandwidth of a network such client is using, and the server may send to the client data (the WLD or the SWLD) suitable for such client in accordance with the information. Alternatively, the server may identify the network bandwidth the client is using, and send to the client data (the WLD or the SWLD) suitable for such client. 
     Also, whether to receive a WLD or a SWLD may be switched in accordance with the speed of traveling.  FIG. 15  is a diagram showing an example operation in such case. For example, when traveling at a high speed (S 331 ), a client receives the SWLD from the server (S 332 ). Meanwhile, when traveling at a low speed (S 333 ), the client receives the WLD from the server (S 334 ). This enables the client to obtain map information suitable to the speed, while reducing the network bandwidth. More specifically, when traveling on an expressway, the client receives the SWLD with a small data amount, which enables the update of rough map information at an appropriate speed. Meanwhile, when traveling on a general road, the client receives the WLD, which enables the obtainment of more detailed map information. 
     As described above, the client may request the server for a WLD or a SWLD in accordance with the traveling speed of such client. Alternatively, the client may send to the server information indicating the traveling speed of such client, and the server may send to the client data (the WLD or the SWLD) suitable to such client in accordance with the information. Alternatively, the server may identify the traveling speed of the client to send data (the WLD or the SWLD) suitable to such client. 
     Also, the client may obtain, from the server, a SWLD first, from which the client may obtain a WLD of an important region. For example, when obtaining map information, the client first obtains a SWLD for rough map information, from which the client narrows to a region in which features such as buildings, signals, or persons appear at high frequency so that the client can later obtain a WLD of such narrowed region. This enables the client to obtain detailed information on a necessary region, while reducing the amount of data received from the server. 
     The server may also create from a WLD different SWLDs for the respective objects, and the client may receive SWLDs in accordance with the intended use. This reduces the network bandwidth. For example, the server recognizes persons or cars in a WLD in advance, and creates a SWLD of persons and a SWLD of cars. The client, when wishing to obtain information on persons around the client, receives the SWLD of persons, and when wising to obtain information on cars, receives the SWLD of cars. Such types of SWLDs may be distinguished by information (flag, or type, etc.) added to the header, etc. 
     Next, the structure and the operation flow of the three-dimensional data encoding device (e.g., a server) according to the present embodiment will be described.  FIG. 16  is a block diagram of three-dimensional data encoding device  400  according to the present embodiment.  FIG. 17  is a flowchart of three-dimensional data encoding processes performed by three-dimensional data encoding device  400 . 
     Three-dimensional data encoding device  400  shown in  FIG. 16  encodes input three-dimensional data  411 , thereby generating encoded three-dimensional data  413  and encoded three-dimensional data  414 , each being an encoded stream. Here, encoded three-dimensional data  413  is encoded three-dimensional data corresponding to a WLD, and encoded three-dimensional data  414  is encoded three-dimensional data corresponding to a SWLD. Such three-dimensional data encoding device  400  includes, obtainer  401 , encoding region determiner  402 , SWLD extractor  403 , WLD encoder  404 , and SWLD encoder  405 . 
     First, as  FIG. 17  shows, obtainer  401  obtains input three-dimensional data  411 , which is point group data in a three-dimensional space (S 401 ). 
     Next, encoding region determiner  402  determines a current spatial region for encoding on the basis of a spatial region in which the point cloud data is present (S 402 ). 
     Next, SWLD extractor  403  defines the current spatial region as a WLD, and calculates the feature from each VXL included in the WLD. Then, SWLD extractor  403  extracts VXLs having an amount of features greater than or equal to a predetermined threshold, defines the extracted VXLs as FVXLs, and adds such FVXLs to a SWLD, thereby generating extracted three-dimensional data  412  (S 403 ). Stated differently, extracted three-dimensional data  412  having an amount of features greater than or equal to the threshold is extracted from input three-dimensional data  411 . 
     Next, WLD encoder  404  encodes input three-dimensional data  411  corresponding to the WLD, thereby generating encoded three-dimensional data  413  corresponding to the WLD (S 404 ). In so doing, WLD encoder  404  adds to the header of encoded three-dimensional data  413  information that distinguishes that such encoded three-dimensional data  413  is a stream including a WLD. 
     SWLD encoder  405  encodes extracted three-dimensional data  412  corresponding to the SWLD, thereby generating encoded three-dimensional data  414  corresponding to the SWLD (S 405 ). In so doing, SWLD encoder  405  adds to the header of encoded three-dimensional data  414  information that distinguishes that such encoded three-dimensional data  414  is a stream including a SWLD. 
     Note that the process of generating encoded three-dimensional data  413  and the process of generating encoded three-dimensional data  414  may be performed in the reverse order. Also note that a part or all of these processes may be performed in parallel. 
     A parameter “world_type” is defined, for example, as information added to each header of encoded three-dimensional data  413  and encoded three-dimensional data  414 . world_type=0 indicates that a stream includes a WLD, and world_type=1 indicates that a stream includes a SWLD. An increased number of values may be further assigned to define a larger number of types, e.g., world_type=2. Also, one of encoded three-dimensional data  413  and encoded three-dimensional data  414  may include a specified flag. For example, encoded three-dimensional data  414  may be assigned with a flag indicating that such stream includes a SWLD. In such a case, the decoding device can distinguish whether such stream is a stream including a WLD or a stream including a SWLD in accordance with the presence/absence of the flag. 
     Also, an encoding method used by WLD encoder  404  to encode a WLD may be different from an encoding method used by SWLD encoder  405  to encode a SWLD. 
     For example, data of a SWLD is decimated, and thus can have a lower correlation with the neighboring data than that of a WLD. For this reason, of intra prediction and inter prediction, inter prediction may be more preferentially performed in an encoding method used for a SWLD than in an encoding method used for a WLD. 
     Also, an encoding method used for a SWLD and an encoding method used for a WLD may represent three-dimensional positions differently. For example, three-dimensional coordinates may be used to represent the three-dimensional positions of FVXLs in a SWLD and an octree described below may be used to represent three-dimensional positions in a WLD, and vice versa. 
     Also, SWLD encoder  405  performs encoding in a manner that encoded three-dimensional data  414  of a SWLD has a smaller data size than the data size of encoded three-dimensional data  413  of a WLD. A SWLD can have a lower inter-data correlation, for example, than that of a WLD as described above. This can lead to a decreased encoding efficiency, and thus to encoded three-dimensional data  414  having a larger data size than the data size of encoded three-dimensional data  413  of a WLD. When the data size of the resulting encoded three-dimensional data  414  is larger than the data size of encoded three-dimensional data  413  of a WLD, SWLD encoder  405  performs encoding again to re-generate encoded three-dimensional data  414  having a reduced data size. 
     For example, SWLD extractor  403  re-generates extracted three-dimensional data  412  having a reduced number of keypoints to be extracted, and SWLD encoder  405  encodes such extracted three-dimensional data  412 . Alternatively, SWLD encoder  405  may perform more coarse quantization. More coarse quantization is achieved, for example, by rounding the data in the lowermost level in an octree structure described below. 
     When failing to decrease the data size of encoded three-dimensional data  414  of the SWLD to smaller than the data size of encoded three-dimensional data  413  of the WLD, SWLD encoder  405  may not generate encoded three-dimensional data  414  of the SWLD. Alternatively, encoded three-dimensional data  413  of the WLD may be copied as encoded three-dimensional data  414  of the SWLD. Stated differently, encoded three-dimensional data  413  of the WLD may be used as it is as encoded three-dimensional data  414  of the SWLD. 
     Next, the structure and the operation flow of the three-dimensional data decoding device (e.g., a client) according to the present embodiment will be described.  FIG. 18  is a block diagram of three-dimensional data decoding device  500  according to the present embodiment.  FIG. 19  is a flowchart of three-dimensional data decoding processes performed by three-dimensional data decoding device  500 . 
     Three-dimensional data decoding device  500  shown in  FIG. 18  decodes encoded three-dimensional data  511 , thereby generating decoded three-dimensional data  512  or decoded three-dimensional data  513 . Encoded three-dimensional data  511  here is, for example, encoded three-dimensional data  413  or encoded three-dimensional data  414  generated by three-dimensional data encoding device  400 . 
     Such three-dimensional data decoding device  500  includes obtainer  501 , header analyzer  502 , WLD decoder  503 , and SWLD decoder  504 . 
     First, as  FIG. 19  shows, obtainer  501  obtains encoded three-dimensional data  511  (S 501 ). Next, header analyzer  502  analyzes the header of encoded three-dimensional data  511  to identify whether encoded three-dimensional data  511  is a stream including a WLD or a stream including a SWLD (S 502 ). For example, the above-described parameter world_type is referred to in making such identification. 
     When encoded three-dimensional data  511  is a stream including a WLD (Yes in S 503 ), WLD decoder  503  decodes encoded three-dimensional data  511 , thereby generating decoded three-dimensional data  512  of the WLD (S 504 ). Meanwhile, when encoded three-dimensional data  511  is a stream including a SWLD (No in S 503 ), SWLD decoder  504  decodes encoded three-dimensional data  511 , thereby generating decoded three-dimensional data  513  of the SWLD (S 505 ). 
     Also, as in the case of the encoding device, a decoding method used by WLD decoder  503  to decode a WLD may be different from a decoding method used by SWLD decoder  504  to decode a SWLD. For example, of intra prediction and inter prediction, inter prediction may be more preferentially performed in a decoding method used for a SWLD than in a decoding method used for a WLD. 
     Also, a decoding method used for a SWLD and a decoding method used for a WLD may represent three-dimensional positions differently. For example, three-dimensional coordinates may be used to represent the three-dimensional positions of FVXLs in a SWLD and an octree described below may be used to represent three-dimensional positions in a WLD, and vice versa. 
     Next, an octree representation will be described, which is a method of representing three-dimensional positions. VXL data included in three-dimensional data is converted into an octree structure before encoded.  FIG. 20  is a diagram showing example VXLs in a WLD.  FIG. 21  is a diagram showing an octree structure of the WLD shown in  FIG. 20 . An example shown in  FIG. 20  illustrates three VXLs 1 to 3 that include point groups (hereinafter referred to as effective VXLs). As  FIG. 21  shows, the octree structure is made of nodes and leaves. Each node has a maximum of eight nodes or leaves. Each leaf has VXL information. Here, of the leaves shown in  FIG. 21 , leaf 1, leaf 2, and leaf 3 represent VXL1, VXL2, and VXL3 shown in  FIG. 20 , respectively. 
     More specifically, each node and each leaf corresponds to a three-dimensional position. Node 1 corresponds to the entire block shown in  FIG. 20 . The block that corresponds to node 1 is divided into eight blocks. Of these eight blocks, blocks including effective VXLs are set as nodes, while the other blocks are set as leaves. Each block that corresponds to a node is further divided into eight nodes or leaves. These processes are repeated by the number of times that is equal to the number of levels in the octree structure. All blocks in the lowermost level are set as leaves. 
       FIG. 22  is a diagram showing an example SWLD generated from the WLD shown in  FIG. 20 . VXL1 and VXL2 shown in  FIG. 20  are judged as FVXL1 and FVXL2 as a result of feature extraction, and thus are added to the SWLD. Meanwhile, VXL3 is not judged as a FVXL, and thus is not added to the SWLD.  FIG. 23  is a diagram showing an octree structure of the SWLD shown in  FIG. 22 . In the octree structure shown in  FIG. 23 , leaf 3 corresponding to VXL3 shown in  FIG. 21  is deleted. Consequently, node 3 shown in  FIG. 21  has lost an effective VXL, and has changed to a leaf. As described above, a SWLD has a smaller number of leaves in general than a WLD does, and thus the encoded three-dimensional data of the SWLD is smaller than the encoded three-dimensional data of the WLD. 
     The following describes variations of the present embodiment. 
     For self-location estimation, for example, a client, being a vehicle-mounted device, etc., may receive a SWLD from the server to use such SWLD to estimate the self-location. Meanwhile, for obstacle detection, the client may detect obstacles by use of three-dimensional information on the periphery obtained by such client 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. 
     In general, a SWLD is less likely to include VXL data on a flat region. As such, the server may hold a subsample world (subWLD) obtained by subsampling a WLD for detection of static obstacles, and send to the client the SWLD and the subWLD. This enables the client to perform self-location estimation and obstacle detection on the client&#39;s part, while reducing the network bandwidth. 
     When the client renders three-dimensional map data at a high speed, map information having a mesh structure is more useful in some cases. As such, the server may generate a mesh from a WLD to hold it beforehand as a mesh world (MWLD). For example, when wishing to perform coarse three-dimensional rendering, the client receives a MWLD, and when wishing to perform detailed three-dimensional rendering, the client receives a WLD. This reduces the network bandwidth. 
     In the above description, the server sets, as FVXLs, VXLs having an amount of features greater than or equal to the threshold, but the server may calculate FVXLs by a different method. For example, the server may judge that a VXL, a VLM, a SPC, or a GOS that constitutes a signal, or an intersection, etc. as necessary for self-location estimation, driving assist, or self-driving, etc., and incorporate such VXL, VLM, SPC, or GOS into a SWLD as a FVXL, a FVLM, a FSPC, or a FGOS. Such judgment may be made manually. Also, FVXLs, etc. that have been set on the basis of an amount of features may be added to FVXLs, etc. obtained by the above method. Stated differently, SWLD extractor  403  may further extract, from input three-dimensional data  411 , data corresponding to an object having a predetermined attribute as extracted three-dimensional data  412 . 
     Also, that a VXL, a VLM, a SPC, or a GOS are necessary for such intended usage may labelled separately from the features. The server may separately hold, as an upper layer of a SWLD (e.g., a lane world), FVXLs of a signal or an intersection, etc. necessary for self-location estimation, driving assist, or self-driving, etc. 
     The server may also add an attribute to VXLs in a WLD on a random access basis or on a predetermined unit basis. An attribute, for example, includes information indicating whether VXLs are necessary for self-location estimation, or information indicating whether VXLs are important as traffic information such as a signal, or an intersection, etc. An attribute may also include a correspondence between VXLs and features (intersection, or road, etc.) in lane information (geographic data files (GDF), etc.). 
     A method as described below may be used to update a WLD or a SWLD. 
     Update information indicating changes, etc. in a person, a roadwork, or a tree line (for trucks) is uploaded to the server as point groups or meta data. The server updates a WLD on the basis of such uploaded information, and then updates a SWLD by use of the updated WLD. 
     The client, when detecting a mismatch between the three-dimensional information such client has generated at the time of self-location estimation and the three-dimensional information received from the server, may send to the server the three-dimensional information such client has generated, together with an update notification. In such a case, the server updates the SWLD by use of the WLD. When the SWLD is not to be updated, the server judges that the WLD itself is old. 
     In the above description, information that distinguishes whether an encoded stream is that of a WLD or a SWLD is added as header information of the encoded stream. However, when there are many types of worlds such as a mesh world and a lane world, information that distinguishes these types of the worlds may be added to header information. Also, when there are many SWLDs with different amounts of features, information that distinguishes the respective SWLDs may be added to header information. 
     In the above description, a SWLD is constituted by FVXLs, but a SWLD may include VXLs that have not been judged as FVXLs. For example, a SWLD may include an adjacent VXL used to calculate the feature of a FVXL. This enables the client to calculate the feature of a FVXL when receiving a SWLD, even in the case where feature information is not added to each FVXL of the SWLD. In such a case, the SWLD may include information that distinguishes whether each VXL is a FVXL or a VXL. 
     As described above, three-dimensional data encoding device  400  extracts, from input three-dimensional data  411  (first three-dimensional data), extracted three-dimensional data  412  (second three-dimensional data) having an amount of a feature greater than or equal to a threshold, and encodes extracted three-dimensional data  412  to generate encoded three-dimensional data  414  (first encoded three-dimensional data). 
     This three-dimensional data encoding device  400  generates encoded three-dimensional data  414  that is obtained by encoding data having an amount of a feature greater than or equal to the threshold. This reduces the amount of data compared to the case where input three-dimensional data  411  is encoded as it is. Three-dimensional data encoding device  400  is thus capable of reducing the amount of data to be transmitted. 
     Three-dimensional data encoding device  400  further encodes input three-dimensional data  411  to generate encoded three-dimensional data  413  (second encoded three-dimensional data). 
     This three-dimensional data encoding device  400  enables selective transmission of encoded three-dimensional data  413  and encoded three-dimensional data  414 , in accordance, for example, with the intended use, etc. 
     Also, extracted three-dimensional data  412  is encoded by a first encoding method, and input three-dimensional data  411  is encoded by a second encoding method different from the first encoding method. 
     This three-dimensional data encoding device  400  enables the use of an encoding method suitable for each of input three-dimensional data  411  and extracted three-dimensional data  412 . 
     Also, of intra prediction and inter prediction, the inter prediction is more preferentially performed in the first encoding method than in the second encoding method. 
     This three-dimensional data encoding device  400  enables inter prediction to be more preferentially performed on extracted three-dimensional data  412  in which adjacent data items are likely to have low correlation. 
     Also, the first encoding method and the second encoding method represent three-dimensional positions differently. For example, the second encoding method represents three-dimensional positions by octree, and the first encoding method represents three-dimensional positions by three-dimensional coordinates. 
     This three-dimensional data encoding device  400  enables the use of a more suitable method to represent the three-dimensional positions of three-dimensional data in consideration of the difference in the number of data items (the number of VXLs or FVXLs) included. 
     Also, at least one of encoded three-dimensional data  413  and encoded three-dimensional data  414  includes an identifier indicating whether the encoded three-dimensional data is encoded three-dimensional data obtained by encoding input three-dimensional data  411  or encoded three-dimensional data obtained by encoding part of input three-dimensional data  411 . Stated differently, such identifier indicates whether the encoded three-dimensional data is encoded three-dimensional data  413  of a WLD or encoded three-dimensional data  414  of a SWLD. 
     This enables the decoding device to readily judge whether the obtained encoded three-dimensional data is encoded three-dimensional data  413  or encoded three-dimensional data  414 . 
     Also, three-dimensional data encoding device  400  encodes extracted three-dimensional data  412  in a manner that encoded three-dimensional data  414  has a smaller data amount than a data amount of encoded three-dimensional data  413 . 
     This three-dimensional data encoding device  400  enables encoded three-dimensional data  414  to have a smaller data amount than the data amount of encoded three-dimensional data  413 . 
     Also, three-dimensional data encoding device  400  further extracts data corresponding to an object having a predetermined attribute from input three-dimensional data  411  as extracted three-dimensional data  412 . The object having a predetermined attribute is, for example, an object necessary for self-location estimation, driving assist, or self-driving, etc., or more specifically, a signal, an intersection, etc. 
     This three-dimensional data encoding device  400  is capable of generating encoded three-dimensional data  414  that includes data required by the decoding device. 
     Also, three-dimensional data encoding device  400  (server) further sends, to a client, one of encoded three-dimensional data  413  and encoded three-dimensional data  414  in accordance with a status of the client. 
     This three-dimensional data encoding device  400  is capable of sending appropriate data in accordance with the status of the client. 
     Also, the status of the client includes one of a communication condition (e.g., network bandwidth) of the client and a traveling speed of the client. 
     Also, three-dimensional data encoding device  400  further sends, to a client, one of encoded three-dimensional data  413  and encoded three-dimensional data  414  in accordance with a request from the client. 
     This three-dimensional data encoding device  400  is capable of sending appropriate data in accordance with the request from the client. 
     Also, three-dimensional data decoding device  500  according to the present embodiment decodes encoded three-dimensional data  413  or encoded three-dimensional data  414  generated by three-dimensional data encoding device  400  described above. 
     Stated differently, three-dimensional data decoding device  500  decodes, by a first decoding method, encoded three-dimensional data  414  obtained by encoding extracted three-dimensional data  412  having an amount of a feature greater than or equal to a threshold, extracted three-dimensional data  412  having been extracted from input three-dimensional data  411 . 
     Three-dimensional data decoding device  500  also decodes, by a second decoding method, encoded three-dimensional data  413  obtained by encoding input three-dimensional data  411 , the second decoding method being different from the first decoding method. 
     This three-dimensional data decoding device  500  enables selective reception of encoded three-dimensional data  414  obtained by encoding data having an amount of a feature greater than or equal to the threshold and encoded three-dimensional data  413 , in accordance, for example, with the intended use, etc. Three-dimensional data decoding device  500  is thus capable of reducing the amount of data to be transmitted. Such three-dimensional data decoding device  500  further enables the use of a decoding method suitable for each of input three-dimensional data  411  and extracted three-dimensional data  412 . 
     Also, of intra prediction and inter prediction, the inter prediction is more preferentially performed in the first decoding method than in the second decoding method. 
     This three-dimensional data decoding device  500  enables inter prediction to be more preferentially performed on the extracted three-dimensional data in which adjacent data items are likely to have low correlation. 
     Also, the first decoding method and the second decoding method represent three-dimensional positions differently. For example, the second decoding method represents three-dimensional positions by octree, and the first decoding method represents three-dimensional positions by three-dimensional coordinates. 
     This three-dimensional data decoding device  500  enables the use of a more suitable method to represent the three-dimensional positions of three-dimensional data in consideration of the difference in the number of data items (the number of VXLs or FVXLs) included. 
     Also, at least one of encoded three-dimensional data  413  and encoded three-dimensional data  414  includes an identifier indicating whether the encoded three-dimensional data is encoded three-dimensional data obtained by encoding input three-dimensional data  411  or encoded three-dimensional data obtained by encoding part of input three-dimensional data  411 . Three-dimensional data decoding device  500  refers to such identifier in identifying between encoded three-dimensional data  413  and encoded three-dimensional data  414 . 
     This three-dimensional data decoding device  500  is capable of readily judging whether the obtained encoded three-dimensional data is encoded three-dimensional data  413  or encoded three-dimensional data  414 . 
     Three-dimensional data decoding device  500  further notifies a server of a status of the client (three-dimensional data decoding device  500 ). Three-dimensional data decoding device  500  receives one of encoded three-dimensional data  413  and encoded three-dimensional data  414  from the server, in accordance with the status of the client. 
     This three-dimensional data decoding device  500  is capable of receiving appropriate data in accordance with the status of the client. 
     Also, the status of the client includes one of a communication condition (e.g., network bandwidth) of the client and a traveling speed of the client. 
     Three-dimensional data decoding device  500  further makes a request of the server for one of encoded three-dimensional data  413  and encoded three-dimensional data  414 , and receives one of encoded three-dimensional data  413  and encoded three-dimensional data  414  from the server, in accordance with the request. 
     This three-dimensional data decoding device  500  is capable of receiving appropriate data in accordance with the intended use. 
     Embodiment 3 
     The present embodiment will describe a method of transmitting/receiving three-dimensional data between vehicles. 
       FIG. 24  is a schematic diagram showing three-dimensional data  607  being transmitted/received between own vehicle  600  and nearby vehicle  601 . 
     In three-dimensional data that is obtained by a sensor mounted on own vehicle  600  (e.g., a distance sensor such as a rangefinder, as well as a stereo camera and a combination of a plurality of monocular cameras), there appears a region, three-dimensional data of which cannot be created, due to an obstacle such as nearby vehicle  601 , despite that such region is included in sensor detection range  602  of own vehicle  600  (such region is hereinafter referred to as occlusion region  604 ). Also, while the obtainment of three-dimensional data of a larger space enables a higher accuracy of autonomous operations, a range of sensor detection only by own vehicle  600  is limited. 
     Sensor detection range  602  of own vehicle  600  includes region  603 , three-dimensional data of which is obtainable, and occlusion region  604 . A range, three-dimensional data of which own vehicle  600  wishes to obtain, includes sensor detection range  602  of own vehicle  600  and other regions. Sensor detection range  605  of nearby vehicle  601  includes occlusion region  604  and region  606  that is not included in sensor detection range  602  of own vehicle  600 . 
     Nearby vehicle  601  transmits information detected by nearby vehicle  601  to own vehicle  600 . Own vehicle  600  obtains the information detected by nearby vehicle  601 , such as a preceding vehicle, thereby obtaining three-dimensional data  607  of occlusion region  604  and region  606  outside of sensor detection range  602  of own vehicle  600 . Own vehicle  600  uses the information obtained by nearby vehicle  601  to complement the three-dimensional data of occlusion region  604  and region  606  outside of the sensor detection range. 
     The usage of three-dimensional data in autonomous operations of a vehicle or a robot includes self-location estimation, detection of surrounding conditions, or both. For example, for self-location estimation, three-dimensional data is used that is generated by own vehicle  600  on the basis of sensor information of own vehicle  600 . For detection of surrounding conditions, three-dimensional data obtained from nearby vehicle  601  is also used in addition to the three-dimensional data generated by own vehicle  600 . 
     Nearby vehicle  601  that transmits three-dimensional data  607  to own vehicle  600  may be determined in accordance with the state of own vehicle  600 . For example, the current nearby vehicle  601  is a preceding vehicle when own vehicle  600  is running straight ahead, an oncoming vehicle when own vehicle  600  is turning right, and a following vehicle when own vehicle  600  is rolling backward. Alternatively, the driver of own vehicle  600  may directly specify nearby vehicle  601  that transmits three-dimensional data  607  to own vehicle  600 . 
     Alternatively, own vehicle  600  may search for nearby vehicle  601  having three-dimensional data of a region that is included in a space, three-dimensional data of which own vehicle  600  wishes to obtain, and that own vehicle  600  cannot obtain. The region own vehicle  600  cannot obtain is occlusion region  604 , or region  606  outside of sensor detection range  602 , etc. 
     Own vehicle  600  may identify occlusion region  604  on the basis of the sensor information of own vehicle  600 . For example, own vehicle  600  identifies, as occlusion region  604 , a region which is included in sensor detection range  602  of own vehicle  600 , and three-dimensional data of which cannot be created. 
     The following describes example operations to be performed when a vehicle that transmits three-dimensional data  607  is a preceding vehicle.  FIG. 25  is a diagram showing an example of three-dimensional data to be transmitted in such case. 
     As  FIG. 25  shows, three-dimensional data  607  transmitted from the preceding vehicle is, for example, a sparse world (SWLD) of a point cloud. Stated differently, the preceding vehicle creates three-dimensional data (point cloud) of a WLD from information detected by a sensor of such preceding vehicle, and extracts data having an amount of features greater than or equal to the threshold from such three-dimensional data of the WLD, thereby creating three-dimensional data (point cloud) of the SWLD. Subsequently, the preceding vehicle transmits the created three-dimensional data of the SWLD to own vehicle  600 . 
     Own vehicle  600  receives the SWLD, and merges the received SWLD with the point cloud created by own vehicle  600 . 
     The SWLD to be transmitted includes information on the absolute coordinates (the position of the SWLD in the coordinates system of a three-dimensional map). The merge is achieved by own vehicle  600  overwriting the point cloud generated by own vehicle  600  on the basis of such absolute coordinates. 
     The SWLD transmitted from nearby vehicle  601  may be: a SWLD of region  606  that is outside of sensor detection range  602  of own vehicle  600  and within sensor detection range  605  of nearby vehicle  601 ; or a SWLD of occlusion region  604  of own vehicle  600 ; or the SWLDs of the both. Of these SWLDs, a SWLD to be transmitted may also be a SWLD of a region used by nearby vehicle  601  to detect the surrounding conditions. 
     Nearby vehicle  601  may change the density of a point cloud to transmit, in accordance with the communication available time, during which own vehicle  600  and nearby vehicle  601  can communicate, and which is based on the speed difference between these vehicles. For example, when the speed difference is large and the communication available time is short, nearby vehicle  601  may extract three-dimensional points having a large amount of features from the SWLD to decrease the density (data amount) of the point cloud. 
     The detection of the surrounding conditions refers to judging the presence/absence of persons, vehicles, equipment for roadworks, etc., identifying their types, and detecting their positions, travelling directions, traveling speeds, etc. 
     Own vehicle  600  may obtain braking information of nearby vehicle  601  instead of or in addition to three-dimensional data  607  generated by nearby vehicle  601 . Here, the braking information of nearby vehicle  601  is, for example, information indicating that the accelerator or the brake of nearby vehicle  601  has been pressed, or the degree of such pressing. 
     In the point clouds generated by the vehicles, the three-dimensional spaces are segmented on a random access unit, in consideration of low-latency communication between the vehicles. Meanwhile, in a three-dimensional map, etc., which is map data downloaded from the server, a three-dimensional space is segmented in a larger random access unit than in the case of inter-vehicle communication. 
     Data on a region that is likely to be an occlusion region, such as a region in front of the preceding vehicle and a region behind the following vehicle, is segmented on a finer random access unit as low-latency data. 
     Data on a region in front of a vehicle has an increased importance when on an expressway, and thus each vehicle creates a SWLD of a range with a narrowed viewing angle on a finer random access unit when running on an expressway. 
     When the SWLD created by the preceding vehicle for transmission includes a region, the point cloud of which own vehicle  600  can obtain, the preceding vehicle may remove the point cloud of such region to reduce the amount of data to transmit. 
     Next, the structure and operations of three-dimensional data creation device  620  will be described, which is the three-dimensional data reception device according to the present embodiment. 
       FIG. 26  is a block diagram of three-dimensional data creation device  620  according to the present embodiment. Such three-dimensional data creation device  620 , which is included, for example, in the above-described own vehicle  600 , mergers first three-dimensional data  632  created by three-dimensional data creation device  620  with the received second three-dimensional data  635 , thereby creating third three-dimensional data  636  having a higher density. 
     Such three-dimensional data creation device  620  includes three-dimensional data creator  621 , request range determiner  622 , searcher  623 , receiver  624 , decoder  625 , and merger  626 .  FIG. 27  is a flowchart of operations performed by three-dimensional data creation device  620 . 
     First, three-dimensional data creator  621  creates first three-dimensional data  632  by use of sensor information  631  detected by the sensor included in own vehicle  600  (S 621 ). Next, request range determiner  622  determines a request range, which is the range of a three-dimensional space, the data on which is insufficient in the created first three-dimensional data  632  (S 622 ). 
     Next, searcher  623  searches for nearby vehicle  601  having the three-dimensional data of the request range, and sends request range information  633  indicating the request range to nearby vehicle  601  having been searched out (S 623 ). Next, receiver  624  receives encoded three-dimensional data  634 , which is an encoded stream of the request range, from nearby vehicle  601  (S 624 ). Note that searcher  623  may indiscriminately send requests to all vehicles included in a specified range to receive encoded three-dimensional data  634  from a vehicle that has responded to the request. Searcher  623  may send a request not only to vehicles but also to an object such as a signal and a sign, and receive encoded three-dimensional data  634  from the object. 
     Next, decoder  625  decodes the received encoded three-dimensional data  634 , thereby obtaining second three-dimensional data  635  (S 625 ). Next, merger  626  merges first three-dimensional data  632  with second three-dimensional data  635 , thereby creating three-dimensional data  636  having a higher density (S 626 ). 
     Next, the structure and operations of three-dimensional data transmission device  640  according to the present embodiment will be described.  FIG. 28  is a block diagram of three-dimensional data transmission device  640 . 
     Three-dimensional data transmission device  640  is included, for example, in the above-described nearby vehicle  601 . Three-dimensional data transmission device  640  processes fifth three-dimensional data  652  created by nearby vehicle  601  into sixth three-dimensional data  654  requested by own vehicle  600 , encodes sixth three-dimensional data  654  to generate encoded three-dimensional data  634 , and sends encoded three-dimensional data  634  to own vehicle  600 . 
     Three-dimensional data transmission device  640  includes three-dimensional data creator  641 , receiver  642 , extractor  643 , encoder  644 , and transmitter  645 .  FIG. 29  is a flowchart of operations performed by three-dimensional data transmission device  640 . 
     First, three-dimensional data creator  641  creates fifth three-dimensional data  652  by use of sensor information  651  detected by the sensor included in nearby vehicle  601  (S 641 ). Next, receiver  642  receives request range information  633  from own vehicle  600  (S 642 ). 
     Next, extractor  643  extracts from fifth three-dimensional data  652  the three-dimensional data of the request range indicated by request range information  633 , thereby processing fifth three-dimensional data  652  into sixth three-dimensional data  654  (S 643 ). Next, encoder  644  encodes sixth three-dimensional data  654  to generate encoded three-dimensional data  643 , which is an encoded stream (S 644 ). Then, transmitter  645  sends encoded three-dimensional data  634  to own vehicle  600  (S 645 ). 
     Note that although an example case is described here in which own vehicle  600  includes three-dimensional data creation device  620  and nearby vehicle  601  includes three-dimensional data transmission device  640 , each of the vehicles may include the functionality of both three-dimensional data creation device  620  and three-dimensional data transmission device  640 . 
     The following describes the structure and operations of three-dimensional data creation device  620  when three-dimensional data creation device  620  is a surrounding condition detection device that enables the detection of the surrounding conditions of own vehicle  600 .  FIG. 30  is a block diagram of the structure of three-dimensional data creation device  620 A in such case. Three-dimensional data creation device  620 A shown in  FIG. 30  further includes detection region determiner  627 , surrounding condition detector  628 , and autonomous operation controller  629 , in addition to the components of three-dimensional data creation device  620  shown in  FIG. 26 . Three-dimensional data creation device  620 A is included in own vehicle  600 . 
       FIG. 31  is a flowchart of processes, performed by three-dimensional data creation device  620 A, of detecting the surrounding conditions of own vehicle  600 . 
     First, three-dimensional data creator  621  creates first three-dimensional data  632 , which is a point cloud, by use of sensor information  631  on the detection range of own vehicle  600  detected by the sensor of own vehicle  600  (S 661 ). Note that three-dimensional data creation device  620 A may further estimate the self-location by use of sensor information  631 . 
     Next, detection region determiner  627  determines a target detection range, which is a spatial region, the surrounding conditions of which are wished to be detected (S 662 ). For example, detection region determiner  627  calculates a region that is necessary for the detection of the surrounding conditions, which is an operation required for safe autonomous operations (self-driving), in accordance with the conditions of autonomous operations, such as the direction and speed of traveling of own vehicle  600 , and determines such region as the target detection range. 
     Next, request range determiner  622  determines, as a request range, occlusion region  604  and a spatial region that is outside of the detection range of the sensor of own vehicle  600  but that is necessary for the detection of the surrounding conditions (S 663 ). 
     When the request range determined in step S 663  is present (Yes in S 664 ), searcher  623  searches for a nearby vehicle having information on the request range. For example, searcher  623  may inquire about whether a nearby vehicle has information on the request range, or may judge whether a nearby vehicle has information on the request range, on the basis of the positions of the request range and such nearby vehicle. Next, searcher  623  sends, to nearby vehicle  601  having been searched out, request signal  637  that requests for the transmission of three-dimensional data. Searcher  623  then receives an acceptance signal from nearby vehicle  601  indicating that the request of request signal  637  has been accepted, after which searcher  623  sends request range information  633  indicating the request range to nearby vehicle  601  (S 665 ). 
     Next, receiver  624  detects a notice that transmission data  638  has been transmitted, which is the information on the request range, and receives such transmission data  638  (S 666 ). 
     Note that three-dimensional data creation device  620 A may indiscriminately send requests to all vehicles in a specified range and receive transmission data  638  from a vehicle that has sent a response indicating that such vehicle has the information on the request range, without searching for a vehicle to send a request to. Searcher  623  may send a request not only to vehicles but also to an object such as a signal and a sign, and receive transmission data  638  from such object. 
     Transmission data  638  includes at least one of the following generated by nearby vehicle  601 : encoded three-dimensional data  634 , which is encoded three-dimensional data of the request range; and surrounding condition detection result  639  of the request range. Surrounding condition detection result  639  indicates the positions, traveling directions and traveling speeds, etc., of persons and vehicles detected by nearby vehicle  601 . Transmission data  638  may also include information indicating the position, motion, etc., of nearby vehicle  601 . For example, transmission data  638  may include braking information of nearby vehicle  601 . 
     When the received transmission data  638  includes encoded three-dimensional data  634  (Yes in  667 ), decoder  625  decodes encoded three-dimensional data  634  to obtain second three-dimensional data  635  of the SWLD (S 668 ). Stated differently, second three-dimensional data  635  is three-dimensional data (SWLD) that has been generated by extracting data having an amount of features greater than or equal to the threshold from fourth three-dimensional data (WLD). 
     Next, merger  626  merges first three-dimensional data  632  with second three-dimensional data  635 , thereby generating third three-dimensional data  636  (S 669 ). 
     Next, surrounding condition detector  628  detects the surrounding conditions of own vehicle  600  by use of third three-dimensional data  636 , which is a point cloud of a spatial region necessary to detect the surrounding conditions (S 670 ). Note that when the received transmission data  638  includes surrounding condition detection result  639 , surrounding condition detector  628  detects the surrounding conditions of own vehicle  600  by use of surrounding condition detection result  639 , in addition to third three-dimensional data  636 . When the received transmission data  638  includes the braking information of nearby vehicle  601 , surrounding condition detector  628  detects the surrounding conditions of own vehicle  600  by use of such braking information, in addition to third three-dimensional data  636 . 
     Next, autonomous operation controller  629  controls the autonomous operations (self-driving) of own vehicle  600  on the basis of the surrounding condition detection result obtained by surrounding condition detector  628  (S 671 ). Note that the surrounding condition detection result may be presented to the driver via a user interface (UI), etc. 
     Meanwhile, when the request range is not present in step S 663  (No in S 664 ), or stated differently, when information on all spatial regions necessary to detect the surrounding conditions has been created on the basis of sensor information  631 , surrounding condition detector  628  detects the surrounding conditions of own vehicle  600  by use of first three-dimensional data  632 , which is the point cloud of the spatial region necessary to detect the surrounding conditions (S 672 ). Then, autonomous operation controller  629  controls the autonomous operations (self-driving) of own vehicle  600  on the basis of the surrounding condition detection result obtained by surrounding condition detector  628  (S 671 ). 
     Meanwhile, when the received transmission data  638  does not include encoded three-dimensional data  634  (No in S 667 ), or stated differently, when transmission data  638  includes only surrounding condition detection result  639  or the braking information of nearby vehicle  601 , surrounding condition detector  628  detects the surrounding conditions of own vehicle  600  by use of first three-dimensional data  632 , and surrounding condition detection result  639  or the braking information (S 673 ). Then, autonomous operation controller  629  controls the autonomous operations (self-driving) of own vehicle  600  on the basis of the surrounding condition detection result obtained by surrounding condition detector  628  (S 671 ). 
     Next, three-dimensional data transmission device  640 A will be described that transmits transmission data  638  to the above-described three-dimensional data creation device  620 A.  FIG. 32  is a block diagram of such three-dimensional data transmission device  640 A. 
     Three-dimensional data transmission device  640 A shown in  FIG. 32  further includes transmission permissibility judgment unit  646 , in addition to the components of three-dimensional data transmission device  640  shown in  FIG. 28 . Three-dimensional data transmission device  640 A is included in nearby vehicle  601 . 
       FIG. 33  is a flowchart of example operations performed by three-dimensional data transmission device  640 A. First, three-dimensional data creator  641  creates fifth three-dimensional data  652  by use of sensor information  651  detected by the sensor included in nearby vehicle  601  (S 681 ). 
     Next, receiver  642  receives from own vehicle  600  request signal  637  that requests for the transmission of three-dimensional data (S 682 ). Next, transmission permissibility judgment unit  646  determines whether to accept the request indicated by request signal  637  (S 683 ). For example, transmission permissibility judgment unit  646  determines whether to accept the request on the basis of the details previously set by the user. Note that receiver  642  may receive a request from the other end such as a request range beforehand, and transmission permissibility judgment unit  646  may determine whether to accept the request in accordance with the details of such request. For example, transmission permissibility judgment unit  646  may determine to accept the request when the three-dimensional data transmission device has the three-dimensional data of the request range, and not to accept the request when the three-dimensional data transmission device does not have the three-dimensional data of the request range. 
     When determining to accept the request (Yes in S 683 ), three-dimensional data transmission device  640 A sends a permission signal to own vehicle  600 , and receiver  642  receives request range information  633  indicating the request range (S 684 ). Next, extractor  643  extracts the point cloud of the request range from fifth three-dimensional data  652 , which is a point cloud, and creates transmission data  638  that includes sixth three-dimensional data  654 , which is the SWLD of the extracted point cloud (S 685 ). 
     Stated differently, three-dimensional data transmission device  640 A creates seventh three-dimensional data (WLD) from sensor information  651 , and extracts data having an amount of features greater than or equal to the threshold from seventh three-dimensional data (WLD), thereby creating fifth three-dimensional data  652  (SWLD). Note that three-dimensional data creator  641  may create three-dimensional data of a SWLD beforehand, from which extractor  643  may extract three-dimensional data of a SWLD of the request range. Alternatively, extractor  643  may generate three-dimensional data of the SWLD of the request range from the three-dimensional data of the WLD of the request range. 
     Transmission data  638  may include surrounding condition detection result  639  of the request range obtained by nearby vehicle  601  and the braking information of nearby vehicle  601 . Transmission data  638  may include only at least one of surrounding condition detection result  639  of the request range obtained by nearby vehicle  601  and the braking information of nearby vehicle  601 , without including sixth three-dimensional data  654 . 
     When transmission data  638  includes sixth three-dimensional data  654  (Yes in S 686 ), encoder  644  encodes sixth three-dimensional data  654  to generate encoded three-dimensional data  634  (S 687 ). 
     Then, transmitter  645  sends to own vehicle  600  transmission data  638  that includes encoded three-dimensional data  634  (S 688 ). 
     Meanwhile, when transmission data  638  does not include sixth three-dimensional data  654  (No in S 686 ), transmitter  645  sends to own vehicle  600  transmission data  638  that includes at least one of surrounding condition detection result  639  of the request range obtained by nearby vehicle  601  and the braking information of nearby vehicle  601  (S 688 ). 
     The following describes variations of the present embodiment. 
     For example, information transmitted from nearby vehicle  601  may not be three-dimensional data or a surrounding condition detection result generated by the nearby vehicle, and thus may be accurate keypoint information on nearby vehicle  601  itself. Own vehicle  600  corrects keypoint information on the preceding vehicle in the point cloud obtained by own vehicle  600  by use of such keypoint information of nearby vehicle  601 . This enables own vehicle  600  to increase the matching accuracy at the time of self-location estimation. 
     The keypoint information of the preceding vehicle is, for example, three-dimensional point information that includes color information and coordinates information. This allows for the use of the keypoint information of the preceding vehicle independently of the type of the sensor of own vehicle  600 , i.e., regardless of whether the sensor is a laser sensor or a stereo camera. 
     Own vehicle  600  may use the point cloud of a SWLD not only at the time of transmission, but also at the time of calculating the accuracy of self-location estimation. For example, when the sensor of own vehicle  600  is an imaging device such as a stereo camera, own vehicle  600  detects two-dimensional points on an image captured by the camera of own vehicle  600 , and uses such two-dimensional points to estimate the self-location. Own vehicle  600  also creates a point cloud of a nearby object at the same time of estimating the self-location. Own vehicle  600  re-projects the three-dimensional points of the SWLD included in the point cloud onto the two-dimensional image, and evaluates the accuracy of self-location estimation on the basis of an error between the detected points and the re-projected points on the two-dimensional image. 
     When the sensor of own vehicle  600  is a laser sensor such as a LIDAR, own vehicle  600  evaluates the accuracy of self-location estimation on the basis of an error calculated by Interactive Closest Point algorithm by use of the SWLD of the created point cloud of and the SWLD of the three-dimensional map. 
     When a communication state via a base station or a server is poor in, for example, a 5G environment, own vehicle  600  may obtain a three-dimensional map from nearby vehicle  601 . 
     Also, own vehicle  600  may obtain information on a remote region that cannot be obtained from a nearby vehicle, over inter-vehicle communication. For example, own vehicle  600  may obtain information on a traffic accident, etc. that has just occurred at a few hundred meters or a few kilometers away from own vehicle  600  from an oncoming vehicle over a passing communication, or by a relay system in which information is sequentially passed to nearby vehicles. Here, the data format of the data to be transmitted is transmitted as meta-information in an upper layer of a dynamic three-dimensional map. 
     The result of detecting the surrounding conditions and the information detected by own vehicle  600  may be presented to the user via a UI. The presentation of such information is achieved, for example, by superimposing the information onto the screen of the car navigation system or the front window. 
     In the case of a vehicle not supporting self-driving but having the functionality of cruise control, the vehicle may identify a nearby vehicle traveling in the self-driving mode, and track such nearby vehicle. 
     Own vehicle  600  may switch the operation mode from the self-driving mode to the tracking mode to track a nearby vehicle, when failing to estimate the self-location for the reason such as failing to obtain a three-dimensional map or having too large a number of occlusion regions. 
     Meanwhile, a vehicle to be tracked may include a UI which warns the user of that the vehicle is being tracked and by which the user can specify whether to permit tracking. In this case, a system may be provided in which, for example, an advertisement is displayed to the vehicle that is tracking and an incentive is given to the vehicle that is being tracked. 
     The information to be transmitted is basically a SWLD being three-dimensional data, but may also be information that is in accordance with request settings set in own vehicle  600  or public settings set in a preceding vehicle. For example, the information to be transmitted may be a WLD being a dense point cloud, the detection result of the surrounding conditions obtained by the preceding vehicle, or the braking information of the preceding vehicle. 
     Own vehicle  600  may also receive a WLD, visualize the three-dimensional data of the WLD, and present such visualized three-dimensional data to the driver by use of a GUI. In so doing, own vehicle  600  may present the three-dimensional data in which information is color-coded, for example, so that the user can distinguish between the point cloud created by own vehicle  600  and the received point cloud. 
     When presenting the information detected by own vehicle  600  and the detection result of nearby vehicle  601  to the driver via the GUI, own vehicle  600  may present the information in which information is color-coded, for example, so that the user can distinguish between the information detected by own vehicle  600  and the received detection result. 
     As described above, in three-dimensional data creation device  620  according to the present embodiment, three-dimensional data creator  621  creates first three-dimensional data  632  from sensor information  631  detected by a sensor. Receiver  624  receives encoded three-dimensional data  634  that is obtained by encoding second three-dimensional data  635 . Decoder  625  decodes received encoded three-dimensional data  634  to obtain second three-dimensional data  635 . Merger  626  merges first three-dimensional data  632  with second three-dimensional data  635  to create third three-dimensional data  636 . 
     Such three-dimensional data creation device  620  is capable of creating detailed third three-dimensional data  636  by use of created first three-dimensional data  632  and received second three-dimensional data  635 . 
     Also, merger  626  merges first three-dimensional data  632  with second three-dimensional data  635  to create third three-dimensional data  636  that is denser than first three-dimensional data  632  and second three-dimensional data  635 . 
     Second three-dimensional data  635  (e.g., SWLD) is three-dimensional data that is generated by extracting, from fourth three-dimensional data (e.g., WLD), data having an amount of a feature greater than or equal to the threshold. 
     Such three-dimensional data creation device  620  reduces the amount of three-dimensional data to be transmitted. 
     Three-dimensional data creation device  620  further includes searcher  623  that searches for a transmission device that transmits encoded three-dimensional data  634 . Receiver  624  receives encoded three-dimensional data  634  from the transmission device that has been searched out. 
     Such three-dimensional data creation device  620  is, for example, capable of searching for a transmission device having necessary three-dimensional data. 
     Such three-dimensional data creation device further includes request range determiner  622  that determines a request range that is a range of a three-dimensional space, the three-dimensional of which is requested. Searcher  623  transmits request range information  633  indicating the request range to the transmission device. Second three-dimensional data  635  includes the three-dimensional data of the request range. 
     Such three-dimensional data creation device  620  is capable of receiving necessary three-dimensional data, while reducing the amount of three-dimensional data to be transmitted. 
     Also, request range determiner  622  determines, as the request range, a spatial range that includes occlusion region  604  undetectable by the sensor. 
     Also, in three-dimensional data transmission device  640  according to the present embodiment, three-dimensional data creator  641  creates fifth three-dimensional data  652  from sensor information  651  detected by the sensor. Extractor  643  extracts part of fifth three-dimensional data  652  to create sixth three-dimensional data  654 . Encoder  644  encodes sixth three-dimensional data  654  to generate encoded three-dimensional data  634 . Transmitter  645  transmits encoded three-dimensional data  634 . 
     Such three-dimensional data transmission device  640  is capable of transmitting self-created three-dimensional data to another device, while reducing the amount of three-dimensional data to be transmitted. 
     Also, three-dimensional data creator  641  creates seventh three-dimensional data (e.g., WLD) from sensor information  651  detected by the sensor, and extracts, from the seventh three-dimensional data, data having an amount of a feature greater than or equal to the threshold, to create fifth three-dimensional data  652  (e.g., SWLD). 
     Such three-dimensional data creation device  640  reduces the amount of three-dimensional data to be transmitted. 
     Three-dimensional data transmission device  640  further includes receiver  642  that receives, from the reception device, request range information  633  indicating the request range that is the range of a three-dimensional space, the three-dimensional data of which is requested. Extractor  643  extracts the three-dimensional data of the request range from fifth three-dimensional data  652  to create sixth three-dimensional data  654 . Transmitter  645  transmits encoded three-dimensional data  634  to the reception device. 
     Such three-dimensional data transmission device  640  reduces the amount of three-dimensional data to be transmitted. 
     Embodiment 4 
     The present embodiment describes operations performed in abnormal cases when self-location estimation is performed on the basis of a three-dimensional map. 
     A three-dimensional map is expected to find its expanded use in self-driving of a vehicle and autonomous movement, etc. of a mobile object such as a robot and a flying object (e.g., a drone). Example means for enabling such autonomous movement include a method in which a mobile object travels in accordance with a three-dimensional map, while estimating its own location on the map (self-location estimation). 
     The self-location estimation is enabled by matching a three-dimensional map with three-dimensional information on the surrounding of the own vehicle (hereinafter referred to as self-detected three-dimensional data) obtained by a sensor equipped in the own vehicle, such as a rangefinder (e.g., a LiDAR) and a stereo camera to estimate the location of the own vehicle on the three-dimensional map. 
     As in the case of an HD map suggested by HERE Technologies, for example, a three-dimensional map may include not only a three-dimensional point cloud, but also two-dimensional map data such as information on the shapes of roads and intersections, or information that changes in real-time such as information on a traffic jam and an accident. A three-dimensional map includes a plurality of layers such as layers of three-dimensional data, two-dimensional data, and meta-data that changes in real-time, from among which the device can obtain or refer to only necessary data. 
     Point cloud data may be a SWLD as described above, or may include point group data that is different from keypoints. The transmission/reception of point cloud data is basically carried out in one or more random access units. 
     A method described below is used as a method of matching a three-dimensional map with self-detected three-dimensional data. For example, the device compares the shapes of the point groups in each other&#39;s point clouds, and determines that portions having a high degree of similarity among keypoints correspond to the same position. When the three-dimensional map is formed by a SWLD, the device also performs matching by comparing the keypoints that form the SWLD with three-dimensional keypoints extracted from the self-detected three-dimensional data. 
     Here, to enable highly accurate self-location estimation, the following needs to be satisfied: (A) the three-dimensional map and the self-detected three-dimensional data have been already obtained; and (B) their accuracies satisfy a predetermined requirement. However, one of (A) and (B) cannot be satisfied in abnormal cases such as ones described below. 
     1. A three-dimensional map is unobtainable over communication. 
     2. A three-dimensional map is not present, or a three-dimensional map having been obtained is corrupt. 
     3. A sensor of the own vehicle has trouble, or the accuracy of the generated self-detected three-dimensional data is inadequate due to bad weather. 
     The following describes operations to cope with such abnormal cases. The following description illustrates an example case of a vehicle, but the method described below is applicable to mobile objects on the whole that are capable of autonomous movement, such as a robot and a drone. 
     The following describes the structure of the three-dimensional information processing device and its operation according to the present embodiment capable of coping with abnormal cases regarding a three-dimensional map or self-detected three-dimensional data.  FIG. 34  is a block diagram of an example structure of three-dimensional information processing device  700  according to the present embodiment.  FIG. 35  is a flowchart of a three-dimensional information processing method performed by three-dimensional information processing device  700 . 
     Three-dimensional information processing device  700  is equipped, for example, in a mobile object such as a vehicle. As shown in  FIG. 34 , three-dimensional information processing device  700  includes three-dimensional map obtainer  701 , self-detected data obtainer  702 , abnormal case judgment unit  703 , coping operation determiner  704 , and operation controller  705 . 
     Note that three-dimensional information processing device  700  may include a non-illustrated two-dimensional or one-dimensional sensor that detects a structural object or a mobile object around the own vehicle, such as a camera capable of obtaining two-dimensional images and a sensor for one-dimensional data utilizing ultrasonic or laser. Three-dimensional information processing device  700  may also include a non-illustrated communication unit that obtains a three-dimensional map over a mobile communication network, such as 4G and 5G, or via inter-vehicle communication or road-to-vehicle communication. 
     As shown in  FIG. 35 , three-dimensional map obtainer  701  obtains three-dimensional map  711  of the surroundings of the traveling route (S 701 ). For example, three-dimensional map obtainer  701  obtains three-dimensional map  711  over a mobile communication network, or via inter-vehicle communication or road-to-vehicle communication. 
     Next, self-detected data obtainer  702  obtains self-detected three-dimensional data  712  on the basis of sensor information (S 702 ). For example, self-detected data obtainer  702  generates self-detected three-dimensional data  712  on the basis of the sensor information obtained by a sensor equipped in the own vehicle. 
     Next, abnormal case judgment unit  703  conducts a predetermined check of at least one of obtained three-dimensional map  711  and self-detected three-dimensional data  712  to detect an abnormal case (S 703 ). Stated differently, abnormal case judgment unit  703  judges whether at least one of obtained three-dimensional map  711  and self-detected three-dimensional data  712  is abnormal. 
     When the abnormal case is detected in step S 703  (Yes in S 704 ), coping operation determiner  704  determines a coping operation to cope with such abnormal case (S 705 ). Next, operation controller  705  controls the operation of each of the processing units necessary to perform the coping operation (S 706 ). 
     Meanwhile, when no abnormal case is detected in step S 703  (No in S 704 ), three-dimensional information processing device  700  terminates the process. 
     Also, three-dimensional information processing device  700  estimates the location of the vehicle equipped with three-dimensional information processing device  700 , using three-dimensional map  711  and self-detected three-dimensional data  712 . Next, three-dimensional information processing device  700  performs the automatic operation of the vehicle by use of the estimated location of the vehicle. 
     As described above, three-dimensional information processing device  700  obtains, via a communication channel, map data (three-dimensional map  711 ) that includes first three-dimensional position information. The first three-dimensional position information includes, for example, a plurality of random access units, each of which is an assembly of at least one subspace and is individually decodable, the at least one subspace having three-dimensional coordinates information and serving as a unit in which each of the plurality of random access units is encoded. The first three-dimensional position information is, for example, data (SWLD) obtained by encoding keypoints, each of which has an amount of a three-dimensional feature greater than or equal to a predetermined threshold. 
     Three-dimensional information processing device  700  also generates second three-dimensional position information (self-detected three-dimensional data  712 ) from information detected by a sensor. Three-dimensional information processing device  700  then judges whether one of the first three-dimensional position information and the second three-dimensional position information is abnormal by performing, on one of the first three-dimensional position information and the second three-dimensional position information, a process of judging whether an abnormality is present. 
     Three-dimensional information processing device  700  determines a coping operation to cope with the abnormality when one of the first three-dimensional position information and the second three-dimensional position information is judged to be abnormal. Three-dimensional information processing device  700  then executes a control that is required to perform the coping operation. 
     This structure enables three-dimensional information processing device  700  to detect an abnormality regarding one of the first three-dimensional position information and the second three-dimensional position information, and to perform a coping operation therefor. 
     The following describes coping operations used for the abnormal case 1 in which three-dimensional map  711  is unobtainable via communication. 
     Three-dimensional map  711  is necessary to perform self-location estimation, and thus the vehicle needs to obtain three-dimensional map  711  via communication when not having obtained in advance three-dimensional map  711  corresponding to the route to the destination. In some cases, however, the vehicle cannot obtain three-dimensional map  711  of the traveling route due to a reason such as a congested communication channel and a deteriorated environment of radio wave reception. 
     Abnormal case judgment unit  703  judges whether three-dimensional map  711  of the entire section on the route to the destination or a section within a predetermined range from the current position has already been obtained, and judges that the current condition applies to the abnormal case 1 when three-dimensional map  711  has not been obtained yet. Stated differently, abnormal case judgment unit  703  judges whether three-dimensional map  711  (the first three-dimensional position information) is obtainable via a communication channel, and judges that three-dimensional map  711  is abnormal when three-dimensional map  711  is unobtainable via a communication channel. 
     When the current condition is judged to be the abnormal case 1, coping operation determiner  704  selects one of the two types of coping operations: (1) continue the self-location estimation; and (2) terminate the self-location estimation. 
     First, a specific example of the coping operation to continue the self-location estimation will be described. Three-dimensional map  711  of the route to the destination is necessary to continue the self-location estimation. 
     For example, the vehicle identifies a place, within the range of three-dimensional map  711  having been obtained, in which the use of a communication channel is possible. The vehicle moves to such identified place, and obtains three-dimensional map  711 . Here, the vehicle may obtain the whole three-dimensional map  711  to the destination, or may obtain three-dimensional map  711  on random access units within the upper limit capacity of a storage of the own vehicle, such as a memory and an HDD. 
     Note that the vehicle may separately obtain communication conditions on the route, and when the communication conditions on the route are predicted to be poor, the vehicle may obtain in advance three-dimensional map  711  of a section in which communication conditions are predicted to be poor, before arriving at such section, or obtain in advance three-dimensional map  711  of the maximum range obtainable. Stated differently, three-dimensional information processing device  700  predicts whether the vehicle will enter an area in which communication conditions are poor. When the vehicle is predicted to enter an area in which communication conditions are poor, three-dimensional information processing device  700  obtains three-dimensional map  711  before the vehicle enters such area. 
     Alternatively, the vehicle may identify a random access unit that forms the minimum three-dimensional map  711 , the range of which is narrower than that of the normal times, required to estimate the location of the vehicle on the route, and receive a random access unit having been identified. Stated differently, three-dimensional information processing device  700  may obtain, via a communication channel, third three-dimensional position information having a narrower range than the range of the first three-dimensional position information, when three-dimensional map  711  (the first three-dimensional position information) is unobtainable via the communication channel. 
     Also, when being unable to access a server that distributes three-dimensional map  711 , the vehicle may obtain three-dimensional map  711  from a mobile object that has already obtained three-dimensional map  711  of the route to the destination and that is capable of communicating with the own vehicle, such as another vehicle traveling around the own vehicle. 
     Next, a specific example of the coping operation to terminate the self-location estimation will be described. Three-dimensional map  711  of the route to the destination is unnecessary in this case. 
     For example, the vehicle notifies the driver of that the vehicle cannot maintain the functionally of automatic operation, etc. that is performed on the basis of the self-location estimation, and shifts the operation mode to a manual mode in which the driver operates the vehicle. 
     Automatic operation is typically carried out when self-location estimation is performed, although there may be a difference in the level of automatic operation in accordance with the degree of human involvement. Meanwhile, the estimated location of the vehicle can also be used as navigation information, etc. when the vehicle is operated by a human, and thus the estimated location of the vehicle is not necessarily used for automatic operation. 
     Also, when being unable to use a communication channel that the vehicle usually uses, such as a mobile communication network (e.g., 4G and 5G), the vehicle checks whether three-dimensional map  711  is obtainable via another communication channel, such as road-to-vehicle Wi-Fi (registered trademark) or millimeter-wave communication, or inter-vehicle communication, and switches to one of these communication channels via which three-dimensional map  711  is obtainable. 
     When being unable to obtain three-dimensional map  711 , the vehicle may obtain a two-dimensional map to continue automatic operation by use of such two-dimensional map and self-detected three-dimensional data  712 . Stated differently, when being unable to obtain three-dimensional map  711  via a communication channel, three-dimensional information processing device  700  may obtain, via a communication channel, map data that includes two-dimensional position information (a two-dimensional map) to estimate the location of the vehicle by use of the two-dimensional position information and self-detected three-dimensional data  712 . 
     More specifically, the vehicle uses the two-dimensional map and self-detected three-dimensional data  712  to estimate its own location, and uses self-detected three-dimensional data  712  to detect a vehicle, a pedestrian, an obstacle, etc. around the own vehicle. 
     Here, the map data such as an HD map is capable of including, together with three-dimensional map  711  formed by a three-dimensional point cloud: two-dimensional map data (a two-dimensional map); simplified map data obtained by extracting, from the two-dimensional map data, characteristic information such as a road shape and an intersection; and meta-data representing real-time information such as a traffic jam, an accident, and a roadwork. For example, the map data has a layer structure in which three-dimensional data (three-dimensional map  711 ), two-dimensional data (a two-dimensional map), and meta-data are disposed from the bottom layer in the stated order. 
     Here, the two-dimensional data is smaller in data size than the three-dimensional data. It may be thus possible for the vehicle to obtain the two-dimensional map even when communication conditions are poor. Alternatively, the vehicle can collectively obtain the two-dimensional map of a wide range in advance when in a section in which communication conditions are good. The vehicle thus may receive a layer including the two-dimensional map without receiving three-dimensional map  711 , when communication conditions are poor and it is difficult to obtain three-dimensional map  711 . Note that the meta-data is small in data size, and thus the vehicle receives the meta-data without fail, regardless, for example, of communication conditions. 
     Example methods of self-location estimation using the two-dimensional map and self-detected three-dimensional data  712  include two methods described below. 
     A first method is to perform matching of two-dimensional features. More specifically, the vehicle extracts two-dimensional features from self-detected three-dimensional data  712  to perform matching between the extracted two-dimensional features and the two-dimensional map. 
     For example, the vehicle projects self-detected three-dimensional data  712  onto the same plane as that of the two-dimensional map, and matches the resulting two-dimensional data with the two-dimensional map. Such matching is performed by use of features of the two-dimensional images extracted from the two-dimensional data and the two-dimensional map. 
     When three-dimensional map  711  includes a SWLD, two-dimensional features on the same plane as that of the two-dimensional map may be stored in three-dimensional map  711  together with three-dimensional features of keypoints in a three-dimensional space. For example, identification information is assigned to two-dimensional features. Alternatively, two-dimensional features are stored in a layer different from the layers of the three-dimensional data and the two-dimensional map, and the vehicle obtains data of the two-dimensional features together with the two-dimensional map. 
     When the two-dimensional map shows, on the same map, information on positions having different heights from the ground (i.e., positions that are not on the same plane), such as a white line inside a road, a guardrail, and a building, the vehicle extracts features from data on a plurality of heights in self-detected three-dimensional data  712 . 
     Also, information indicating a correspondence between keypoints on the two-dimensional map and keypoints on three-dimensional map  711  may be stored as meta-information of the map data. 
     A second method is to perform matching of three-dimensional features. More specifically, the vehicle obtains three-dimensional features corresponding to keypoints on the two-dimensional map, and matches the obtained three-dimensional features with three-dimensional features in self-detected three-dimensional data  712 . 
     More specifically, three-dimensional features corresponding to keypoints on the two-dimensional map are stored in the map data. The vehicle obtains such three-dimensional features when obtaining the two-dimensional map. Note that when three-dimensional map  711  includes a SWLD, information is provided that identifies those keypoints, among the keypoints in the SWLD, that correspond to keypoints on the two-dimensional map. Such identification information enables the vehicle to determine three-dimensional features that should be obtained together with the two-dimensional map. In this case, the representation of two-dimensional positions is only required, and thus the amount of data can be reduced compared to the case of representing three-dimensional positions. 
     The use of the two-dimensional map to perform self-location estimation decreases the accuracy of the self-location estimation compared to the case of using three-dimensional map  711 . For this reason, the vehicle judges whether the vehicle can continue automatic operation by use of the location having decreased estimation accuracy, and may continue automatic operation only when judging that the vehicle can continue automatic operation. 
     Whether the vehicle can continue automatic operation is affected by an environment in which the vehicle is traveling such as whether the road on which the vehicle is traveling is a road in an urban area or a road accessed less often by another vehicle or a pedestrian, such as an expressway, and the width of a road or the degree of congestion of a road (the density of vehicles or pedestrians). It is also possible to dispose, in a premise of a business place, a town, or inside a building, markers recognized by a senor such as a camera. Since a two-dimensional sensor is capable of highly accurate recognition of such markers in the specified areas, highly accurate self-location estimation is enabled by, for example, incorporating information on the positions of the markers into the two-dimensional map. 
     Also, by incorporating, into the map, identification information indicating whether each area corresponds to a specified area, for example, the vehicle can judge whether such vehicle is currently in a specified area. When in a specified area, the vehicle judges that the vehicle can continue automatic operation. As described above, the vehicle may judge whether the vehicle can continue automatic operation on the basis of the accuracy of self-location estimation that uses the two-dimensional map or an environment in which the vehicle is traveling. 
     As described above, three-dimensional information processing device  700  judges whether to perform automatic operation that utilizes the location of the vehicle having been estimated by use of the two-dimensional map and self-detected three-dimensional data  712 , on the basis of an environment in which the vehicle is traveling (a traveling environment of the mobile object). 
     Alternatively, the vehicle may not judge whether the vehicle can continue automatic operation, but may switch levels (modes) of automatic operation in accordance with the accuracy of self-location estimation or the traveling environment of the vehicle. Here, to switch levels (modes) of automatic operation means, for example, to limit the speed, increase the degree of driver operation (lower the automatic level of automatic operation), switch to a mode in which the vehicle obtains information on the operation of a preceding vehicle to refer to it for its own operation, switch to a mode in which the vehicle obtains information on the operation of a vehicle heading for the same destination to use it for automatic operation, etc. 
     The map may also include information, associated with the position information, indicating a recommendation level of automatic operation for the case where the two-dimensional map is used for self-location estimation. The recommendation level may be meta-data that dynamically changes in accordance with the volume of traffic, etc. This enables the vehicle to determine a level only by obtaining information from the map without needing to judge a level every time an environment, etc. around the vehicle changes. Also, it is possible to maintain a constant level of automatic operation of individual vehicles by such plurality of vehicles referring to the same map. Note that the recommendation level may not be “recommendation,” and thus such level may be a mandatory level that should be abided by. 
     The vehicle may also switch the level of automatic operation in accordance with the presence or absence of the driver (whether the vehicle is manned or unmanned). For example, the vehicle lowers the level of automatic operation when the vehicle is manned, and terminates automatic operation when unmanned. The vehicle may recognize a pedestrian, a vehicle, and a traffic sign around the vehicle to determine a position where the vehicle can stop safely. Alternatively, the map may include position information indicating positions where the vehicle can stop safely, and the vehicle refers to such position information to determine a position where the vehicle can stop safely. 
     The following describes coping operations to cope with the abnormal case 2 in which three-dimensional map  711  is not present, or three-dimensional map  711  having been obtained is corrupt. 
     Abnormal case judgment unit  703  checks whether the current condition applies to one of (1) three-dimensional map  711  of part or the entirety of the section on the route to the destination not being present in a distribution server, etc. to which the vehicle accesses, and thus unobtainable; and (2) part or the entirety of obtained three-dimensional map  711  being corrupt. When one of these cases applies, the vehicle judges that the current condition applies to the abnormal case 2. Stated differently, abnormal case judgment unit  703  judges whether the data of three-dimensional map  711  has integrity, and judges that three-dimensional map  711  is abnormal when the data of three-dimensional map  711  has no integrity. 
     When the current condition is judged to apply to the abnormal case 2, coping operations described below are performed. First, an example coping operation for the case where (1) three-dimensional map  711  is unobtainable will be described. 
     For example, the vehicle sets a route that avoids a section, three-dimensional map  711  of which is not present. 
     When being unable to set an alternative route for a reason that an alternative route is not present, an alternative route is present but its distance is substantially longer, or etc., the vehicle sets a route that includes a section, three-dimensional map  711  of which is not present. When in such section, the vehicle notifies the driver of the necessity to switch to another operation mode, and switches the operation mode to the manual mode. 
     When the current condition applies to (2) in which part or the entirety of obtained three-dimensional map  711  is corrupt, a coping operation described below is performed. 
     The vehicle identifies a corrupted portion of three-dimensional map  711 , requests for the data of such corrupted portion via communication, obtains the data of the corrupted portion, and updates three-dimensional map  711  using the obtained data. In so doing, the vehicle may specify the corrupted portion on the basis of position information in three-dimensional map  711 , such as absolute coordinates and relative coordinates, or may specify the corrupted portion by an index number, etc. assigned to a random access unit that forms the corrupted portion. In such case, the vehicle replaces the random access unit including the corrupted portion with a random access unit having been obtained. 
     The following describes coping operations to cope with the abnormal case 3 in which the vehicle fails to generate self-detected three-dimensional data  712  due to trouble of a sensor of the own vehicle or bad weather. 
     Abnormal case judgment unit  703  checks whether an error in generated self-detected three-dimensional data  712  falls within an acceptable range, and judges that the current condition applies to the abnormal case 3 when such error is beyond the acceptable range. Stated differently, abnormal case judgment unit  703  judges whether the data accuracy of generated self-detected three-dimensional data  712  is higher than or equal to the reference value, and judges that self-detected three-dimensional data  712  is abnormal when the data accuracy of generated self-detected three-dimensional data  712  is not higher than or equal to the reference value. 
     A method described below is used to check whether an error in generated self-detected three-dimensional data  712  is within the acceptable range. 
     A spatial resolution of self-detected three-dimensional data  712  when the own vehicle is in normal operation is determined in advance on the basis of the resolutions in the depth and scanning directions of a three-dimensional sensor of the own vehicle, such as a rangefinder and a stereo camera, or on the basis of the density of generatable point groups. Also, the vehicle obtains the spatial resolution of three-dimensional map  711  from meta-information, etc. included in three-dimensional map  711 . 
     The vehicle uses the spatial resolutions of self-detected three-dimensional data  712  and three-dimensional map  711  to estimate a reference value used to specify a matching error in matching self-detected three-dimensional data  712  with three-dimensional map  711  on the basis of three-dimensional features, etc. Used as the matching error is an error in three-dimensional features of the respective keypoints, statistics such as the mean value of errors in three-dimensional features among a plurality of keypoints, or an error in spatial distances among a plurality of keypoints. The acceptable range of a deviation from the reference value is set in advance. 
     The vehicle judges that the current condition applies to the abnormal case 3 when the matching error between self-detected three-dimensional data  712  generated before or in the middle of traveling and three-dimensional map  711  is beyond the acceptable range. 
     Alternatively, the vehicle may use a test pattern having a known three-dimensional shape for accuracy check to obtain, before the start of traveling, for example, self-detected three-dimensional data  712  corresponding to such test pattern, and judge whether the current condition applies to the abnormal case 3 on the basis of whether a shape error is within the acceptable range. 
     For example, the vehicle makes the above judgment before every start of traveling. Alternatively, the vehicle makes the above judgment at a constant time interval while traveling, thereby obtaining time-series variations in the matching error. When the matching error shows an increasing trend, the vehicle may judge that the current condition applies to the abnormal case 3 even when the error is within the acceptable range. Also, when an abnormality can be predicted on the basis of the time-series variations, the vehicle may notify the user of that an abnormality is predicted by displaying, for example, a message that prompts the user for inspection or repair. The vehicle may discriminate between an abnormality attributable to a transient factor such as bad weather and an abnormality attributable to sensor trouble on the basis of time-series variations, and notify the user only of an abnormality attributable to sensor trouble. 
     When the current condition is judged to be the abnormal case 3, the vehicle performs one, or selective ones of the following three types of coping operations: (1) operate an alternative emergency sensor (rescue mode); (2) switch to another operation mode; and (3) calibrate the operation of a three-dimensional sensor. 
     First, the coping operation (1) operate an alternative emergency sensor will be described. The vehicle operates an alternative emergency sensor that is different from a three-dimensional sensor used for normal operation. Stated differently, when the accuracy of generated self-detected three-dimensional data  712  is not higher than or equal to the reference value, three-dimensional information processing device  700  generates self-detected three-dimensional data  712  (fourth three-dimensional position information) from information detected by the alternative sensor that is different from a usual sensor. 
     More specifically, when obtaining self-detected three-dimensional data  712  in a combined use of a plurality of cameras or LiDARs, the vehicle identifies a malfunctioning sensor, on the basis of a direction, etc. in which the matching error of self-detected three-dimensional data  712  is beyond the acceptable range. Subsequently, the vehicle operates an alternative sensor corresponding to such malfunctioning sensor. 
     The alternative sensor may be a three-dimensional sensor, a camera capable of obtaining two-dimensional images, or a one-dimensional sensor such as an ultrasonic sensor. The use of an alternative sensor other than a three-dimensional sensor can result in a decrease in the accuracy of self-location estimation or the failure to perform self-location estimation. The vehicle thus may switch automatic operation modes depending on the type of an alternative sensor. 
     When an alternative sensor is a three-dimensional sensor, for example, the vehicle maintains the current automatic operation mode. When an alternative sensor is a two-dimensional sensor, the vehicle switches the operation mode from the full automatic operation mode to the semi-automatic operation mode that requires human operation. When an alternative sensor is a one-dimensional sensor, the vehicle switches the operation mode to the manual mode that performs no automatic braking control. 
     Alternatively, the vehicle may switch automatic operation modes on the basis of a traveling environment. When an alternative sensor is a two-dimensional sensor, for example, the vehicle maintains the full automatic operation mode when traveling on an expressway, and switches the operation mode to the semi-automatic operation mode when traveling in an urban area. 
     Also, even when no alternative sensor is available, the vehicle may continue the self-location estimation so long as a sufficient number of keypoints are obtainable only by normally operating sensors. Since detection cannot work in a specific direction in this case, the vehicle switches the current operation mode to the semi-automatic operation mode or the manual mode. 
     Next, the coping operation (2) switch to another operation mode will be described. The vehicle switches the current operation mode from the automatic operation mode to the manual mode. The vehicle may continue automatic operation until arriving at the shoulder of the road, or another place where the vehicle can stop safely, and then stop there. The vehicle may switch the current operation mode to the manual mode after stopping. As described above, three-dimensional information processing device  700  switches the automatic operation mode to another mode when the accuracy of generated self-detected three-dimensional data  712  is not higher than or equal to the reference value. 
     Next, the coping operation (3) calibrate the operation of a three-dimensional sensor will be described. The vehicle identifies a malfunctioning three-dimensional sensor from a direction, etc. in which an matching error is occurring, and calibrates the identified sensor. More specifically, when a plurality of LiDARs or cameras are used as sensors, an overlapped portion is included in a three-dimensional space reconstructed by each of the sensors. Stated differently, data corresponding to such overlapped portion is obtained by a plurality of sensors. However, a properly operating sensor and a malfunctioning sensor obtain different three-dimensional point group data corresponding to the overlapped portion. The vehicle thus calibrates the origin point of the LiDAR or adjusts the operation for a predetermined part such as one responsible for camera exposure and focus so that the malfunctioning sensor can obtain the data of a three-dimensional point group equivalent to that obtained by a properly operating sensor. 
     When the matching error falls within the acceptable range as a result of such adjustment, the vehicle maintains the previous operation mode. Meanwhile, when the matching accuracy fails to fall within the acceptable range after such adjustment, the vehicle performs one of the above coping operations: (1) operate an alternative emergency sensor; and (2) switch to another operation mode. 
     As described above, three-dimensional information processing device  700  calibrates a sensor operation when the data accuracy of generated self-detected three-dimensional data  712  is not higher than or equal to the reference value. 
     The following describes a method of selecting a cooping operation. A coping operation may be selected by the user such as a driver, or may be automatically selected by the vehicle without user&#39;s involvement. 
     The vehicle may switch controls in accordance with whether the driver is onboard. For example, when the driver is onboard, the vehicle prioritizes the manual mode. Meanwhile, when the driver is not onboard, the vehicle prioritizes the mode to move to a safe place and stop. 
     Three-dimensional map  711  may include information indicating places to stop as meta-information. Alternatively, the vehicle may issue, to a service firm that manages operation information on a self-driving vehicle, a request to send a reply indicating a place to stop, thereby obtaining information on the place to stop. 
     Also, when the vehicle travels on a fixed route, for example, the operation mode of the vehicle may be switched to a mode in which an operator controls the operation of the vehicle via a communication channel. It is highly dangerous when there is a failure in the function of self-location estimation especially when the vehicle is traveling in the full automatic operation mode. When any abnormal case is detected or a detected abnormality cannot be fixed, the vehicle notifies, via a communication channel, the service firm that manages the operation information of the occurrence of the abnormality. Such service firm may notify vehicles, etc. traveling around such vehicle in trouble of the presence of a vehicle having an abnormality or that they should clear a nearby space for the vehicle to stop. 
     The vehicle may also travel at a decreased speed compared to normal times when any abnormal case has been detected. 
     When the vehicle is a self-driving vehicle from a vehicle dispatch service such as a taxi, and an abnormal case occurs in such vehicle, the vehicle contacts an operation control center, and then stops at a safe place. The firm of the vehicle dispatch service dispatches an alternative vehicle. The user of such vehicle dispatch service may operate the vehicle instead. In these cases, fee discount or benefit points may be provided in combination. 
     In the description of the coping operations for the abnormal case 1, self-location estimation is performed on the basis of the two-dimensional map, but self-location estimation may be performed also in normal times by use of the two-dimensional map.  FIG. 36  is a flowchart of self-location estimation processes performed in such case. 
     First, the vehicle obtains three-dimensional map  711  of the surroundings of the traveling route (S 711 ). The vehicle then obtains self-detected three-dimensional data  712  on the basis of sensor information (S 712 ). 
     Next, the vehicle judges whether three-dimensional map  711  is necessary for self-location estimation (S 713 ). More specifically, the vehicle judges whether three-dimensional map  711  is necessary on the basis of the accuracy of its location having been estimated by use of the two-dimensional map and the traveling environment. For example, a method similar to the above-described coping operations for the abnormal case 1 is used. 
     When judging that three-dimensional map  711  is not necessary (No in S 714 ), the vehicle obtains a two-dimensional map (S 715 ). In so doing, the vehicle may obtain additional information together that is mentioned when the coping operations for the abnormal case 1 have been described. Alternatively, the vehicle may generate a two-dimensional map from three-dimensional map  711 . For example, the vehicle may generate a two-dimensional map by cutting out any plane from three-dimensional map  711 . 
     Next, the vehicle performs self-location estimation by use of self-detected three-dimensional data  712  and the two-dimensional map (S 716 ). Note that a method of self-location estimation by use of a two-dimensional map is similar to the above-described coping operations for the abnormal case 1. 
     Meanwhile, when judging that three-dimensional map  711  is necessary (Yes in S 714 ), the vehicle obtains three-dimensional map  711  (S 717 ). Then, the vehicle performs self-location estimation by use of self-detected three-dimensional data  712  and three-dimensional map  711  (S 718 ). 
     Note that the vehicle may selectively decide on which one of the two-dimensional map and three-dimensional map  711  to basically use, in accordance with a speed supported by a communication device of the own vehicle or conditions of a communication channel. For example, a communication speed that is required to travel while receiving three-dimensional map  711  is set in advance, and the vehicle may basically use the two-dimensional map when the communication speed at the time of traveling is less than or equal to the such set value, and basically use three-dimensional map  711  when the communication speed at the time of traveling is greater than the set value. Note that the vehicle may basically use the two-dimensional map without judging which one of the two-dimensional map and the three-dimensional map to use. 
     Although the three-dimensional information processing device according to the embodiments of the present disclosure has been described above, the present disclosure is not limited to such embodiments. 
     Note that each of the processing units included in the three-dimensional information processing device according to the embodiments is implemented typically as a large-scale integration (LSI), which is an integrated circuit (IC). They may take the form of individual chips, or one or more or all of them may be encapsulated 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. 
     Also, the present disclosure may be embodied as a three-dimensional information processing method performed by the three-dimensional information processing device. 
     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. 
     Although the three-dimensional information processing device according to one or more aspects has been described on the basis of the embodiments, the present disclosure is not limited to such embodiments. The one or more aspects may thus include an embodiment achieved by making various modifications to the above embodiments that can be conceived by those skilled in the art as well as an embodiment achieved by combining structural components in different embodiments, without materially departing from the spirit of the present disclosure. 
     Embodiment 5 
     Other application examples of the configurations of the image processing method and apparatus described in each embodiment described above and a system using the application examples will be described. The system is applicable to an increasingly intelligent video system with object space extending to a wider area. For example, the system is applicable to (1) a monitoring system mounted in a security camera of a store or a factory, a vehicle-mounted camera of the police or the like, (2) a transportation information system using a camera owned by an individual person, each vehicle-mounted camera, a camera installed in a road or the like, (3) an environmental research or delivery system using a remote-controllable or auto-controllable apparatus such as a drone, and (4) a content transmission and reception system of a video or the like using a camera installed in an entertainment facility, a stadium or the like, a moving camera such as a drone, a camera owned by an individual person or the like. 
       FIG. 37  is a diagram illustrating a configuration of video information processing system ex 100  according to the present embodiment. The present embodiment describes an example of preventing occurrence of a blind spot and an example of prohibiting capturing of a specific area. 
     Video information processing system ex 100  illustrated in  FIG. 37  includes video information processing apparatus ex 101 , a plurality of cameras ex 102 , and video reception apparatus ex 103 . Note that video information processing system ex 100  does not necessarily need to include video reception apparatus ex 103 . 
     Video information processing apparatus ex 101  includes storage ex 111  and analyzer ex 112 . Each of N cameras ex 102  has a function of capturing videos and a function of transmitting captured video data to video information processing apparatus ex 101 . Moreover, camera ex 102  may have a function of displaying a video that is being captured. Note that camera ex 102  may code a captured video signal by using a coding scheme such as HEVC or H.264, and may then transmit the coded video signal to video information processing apparatus ex 101 , or camera ex 102  may transmit the video data that is not coded to video information processing apparatus ex 101 . 
     Here, each camera ex 102  is a fixed camera such as a monitoring camera, a moving camera mounted in a radio-controlled unmanned flight vehicle, a vehicle or the like, or a user camera owned by a user. 
     The moving camera receives an instruction signal transmitted from video information processing apparatus ex 101 , and changes a position or capturing direction of the moving camera itself in response to the received instruction signal. 
     Moreover, time of the plurality of cameras ex 102  is calibrated by using time information of a server or a reference camera prior to start of capturing. Moreover, spatial positions of the plurality of cameras ex 102  are calibrated based on how an object in space to be captured is captured or a relative position from a reference camera. 
     Storage ex 111  in information processing apparatus ex 101  stores the video data transmitted from N cameras ex 102 . 
     Analyzer ex 112  detects a blind spot from the video data stored in storage ex 111 , and transmits to the moving camera the instruction signal that indicates an instruction to the moving camera for preventing occurrence of a blind spot. The moving camera moves in response to the instruction signal, and continues capturing. 
     Analyzer ex 112  detects a blind spot by using Structure from Motion (SfM), for example. SfM is a technique of restoring a three-dimensional shape of a subject from a plurality of videos captured from different positions, and SfM is widely known as a shape restoration technology of estimating a subject shape and a camera position simultaneously. For example, analyzer ex 112  restores the three-dimensional shape in the facility or in the stadium from the video data stored in storage ex 111  by using SfM, and detects as a blind spot an area that cannot be restored. 
     Note that when the position and capturing direction of camera ex 102  are fixed and information of the position and capturing direction is known, analyzer ex 112  may perform SfM by using these pieces of known information. Moreover, when the position and capturing direction of the moving camera can be acquired with, for example, a GPS and angle sensor in the moving camera, the moving camera may transmit information of the position and capturing direction of the moving camera to analyzer ex 112 , and analyzer ex 112  may perform SfM by using the transmitted information of the position and the capturing direction. 
     Note that a method for detecting a blind spot is not limited to the above-described method using SfM. For example, analyzer ex 112  may use information from a depth sensor such as a laser range finder, to know a spatial distance of the object to be captured. Moreover, when an image includes a marker that is set in space in advance or a specific object, analyzer ex 112  may detect information of the camera position, capturing direction, and zoom magnification from the size of the marker or the object. Thus, analyzer ex 112  detects a blind spot by using any method that enables detection of the capturing area of each camera. Moreover, analyzer ex 112  may acquire, for example, information of a mutual positional relationship between a plurality of objects to be captured, from video data or a proximity sensor, and analyzer ex 112  may identify an area where a blind spot is highly likely to occur, based on the acquired positional relationship. 
     Here, the blind spot includes not only a portion having no video in an area to be captured but also a portion having poor image quality as compared to other portions, and a portion having no predetermined image quality. This portion to be detected may be set appropriately according to the configuration or purpose of the system. For example, required image quality of a specific subject in space to be captured may be set high. Moreover, conversely, the required image quality of a specific area in space to be captured may be set low, and the required image quality may be set such that the area is not determined to be a blind spot even when no video is captured. 
     Note that the above-described image quality includes various pieces of information regarding a video, such as area occupied by a subject to be captured in the video (for example, a number of pixels), or whether the video is focused on the subject to be captured. Based on these pieces of information or combination thereof, whether the area is a blind spot may be determined. 
     Note that detection of the area that is actually a blind spot is described above, but the area that needs to be detected in order to prevent occurrence of a blind spot is not limited to the area that is actually a blind spot. For example, when a plurality of objects to be captured exists and at least part of the objects is moving, a new blind spot is likely to occur because another object to be captured enters between a certain object to be captured and a camera. Meanwhile, analyzer ex 112  may detect movement of the plurality of objects to be captured from, for example, the captured video data, and analyzer ex 112  may estimate the area that is likely to become a new blind spot, based on the detected movement of the plurality of objects to be captured and positional information of camera ex 102 . In this case, video information processing apparatus ex 101  may transmit the instruction signal to the moving camera to capture the area that is likely to become a blind spot, and video information processing apparatus ex 101  may prevent occurrence of a blind spot. 
     Note that when there is a plurality of moving cameras, video information processing apparatus ex 101  needs to select any of the moving cameras to which the instruction signal is to be transmitted in order to cause the moving camera to capture a blind spot or an area that is likely to become a blind spot. Moreover, when there is a plurality of moving cameras and there is a plurality of blind spots or areas that are likely to become blind spots, video information processing apparatus ex 101  needs to determine which blind spot or area that is likely to become a blind spot each of the plurality of moving cameras is to capture. For example, video information processing apparatus ex 101  selects the moving camera closest to a blind spot or an area that is likely to become a blind spot, based on a position of a blind spot or an area that is likely to become a blind spot, and a position of an area each moving camera is capturing. Moreover, video information processing apparatus ex 101  may determine for each camera whether a new blind spot occurs when video data which the moving camera is currently capturing is not obtained, and video information processing apparatus ex 101  may select the moving camera that is determined that a blind spot does not occur even when the video data which is currently being captured is not obtained. 
     The above-described configuration enables video information processing apparatus ex 101  to prevent occurrence of a blind spot by detecting a blind spot and transmitting the instruction signal to the moving camera so as to prevent the blind spot. 
     Variation 1 
     Note that the example of transmitting the instruction signal for instructing the moving camera to move is described above; however, the instruction signal may be a signal for instructing the user of the user camera to move. For example, the user camera displays an instruction image that instructs the user to change the direction of the camera, based on the instruction signal. Note that the user camera may display the instruction image that indicates a movement path on a map, as the user movement instruction. Moreover, in order to improve the quality of the acquired image, the user camera may display detailed capturing instructions such as the capturing direction, an angle, an angle of view, image quality, and movement of the capturing area. Further, video information processing apparatus ex 101  may automatically control such feature data of camera ex 102  regarding capturing when the feature data is controllable on a video information processing apparatus ex 101  side. 
     Here, the user camera is, for example, a smartphone, a tablet terminal, a wearable terminal, or a head mounted display (HMD) that a spectator in the stadium or a guard in the facility carries. 
     Moreover, a display terminal that displays the instruction image does not need to be identical to the user camera that captures video data. For example, the user camera may transmit the instruction signal or the instruction image to the display terminal associated with the user camera in advance, and the display terminal may display the instruction image. Moreover, information of the display terminal corresponding to the user camera may be registered in video information processing apparatus ex 101  in advance. In this case, video information processing apparatus ex 101  may cause the display terminal to display the instruction image by transmitting the instruction signal directly to the display terminal corresponding to the user camera. 
     Variation 2 
     Analyzer ex 112  may generate a free viewpoint video (three-dimensional reconfiguration data), for example, by using SfM to restore the three-dimensional shape in the facility or in the stadium from the video data stored in storage ex 111 . This free viewpoint video is stored in storage ex 111 . Video information processing apparatus ex 101  reads from storage ex 111  the video data according to visual field information (and/or viewpoint information) transmitted from video reception apparatus ex 103 , and transmits the read video data to video reception apparatus ex 103 . Note that video reception apparatus ex 103  may be one of the plurality of cameras. 
     Variation 3 
     Video information processing apparatus ex 101  may detect a capturing prohibited area. In this case, analyzer ex 112  analyzes the captured image, and when the moving camera is capturing the capturing prohibited area, analyzer ex 112  transmits a capturing prohibition signal to the moving camera. The moving camera stops capturing while receiving the capturing prohibition signal. 
     For example, analyzer ex 112  matches three-dimensional virtual space restored by using SfM with the captured video, and accordingly analyzer ex 112  determines whether the moving camera set in advance in space is capturing the capturing prohibited area. Alternatively, analyzer ex 112  determines whether the moving camera is capturing the capturing prohibited area, by using a marker or characteristic object placed in space as a trigger. The capturing prohibited area is, for example, a rest room in the facility or in the stadium. 
     Moreover, when the user camera is capturing the capturing prohibited area, the user camera may notify the user of a fact that the current place is a capturing prohibited place, by causing a display connected wirelessly or with wires to display a message, or by outputting a sound or voice from a speaker or an earphone. 
     For example, a fact that capturing in the current direction of the camera orientation is prohibited is displayed as the message. Alternatively, the capturing prohibited area and the current capturing area are indicated on a displayed map. Moreover, the capturing is automatically resumed, for example, when the capturing prohibition signal is no longer output. Moreover, the capturing may be resumed when the capturing prohibition signal is not output and the user performs operations for resuming the capturing. Moreover, when the capturing is stopped and resumed twice or more in a short period, calibration may be performed again. Moreover, notification for checking the current position or for prompting movement may be given to the user. 
     Moreover, in a case of special work such as the police, pass code or fingerprint authentication or the like that disables such a function may be used for recording. Further, even in such a case, when the video of the capturing prohibited area is displayed or stored outside, image processing such as mosaic may be performed automatically. 
     The above configuration enables video information processing apparatus ex 101  to set a certain area as the capturing prohibited area by performing determination of capturing prohibition and giving the user notification for stopping capturing. 
     Variation 4 
     Since it is necessary to collect videos of the plurality of viewpoints in order to construct three-dimensional virtual space from the videos, video information processing system ex 100  sets an incentive for a user who transmits a captured video. For example, video information processing apparatus ex 101  distributes videos with no charge or at discount rate to the user that transmits a video, or gives the user who transmits a video a point having a monetary value that can be used in an online or off-line store or in a game, or a point having a non-monetary value such as a social status in virtual space such as a game. Moreover, video information processing apparatus ex 101  gives a particularly high point to the user who transmits the captured video of a valuable visual field (and/or viewpoint) such as a frequently requested video. 
     Variation 5 
     Video information processing apparatus ex 101  may transmit additional information to the user camera based on an analysis result made by analyzer ex 112 . In this case, the user camera superimposes the additional information of the captured video, and displays the superimposed video on a screen. The additional information is, for example, information of a player such as a player name or height when a game in a stadium is captured, and the player name or a photograph of the player&#39;s face is displayed in association with each player in the video. Note that video information processing apparatus ex 101  may extract the additional information by search via the Internet based on part or all areas of the video data. Moreover, camera ex 102  may receive such additional information by the near field communication including Bluetooth (registered trademark) or by visible light communication from illumination of the stadium or the like, and may map the received additional information to the video data. Moreover, camera ex 102  may perform this mapping based on a certain rule such as a table that is kept in the storage connected to camera ex 102  wirelessly or with wires and that indicates correspondence between the information obtained by the visible light communication technology and the additional information. Camera ex 102  may perform this mapping by using a result of a most probable combination by Internet search. 
     Moreover, in the monitoring system, a highly accurate monitoring system can be implemented, for example, by superimposition of information of a person on a blacklist on the user camera carried by a guard in the facility. 
     Variation 6 
     Analyzer ex 112  may determine which area in the facility or in the stadium the user camera is capturing, by matching the free viewpoint video with the video captured by the user camera. Note that the method for determining the capturing area is not limited thereto, but various methods for determining the capturing area described in each of the above-described embodiments or other methods for determining the capturing area may be used. 
     Video information processing apparatus ex 101  transmits a past video to the user camera based on the analysis result made by analyzer ex 112 . The user camera superimposes the past video on the captured video, or replaces the captured video with the past video, and displays the video on a screen. 
     For example, a highlight scene of a first half is displayed as a past video during halftime. This enables the user to enjoy the highlight scene of the first half during halftime as a video captured in a direction in which the user is watching. Note that the past video is not limited to the highlight scene of the first half, but may be the highlight scene of the past game held in the stadium. Moreover, timing at which video information processing apparatus ex 101  distributes the past video is not limited to timing of distributing during halftime, but may be, for example, timing of distributing after the game end or during the game. In particular, in the case of distributing during halftime, video information processing apparatus ex 101  may distribute a scene which the user has missed and which is considered to be important, based on the analysis result made by analyzer ex 112 . Moreover, video information processing apparatus ex 101  may distribute the past video only when there is a user request, or may distribute a message of distribution permission prior to distribution of the past video. 
     Variation 7 
     Video information processing apparatus ex 101  may transmit advertisement information to the user camera based on the analysis result made by analyzer ex 112 . The user camera superimposes the advertisement information of the captured video, and displays the superimposed video on a screen. 
     The advertisement information may be distributed, for example, immediately before distribution of the past video during halftime or after the game end as described in variation 6. This enables a distribution company to obtain advertising rates from an advertiser and to provide the user with video distribution services at a low cost or with no charge. Moreover, video information processing apparatus ex 101  may distribute a message of advertisement distribution permission immediately before distribution of the advertisement information, or may provide services with no charge only when the user views the advertisement, or may provide services at a lower cost than a cost in the case where the user does not view the advertisement. 
     Moreover, when the user clicks “Order now” or the like in response to the advertisement, a staff member who knows the position of the user based on the system or some positional information, or an automatic delivery system in the facility delivers an ordered drink to a seat of the user. Payment may be made by hand delivery to the staff member, or may be made based on credit card information set in an app of a mobile terminal or the like in advance. Moreover, the advertisement may include a link to an e-commerce site, and on-line shopping such as ordinary home delivery may be possible. 
     Variation 8 
     Video reception apparatus ex 103  may be one of the cameras ex 102  (user cameras). In this case, analyzer ex 112  matches the free viewpoint video with the video captured by the user camera, and accordingly analyzer ex 112  determines which area in the facility or in the stadium the user camera is capturing. Note that the method for determining the capturing area is not limited thereto. 
     For example, when the user performs a swipe operation in a direction of an arrow displayed on a screen, the user camera generates viewpoint information that indicates movement of the viewpoint in the direction. Video information processing apparatus ex 101  reads from storage ex 111  the video data that captures an area that is moved according to the viewpoint information from the area captured by the user camera determined by analyzer ex 112 , and video information processing apparatus ex 101  starts transmission of the read video data to the user camera. Then, the user camera displays the video distributed from video information processing apparatus ex 101 , instead of the captured video. 
     This enables the user in the facility or in the stadium to view the video captured from a favorite viewpoint with such a simple operation as screen swipe. For example, a spectator who is watching a game on a third base side of a baseball stadium can view the video captured from the viewpoint on a first base side. Moreover, the monitoring system enables a guard in the facility to view, for example, the video from the viewpoint from which the guard wants to check or the video to be watched closely as an interruption from a center, while changing the viewpoint adaptively, with such a simple operation as screen swipe. For this reason, a highly accurate monitoring system can be implemented. 
     Moreover, distribution of the video to the user in the facility or in the stadium is effective, for example, even when an obstacle exists between the user camera and an object to be captured, and there is an invisible area. In this case, the user camera may switch the video of some area of the areas captured by the user camera that includes the obstacle, from the captured video to a video distributed from video information processing apparatus ex 101 , and may display the distributed video, or the user camera may switch the entire screen from the captured video to the distributed video, and may display the distributed video. Moreover, the user camera may combine the captured video with the distributed video to display the video that seems to penetrate the obstacle such that the object to be viewed is visible. Even when the object to be captured is invisible from the position of the user due to influence of the obstacle, this configuration can reduce the influence of the obstacle because the user can view the video distributed from video information processing apparatus ex 101 . 
     Moreover, when the distributed video is displayed as the video of the area invisible due to the obstacle, display switching control different from display switching control depending on input processing made by the user such as the screen swipe described above may be performed. For example, when it is determined that the capturing area includes the obstacle, based on information of movement and capturing direction of the user camera, and based on positional information of the obstacle obtained in advance, display switching from the captured video to the distributed video may be performed automatically. Moreover, when it is determined from analysis of the captured video data that the obstacle which is not the object to be captured is being captured, display switching from the captured video to the distributed video may be performed automatically. Moreover, when area of the obstacle in the captured video (for example, a number of pixels) exceeds a predetermined threshold, or when a ratio of the area of the obstacle to area of the object to be captured exceeds a predetermined proportion, display switching from the captured video to the distributed video may be performed automatically. 
     Note that the display switching from the captured video to the distributed video, and display switching from the distributed video to the captured video may performed in response to the input processing made by the user. 
     Variation 9 
     A speed at which the video data is transmitted to video information processing apparatus ex 101  may be instructed based on importance of the video data captured by each camera ex 102 . 
     In this case, analyzer ex 112  determines importance of video data stored in storage ex 111  or importance of camera ex 102  that captures the video data. The determination of the importance here is made based on, for example, a number of persons or a number of moving objects in the video, the information such as image quality of the video data, or combination thereof. 
     Moreover, the determination of the importance of the video data may be made based on the position of camera ex 102  that captures the video data or the area captured in the video data. For example, when a plurality of other capturing cameras ex 102  exists near camera ex 102  concerned, the importance of the video data captured by camera ex 102  concerned is set low. Moreover, when the position of camera ex 102  concerned is distant from the positions of other cameras ex 102 , but there exists a plurality of other cameras ex 102  that captures an identical area, the importance of the video data captured by camera ex 102  concerned is set low. Moreover, the determination of the importance of the video data may be made based on frequency of requests in video distribution services. Note that the method for determining the importance is limited to neither the above-described methods nor combination thereof, but may be a method according to the configuration or purpose of the monitoring system or video distribution system. 
     Moreover, the determination of the importance may not be made based on the captured video data. For example, the importance of camera ex 102  that transmits the video data to terminals other than video information processing apparatus ex 101  may be set high. Conversely, the importance of camera ex 102  that transmits the video data to terminals other than video information processing apparatus ex 101  may be set low. Accordingly, for example, when a plurality of services that needs transmission of video data uses a common communication band, a degree of freedom of controlling the communication band according to a purpose or characteristics of each service increases. This prevents quality of each service from degrading because necessary video data cannot be obtained. 
     Moreover, analyzer ex 112  may determine the importance of the video data by using the free viewpoint video and the captured video of camera ex 102 . 
     Video information processing apparatus ex 101  transmits a communication speed instruction signal to camera ex 102  based on a determination result of the importance made by analyzer ex 112 . Video information processing apparatus ex 101  gives instruction of high speed communication to, for example, camera ex 102  that is capturing a video with high importance. Moreover, in addition to speed control, regarding important information, video information processing apparatus ex 101  may transmit a signal that instructs a scheme for sending the important information twice or more in order to reduce disadvantages owing to loss. This enables efficient communication in the entire facility or in the entire stadium. Note that communication between camera ex 102  and video information processing apparatus ex 101  may be wired communication, or may be wireless communication. Moreover, video information processing apparatus ex 101  may control only any one of the wired communication and wireless communication. 
     Camera ex 102  transmits the captured video data to video information processing apparatus ex 101  at the communication speed according to the communication speed instruction signal. Note that when retransmission fails predetermined number of times, camera ex 102  may stop retransmission of the captured video data and start transmission of next captured video data. This enables efficient communication in the entire facility or in the entire stadium and high-speed processing in analyzer ex 112  can be implemented. 
     Moreover, when the communication speed allocated to each camera ex 102  fails to have a bandwidth sufficient for transmitting the captured video data, camera ex 102  may convert the captured video data into video data with a bit rate that enables transmission at the allocated communication speed, and transmit the converted video data, or may stop transmission of the video data. 
     Moreover, as described above, when the video data is used for preventing occurrence of a blind spot, only some area of the capturing areas in the captured video data is likely to be needed for filling the blind spot. In this case, camera ex 102  may generate extracted video data by extracting only at least the area needed for preventing occurrence of the blind spot from the video data, and transmit the generated extracted video data to video information processing apparatus ex 101 . This configuration can realize suppression of occurrence of the blind spot at a narrower communication bandwidth. 
     Moreover, for example, when superimposed display or video distribution of the additional information is performed, camera ex 102  needs to transmit the positional information and information of the capturing direction of camera ex 102  to video information processing apparatus ex 101 . In this case, camera ex 102  to which only the bandwidth insufficient for transmitting the video data is allocated may transmit only the positional information and information of the capturing direction detected by camera ex 102 . Moreover, when video information processing apparatus ex 101  estimates the positional information and information of the capturing direction of camera ex 102 , camera ex 102  may convert the captured video data into video data with resolution necessary for estimation of the positional information and the information of the capturing direction, and transmit the converted video data to video information processing apparatus ex 101 . This configuration can also provide superimposed display or video distribution services of the additional information to camera ex 102  to which only the narrow communication bandwidth is allocated. Moreover, since video information processing apparatus ex 101  can acquire information of the capturing area from more cameras ex 102 , video information processing apparatus ex 101  is effective, for example, for using information of the capturing area for a purpose of detecting an area that attracts attention, or the like. 
     Note that the above-described switching of transmission processing of the video data according to the allocated communication bandwidth may be performed by camera ex 102  based on the notified communication bandwidth, or video information processing apparatus ex 101  may determine the operation of each camera ex 102  and notify each camera ex 102  of a control signal that indicates the determined operation. This enables appropriate sharing of tasks of processing according to an amount of calculation necessary for determination of switching of the operations, throughput of camera ex 102 , required communication bandwidth, and the like. 
     Variation 10 
     Analyzer ex 112  may determine the importance of the video data based on the visual field information (and/or viewpoint information) transmitted from video reception apparatus ex 103 . For example, analyzer ex 112  sets high importance of the captured video data including a lot of areas indicated by the visual field information (and/or viewpoint information). Moreover, analyzer ex 112  may determine the importance of the video data in consideration of the number of persons or the number of moving objects in the video. Note that the method for determining the importance is not limited thereto. 
     Note that a communication control method described in the present embodiment does not necessarily need to be used in a system that reconstructs the three-dimensional shape from the plurality of pieces of video data. For example, when video data is transmitted by wired communication and/or wireless communication selectively or at a different transmission speed in an environment where there exists a plurality of cameras ex 102 , the communication control method described in the present embodiment is effective. 
     Variation 11 
     In the video distribution system, video information processing apparatus ex 101  may transmit an outline video that indicates an entire capturing scene to video reception apparatus ex 103 . 
     Specifically, when video information processing apparatus ex 101  has received a distribution request transmitted from video reception apparatus ex 103 , video information processing apparatus ex 101  reads the outline video of an inside of the entire facility or an inside of the entire stadium from storage ex 111 , and transmits the external appearance video to video reception apparatus ex 103 . This outline video may have a long update interval (may have a low frame rate), and may have low image quality. A viewer touches a portion to watch in the outline video displayed on a screen of video reception apparatus ex 103 . Accordingly, video reception apparatus ex 103  transmits the visual field information (and/or viewpoint information) corresponding to the touched portion to video information processing apparatus ex 101 . 
     Video information processing apparatus ex 101  reads the video data corresponding to the visual field information (and/or viewpoint information) from storage ex 111 , and transmits the video data to video reception apparatus ex 103 . 
     Moreover, analyzer ex 112  generates the free viewpoint video by preferentially restoring the three-dimensional shape (three-dimensional reconfiguration) of the area indicated by the visual field information (and/or viewpoint information). Analyzer ex 112  restores the three-dimensional shape of an inside of the entire facility or an inside of the entire stadium with accuracy in the extent of indicating the outline. Accordingly, video information processing apparatus ex 101  can efficiently restore the three-dimensional shape. As a result, a high frame rate and high image quality of the free viewpoint video of the area the viewer wants to watch can be implemented. 
     Variation 12 
     Note that video information processing apparatus ex 101  may store in advance as a previous video, for example, three-dimensional shape restored data of the facility or stadium generated in advance from design drawings or the like. Note that the previous video is not limited thereto, but may be virtual space data in which unevenness of space obtained from a depth sensor and a picture derived from a past image or video data or an image or video data at a time of calibration are mapped for each object. 
     For example, when soccer is played in a stadium, analyzer ex 112  may restore the three-dimensional shapes of only players and a ball, and generate the free viewpoint video by combining the obtained restored data and the previous video. Alternatively, analyzer ex 112  may preferentially restore the three-dimensional shapes of players and a ball. Accordingly, video information processing apparatus ex 101  can restore the three-dimensional shape efficiently. As a result, a high frame rate and high image quality of the free viewpoint video regarding players and a ball to which viewers pay attention can be implemented. Moreover, in the monitoring system, analyzer ex 112  may preferentially restore the three-dimensional shapes of only persons and moving objects. 
     Variation 13 
     Time of each apparatus may be calibrated when capturing starts, based on information such as reference time of the server. Analyzer ex 112  restores the three-dimensional shape by using the plurality of pieces of video data captured at time within a preset time range among the plurality of pieces of captured video data captured by the plurality of cameras ex 102  in accordance with accuracy of time settings. This detection of time uses, for example, time when the captured video data is stored in storage ex 111 . Note that the method for detecting time is not limited thereto. Accordingly, since video information processing apparatus ex 101  can restore the three-dimensional shape efficiently, a high frame rate and high image quality of the free viewpoint video can be implemented. 
     Alternatively, analyzer ex 112  may restore the three-dimensional shape by using only high-quality data, or by preferentially using high-quality data among the plurality of pieces of video data stored in storage ex 111 . 
     Variation 14 
     Analyzer ex 112  may restore the three-dimensional shape by using camera attribute information. For example, analyzer ex 112  may generate the three-dimensional shape video by a method such as a volume intersection technique or a multi-view stereo method by using camera attribute information. In this case, camera ex 102  transmits the captured video data and the camera attribute information to video information processing apparatus ex 101 . Examples of the camera attribute information include a capturing position, a capturing angle, capturing time, and zoom magnification. 
     Accordingly, since video information processing apparatus ex 101  can restore the three-dimensional shape efficiently, a high frame rate and high image quality of the free viewpoint video can be implemented. 
     Specifically, camera ex 102  defines three-dimensional coordinates in the facility or in the stadium, and transmits to video information processing apparatus ex 101  information as camera attribute information that indicates an angle, zoom magnification, and time of capturing of certain coordinates by camera ex 102 , together with the video. Moreover, when camera ex 102  is activated, a clock on a communication network in the facility or in the stadium is synchronized with a clock in the camera, and time information is generated. 
     Moreover, the positional and angle information of camera ex 102  is acquired by pointing camera ex 102  at a specific point in the facility or in the stadium when camera ex 102  is activated or at any timing.  FIG. 38  is a diagram illustrating an example of notification displayed on a screen of camera ex 102  when camera ex 102  is activated. When the user matches “+” displayed in a center of the screen with “+” which is in a center of a soccer ball in advertisement in north of the stadium in response to this notification and touches the display of camera ex 102 , camera ex 102  acquires vector information from camera ex 102  to the advertisement, and identifies reference of the camera position and angle. Subsequently, camera coordinates and an angle at each time are identified from motion information of camera ex 102 . Of course, the display is not limited thereto, and display that instructs coordinates, an angle, or a movement speed of the capturing area during a capturing period by using an arrow or the like may be used. 
     The coordinates of camera ex 102  may be identified by using a radio wave of the global positioning system (GPS), wireless fidelity (WiFi) (registered trademark), third generation (3G), long term evolution (LTE), and fifth generation (5G) (wireless LAN), or by using the near field communication such as beacon (Bluetooth (registered trademark), ultrasonic waves). Moreover, information about which base station in the facility or in the stadium has received the captured video data may be used. 
     Variation 15 
     The system may be provided as an application that operates on a mobile terminal such as a smartphone. 
     Accounts of various social networking services (SNS) or the like may be used for login to the system. Note that an account dedicated to an app or a guest account that has limited functions may be used. Favorite videos, favorite accounts or the like can be evaluated by using the accounts in such a manner. Moreover, the bandwidth is preferentially allocated to, for example, video data similar to video data that is being captured or viewed, or to video data of the viewpoint similar to the viewpoint of video data that is being captured or viewed, and this can increase resolution of these pieces of video data. Accordingly, the three-dimensional shape from these viewpoints can be restored with better accuracy. 
     Moreover, the user can preferentially watch the selected image over other users by selecting a favorite image video and by following the other party with the application, or the user can have connection by text chatting or the like on condition of approval of the other party. Thus, it is possible to generate a new community. 
     Thus, connection between the users in the community can activate capturing itself or sharing of captured images, and can prompt restoration of three-dimensional shapes with higher accuracy. 
     Moreover, according to settings of connection in the community, the user can edit images or videos captured by another person, or can perform collage of an image of another person and an image of the user to create a new image or video. This enables sharing of a new video work, such as sharing the new image or video with only persons in the community. Moreover, the video work can also be used for a game of augmented reality or the like by inserting a computer-graphics (CO character in this editing. 
     Moreover, since the system enables sequential output of three-dimensional model data, a 3D printer or the like that the facility has can output a three-dimensional object, based on the three-dimensional model data in a characteristic scene such as a goal scene. This also enables sale after the game of an object based on the scene during the game as a souvenir such as a key ring, or distribution after the game of such an object to participating users. Of course, this also enables printing of an image captured from the best viewpoint as an ordinary photograph. 
     Variation 16 
     A center connected to the system can use the above-described system to manage a rough state of the overall region, for example, from a video of a vehicle-mounted camera of the police and a wearable camera of a police officer, and the like. 
     During ordinary patrol, still images are transmitted and received, for example, every several minutes. Moreover, the center identifies a region in which crime is highly likely to occur, based on a criminal map prepared based on a result of analysis using past criminal data or the like. Alternatively, the center keeps regional data related to a crime rate identified in this manner. In a region with the identified high-crime-rate, high frequency of transmission and reception of images may be set, or a change of images to moving images may be made. Moreover, when an incident occurs, moving images or three-dimensional reconfiguration data using SfM may be used. Moreover, the center or each terminal can compensate the image or virtual space by concurrently using information from other sensors such as a depth sensor and a thermal sensor, and accordingly the police officer can understand the situation with better accuracy. 
     Moreover, the center can use the three-dimensional reconfiguration data to feed back information of the object to the plurality of terminals. This enables each individual person having a terminal to keep track of the object. 
     Moreover, in these years, capturing has been performed from the air by an apparatus that can fly such as a quadcopter and a drone, for purposes of investigation of buildings or environment, capturing with realism such as sports or the like. While blur of images is likely to become a problem in capturing by such an autonomous moving apparatus, SfM can create three dimensions while compensating the blur with a position and an inclination. This can realize improvement in image quality and improvement in restoration accuracy of space. 
     Moreover, installation of a vehicle-mounted camera that captures an outside of a car is mandatory in some countries. In such a vehicle-mounted camera, weather and a road surface state in a direction of a destination, traffic congestion level and the like can be understood with better accuracy by using three-dimensional data modeled from a plurality of images. 
     Variation 17 
     The above-described system may also be applied to a system that performs distance measurement or modeling of a building or equipment by using a plurality of cameras, for example. 
     Here, for example, in a case of capturing an image of a building from above using one drone, and performing distance measurement or modeling of the building, there is an issue in that an image of a mobile object may be captured by the camera during distance measurement, thereby reducing the accuracy of distance measurement. There is also an issue in that distance measurement and modeling cannot be performed with respect to a mobile object. 
     Meanwhile, by using a plurality of cameras (fixed cameras, smartphones, wearable cameras, drones, etc.) as described above, distance measurement and modeling of a building may be performed with stable accuracy regardless of presence or absence of a mobile object. Also, distance measurement and modeling may be performed with respect to a mobile object. 
     Specifically, for example, at a construction site, a camera is attached to a helmet or the like of a worker. This allows distance measurement of the building to be performed in parallel to the work of the worker. Also, efficiency of work may be increased, and mistakes may be prevented. Furthermore, modeling of the building may be performed by using a video captured by the camera worn by the worker. Moreover, a manager at a remote location may check the progress by looking at a modeled building. 
     Moreover, this system may be used for inspection of equipment that cannot be stopped, such as a machine at a factory or a power station. Also, this system can be used to inspect opening/closing of a bridge or a dam, or to inspect an abnormality in the operation of a ride in an amusement park, for example. 
     Moreover, by monitoring the level of traffic jam or the amount of traffic on a road by this system, a map indicating the level of traffic jam or the amount of traffic on the road in each time zone may be created. 
     Embodiment 6 
     The processing described in each of the above-described embodiments can be carried out easily in a standalone computer system by recording a program for implementing the configuration of the image processing method described in each embodiment on a storage medium. The storage medium may be any type of medium capable of storing the program, such as a magnetic disk, an optical disc, a magneto-optical disk, an integrated circuit (IC) card, and a semiconductor memory. 
     Here, application examples of the image processing method described in each of the embodiments and the systems using the application examples will be further described. The systems include an apparatus that uses the image processing method. A change in other configurations of the systems can be made appropriately in accordance with the circumstances. 
       FIG. 39  is a diagram illustrating an overall configuration of content providing system ex 200  that implements content distribution services. An area in which communication services are provided is divided with a desired size. Base stations ex 206 , ex 207 , ex 208 , ex 209 , and ex 210  which are fixed wireless stations are installed in respective cells. 
     In content providing system ex 200 , various devices such as computer ex 211 , personal digital assistant (PDA) ex 212 , camera ex 213 , smartphone ex 214 , and game machine ex 215  are connected to Internet ex 201  via Internet service provider ex 202 , wide area network (WAN) ex 204 , and base stations ex 206  to ex 210 . 
     However, the configuration of content providing system ex 200  is not limited to the configuration illustrated in  FIG. 39 , and any elements may be combined and connected. Moreover, each device may be connected directly to telephone lines, cable TV, or WAN ex 204  such as optical communication, instead of via base stations ex 206  to ex 210  which are fixed wireless stations. Alternatively, each device may be interconnected directly via near field communication or the like. 
     Camera ex 213  is a device capable of capturing moving images, such as a digital camcorder. Camera ex 216  is a device capable of capturing still images and moving images, such as a digital camera. Moreover, smartphone ex 214  is, for example, a smartphone conforming to a global system for mobile communication (GSM) (registered trademark) scheme, a code division multiple access (CDMA) scheme, a wideband-code division multiple access (W-CDMA) scheme, an long term evolution (LTE) scheme, an high speed packet access (HSPA) scheme, or a communication scheme using high-frequency bands, or a personal handyphone system (PHS), and smartphone ex 214  may be any of them. 
     In content providing system ex 200 , camera ex 213  or the like is connected to streaming server ex 203  via base station ex 209  and WAN ex 204 . Accordingly, live streaming or the like becomes possible. In the live streaming, coding processing is performed on content (for example, a video of a music event) captured by the user using camera ex 213  and the resulting content is transmitted to streaming server ex 203 . Meanwhile, streaming server ex 203  perform stream distribution of content data transmitted to a client that has made a request. Examples of the client include computer ex 211 , PDA ex 212 , camera ex 213 , smartphone ex 214 , and game machine ex 215  capable of decoding the data that has undergone the coding processing. Each device that has received the distributed data performs decoding processing on the received data to reproduce the data. 
     Note that the coding processing of the captured video may be performed by camera ex 213 , or may be performed by streaming server ex 203  that performs data transmission processing, or camera ex 213  and streaming server ex 203  may share tasks of the coding processing of the captured video with each other. Similarly, the decoding processing of the distributed data may be performed by the client, or may be performed by streaming server ex 203 , or the client and streaming server ex 203  may share tasks of the decoding processing of the captured video with each other. Moreover, in addition to still and/or moving image data captured by camera ex 213 , still and/or moving image data captured by camera ex 216  may be transmitted to streaming server ex 203  via computer ex 211 . In this case, the coding processing may be performed by any of camera ex 216 , computer ex 211 , and streaming server ex 203 , or camera ex 216 , computer ex 211 , and streaming server ex 203  may share tasks of the coding processing with each other. Further, regarding display of the decoded image, a plurality of devices connected to the system may cooperate to display an identical image, or a device having a large display unit may display the entire image and a device such as smartphone ex 214  may enlarge and display some area of the image. 
     Moreover, the coding processing and the decoding processing are performed in general by LSI ex 500  in computer ex 211  or each device. LSI ex 500  may include a single chip or a plurality of chips. Note that software for coding/decoding a moving image may be recorded on any recording medium (such as a CD-ROM, a flexible disk, and a hard disk) that is readable by computer ex 211  or the like, and the coding processing and the decoding processing may be performed using the software. Further, in the case where smartphone ex 214  is equipped with a camera, moving image data acquired by the camera may be transmitted. This moving image data is data that has been coded by LSI ex 500  in smartphone ex 214 . 
     Moreover, streaming server ex 203  may be a plurality of servers or a plurality of computers that processes, records, and distributes data. 
     In the above-described manner, content providing system ex 200  enables the client to receive and reproduce coded data. Thus, content providing system ex 200  enables the client to receive, decode, and reproduce in real time information transmitted by a user, and enables even a user having no special right or equipment to implement personal broadcasting. 
     Note that in addition to the example of content providing system ex 200 , each of the above-described embodiments may also be applied to digital broadcasting system ex 300 , as illustrated in  FIG. 40 . Specifically, broadcasting station ex 301  transmits multiplexed data obtained by multiplexing video data with music data or the like via a radio wave to communication or satellite ex 302 . This video data is data coded by the moving image coding method described in each of the above-described embodiments. Broadcasting satellite ex 302  that has received this data transmits a broadcasting radio wave, and home antenna ex 304  capable of receiving satellite broadcasting receives this radio wave. An apparatus such as television (receiver) ex 400  or set top box (STB) ex 317  decodes and reproduces the received multiplexed data. 
     Moreover, the moving image decoding apparatus or the moving image coding apparatus described in each of the above-described embodiments can be implemented in reader/recorder ex 318  that reads and decodes the multiplexed data recorded on recording medium ex 315  such as a digital versatile disc (DVD) and a blu-ray disc (BD) or memory ex 316  such as an secured digital (SD), or that codes a video signal and further multiplexes the video signal with a music signal depending on circumstances, and writes the resulting signal on recording medium ex 315  or memory ex 316 . In this case, monitor ex 319  may display the reproduced video signal, and another apparatus or system can reproduce the video signal by using recording medium ex 315  or memory ex 316  having the multiplexed data recorded thereon. Moreover, the moving image decoding apparatus may be implemented in set top box ex 317  connected to cable ex 303  for a community antenna television system (CATV) or antenna ex 304  for satellite/terrestrial broadcasting, and monitor ex 319  of the television may display the video signal. At this time, the moving image decoding apparatus may be incorporated into the television instead of the set top box. 
       FIG. 41  is a diagram illustrating smartphone ex 214 . Moreover,  FIG. 42  is a diagram illustrating a configuration example of smartphone ex 214 . Smartphone ex 214  includes antenna ex 450  that transmits and receives a radio wave to and from base station ex 210 , camera ex 465  capable of capturing a video and a still image, and display unit ex 458  such as a liquid crystal display that displays the video captured by camera ex 465  and data obtained by decoding a video or the like received on antenna ex 450 . Smartphone ex 214  further includes operation unit ex 466  which is a touch panel or the like, audio outputter ex 457  such as a speaker for outputting audio, audio inputter ex 456  such as a microphone for inputting audio, memory unit ex 467  capable of storing coded data or decoded data of a captured video, a captured still image, recorded audio, a received video, a received still image, or a received email, memory ex 316  illustrated in  FIG. 40 , or slot ex 464  which is an interface to SIM ex 468  for identifying a user and for authentication of access to various types of data including a network. 
     In smartphone ex 214 , power supply circuit ex 461 , operation input controller ex 462 , video signal processor ex 455 , camera interface ex 463 , liquid crystal display (LCD) controller ex 459 , modulator/demodulator ex 452 , multiplexer/demultiplexer ex 453 , audio signal processor ex 454 , slot ex 464 , and memory unit ex 467  are connected via bus ex 470  to main controller ex 460  that comprehensively controls display unit ex 458 , operation unit ex 466  and the like, respectively. 
     When an on-hook/power key is turned on by a user operation, power supply circuit ex 461  supplies electric power to each unit from a battery pack, and accordingly activates smartphone ex 214  into an operable state. 
     In smartphone ex 214  based on control of main controller ex 460  that includes a CPU, a ROM, a RAM and the like, audio signal processor ex 454  converts an audio signal recorded with audio inputter ex 456  in a voice call mode into a digital audio signal, and modulator/demodulator ex 452  performs spread spectrum processing on this digital audio signal, and transmitter/receiver ex 451  performs digital-to-analog conversion processing and frequency conversion processing on this signal and then transmits the resulting signal via antenna ex 450 . Moreover, smartphone ex 214 , amplifies reception data received via antenna ex 450  in the voice call mode and performs frequency conversion processing and analog-to-digital conversion processing on the data, and modulator/demodulator ex 452  performs spread spectrum processing on the resulting signal, and audio signal processor ex 454  converts the resulting signal into an analog audio signal, and then audio outputter ex 457  outputs the analog audio signal. 
     In the case where an email is transmitted in a data communication mode, text data of the email input by operation of operation unit ex 466  or the like of a body is sent to main controller ex 460  via operation input controller ex 462 . In main controller ex 460  modulator/demodulator ex 452  performs spread spectrum processing on the text data, and transmitter/receiver ex 451  performs digital-to-analog conversion processing and frequency conversion processing on the text data and then transmits the resulting text data to base station ex 210  via antenna ex 450 . In the case of receiving an email, substantially the opposite processing is performed on the received data, and the resulting data is output to display unit ex 458 . 
     In the case where a video, a still image, or a combination of a video and audio are transmitted in the data communication mode, video signal processor ex 455  compresses and codes a video signal supplied from camera ex 465  by the moving image coding method described in each of the above embodiments, and sends the coded video data to multiplexer/demultiplexer ex 453 . Moreover, audio signal processor ex 454  codes an audio signal recorded with audio inputter ex 456  while the video, the still image, or the like is being captured by camera ex 465 , and sends the coded audio data to multiplexer/demultiplexer ex 453 . 
     Multiplexer/demultiplexer ex 453  multiplexes the coded video data supplied from video signal processor ex 455  and the coded audio data supplied from audio signal processor ex 454  by a predetermined scheme. Modulator/demodulator (modulation/demodulation circuit) ex 452  performs spread spectrum processing on the resulting multiplexed data. Transmitter/receiver ex 451  performs digital-to-analog conversion processing and frequency conversion processing on the multiplexed data, and then transmits the resulting data via antenna ex 450 . 
     In the case of receiving data of a moving image file linked to a website or the like in the data communication mode, or in the case of receiving an email having a video or audio attached thereto, multiplexer/demultiplexer ex 453  demultiplexes multiplexed data into a bitstream of video data and a bitstream of audio data in order to decode the multiplexed data received via antenna ex 450 . Multiplexer/demultiplexer ex 453  supplies the coded video data to video signal processor ex 455  and the coded audio data to audio signal processor ex 454  via synchronization bus ex 470 . Video signal processor ex 455  decodes the video signal by a moving image decoding method corresponding to the moving image coding method described in each of the above embodiments. Display unit ex 458  displays via LCD controller ex 459  a video or still image in the moving image file linked to the website. Moreover, audio signal processor ex 454  decodes the audio signal, and audio outputter ex 457  outputs audio. 
     Moreover, like television ex 400 , three implementation forms of a terminal such as smartphone ex 214 , that is, a transmission/reception terminal including both an encoder and a decoder, a transmission terminal including only an encoder, and a reception terminal including only a decoder, are conceivable. Further, digital broadcasting system ex 300  in which multiplexed data obtained by multiplexing video data with music data or the like is received and transmitted is described above; however, the multiplexed data may be data obtained by multiplexing text data or the like related to the video other than audio data, or may be video data as is instead of the multiplexed data. Moreover, the present disclosure is not limited to the above-described exemplary embodiments, and various variations or modifications can be made without departing from the scope 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 information processing device.