Three-dimensional data encoding method, three-dimensional data decoding method, three-dimensional data encoding device, and three-dimensional data decoding device

Abstract

The three-dimensional data encoding method calculates a plurality of coefficient values (S7091) based on a plurality of attribute information of a plurality of three-dimensional points included in point cloud data, generates a plurality of quantization values by quantizing each of the plurality of coefficient values (S7092), generates a bitstream including the plurality of quantization values (S7093), the plurality of coefficient values belong to a certain layer of a plurality of layers, and in quantization (S7092), each of the plurality of coefficient values is quantized using a quantization parameter for the layer to which the coefficient value belongs. The bitstream includes first information indicating a reference quantization parameter and a plurality of second information for calculating a plurality of quantization parameters for a plurality of layers based on the reference quantization parameter.

Description

Technical Field

[0001] The present disclosure relates to a three-dimensional data encoding method, a three-dimensional data decoding method, a three-dimensional data encoding apparatus, and a three-dimensional data decoding apparatus. Background Art

[0002] In large fields such as computer vision, map information, monitoring, infrastructure inspection, or video distribution for autonomous operation of automobiles or robots, devices or services that make flexible use of three-dimensional data will be popularized in the future. The three-dimensional data is obtained by various methods such as distance sensors such as rangefinders, stereo cameras, or combinations of multiple monocular cameras.

[0003] As a representation method of three-dimensional data, there is a representation method called point cloud, which represents the shape of a three-dimensional structure by a point group in a three-dimensional space. The position and color of the point group are stored in the point cloud. Although it is expected that the point cloud will become the mainstream as a representation method of three-dimensional data, the data volume of the point group is very large. Therefore, in the storage or transmission of three-dimensional data, like two-dimensional moving images (as an example, MPEG-4 AVC or HEVC standardized by MPEG), data volume compression needs to be performed by encoding.

[0004] In addition, for the compression of point clouds, some are supported by publicly available libraries (PointCloud Library) that perform point cloud correlation processing.

[0005] In addition, there is a well-known technique of using three-dimensional map data to retrieve and display facilities around a vehicle (for example, refer to Patent Document 1).

[0006] Prior Art Documents

[0007] Patent Documents

[0008] Patent Document 1 International Publication No. 2014 / 020663 Summary of the Invention

[0009] Problems to be Solved by the Invention

[0010] In the encoding process of three-dimensional data, it is desired to perform encoding appropriately.

[0011] An object of the present disclosure is to provide a three-dimensional data encoding method, a three-dimensional data decoding method, a three-dimensional data encoding apparatus, or a three-dimensional data decoding apparatus that can perform encoding appropriately.

[0012] Means for Solving the Problems

[0013] A three-dimensional data encoding method according to one aspect of the present disclosure calculates a plurality of coefficient values based on a plurality of attribute information of a plurality of three-dimensional points included in point cloud data, generates a plurality of quantization values by quantizing each of the plurality of coefficient values, generates a bitstream including the plurality of quantization values, the plurality of coefficient values belong to a certain layer among a plurality of layers, and in the quantization, each of the plurality of coefficient values is quantized using a quantization parameter for the layer to which the coefficient value belongs. The bitstream includes first information indicating a reference quantization parameter and a plurality of second information for calculating a plurality of quantization parameters for the plurality of layers based on the reference quantization parameter.

[0014] A three-dimensional data decoding method according to one aspect of the present disclosure uses (i) first information indicating a reference quantization parameter and (ii) a plurality of second information for calculating a plurality of quantization parameters for a plurality of layers based on the reference quantization parameter included in a bitstream to calculate the quantization parameters for the plurality of layers, and generates a plurality of coefficient values by inverse quantizing each of the plurality of quantization values included in the bitstream using the quantization parameter for the layer to which the quantization value belongs among the calculated quantization parameters for the plurality of layers. Based on the plurality of coefficient values, a plurality of attribute information of a plurality of three-dimensional points included in point cloud data is calculated.

[0015] Advantageous Effects of the Invention

[0016] The present disclosure can provide a three-dimensional data encoding method, a three-dimensional data decoding method, a three-dimensional data encoding device, or a three-dimensional data decoding device that can perform encoding appropriately. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 Shows the configuration of encoded three-dimensional data of Embodiment 1.

[0018] Figure 2 Shows an example of the prediction structure between SPCs belonging to the lowest layer of the GOS of Embodiment 1.

[0019] Figure 3 Shows an example of the inter-layer prediction structure of Embodiment 1.

[0020] Figure 4 Shows an example of the encoding order of the GOS of Embodiment 1.

[0021] Figure 5 Shows an example of the encoding order of the GOS of Embodiment 1.

[0022] Figure 6 Is a block diagram of the three-dimensional data encoding device of Embodiment 1.

[0023] Figure 7 Is a flowchart of the encoding process of Embodiment 1.

[0024] Figure 8 It is a block diagram of the three-dimensional data decoding device according to Embodiment 1.

[0025] Fig. 9 It is a flowchart of the decoding process according to Embodiment 1.

[0026] Fig.10 It shows an example of the meta information according to Embodiment 1.

[0027] Fig.11 It shows a configuration example of the SWLD according to Embodiment 2.

[0028] Fig.12 It shows an operation example of the server and the client according to Embodiment 2.

[0029] Fig.13 It shows an operation example of the server and the client according to Embodiment 2.

[0030] Fig.14 It shows an operation example of the server and the client according to Embodiment 2.

[0031] Fig.15 It shows an operation example of the server and the client according to Embodiment 2.

[0032] Fig.16 It is a block diagram of the three-dimensional data encoding device according to Embodiment 2.

[0033] Fig.17 It is a flowchart of the encoding process according to Embodiment 2.

[0034] Fig.18 It is a block diagram of the three-dimensional data decoding device according to Embodiment 2.

[0035] Fig.19 It is a flowchart of the decoding process according to Embodiment 2.

[0036] Fig. 20 It shows a configuration example of the WLD according to Embodiment 2.

[0037] Fig.21 It shows an example of the octree structure of the WLD according to Embodiment 2.

[0038] Fig. 22 It shows a configuration example of the SWLD according to Embodiment 2.

[0039] Fig.23 It shows an example of the octree structure of the SWLD according to Embodiment 2.

[0040] Fig.24 It is a block diagram of the three-dimensional data production device according to Embodiment 3.

[0041] Fig.25 It is a block diagram of the three-dimensional data transmission device of Embodiment 3.

[0042] Fig.26 It is a block diagram of the three-dimensional information processing device of Embodiment 4.

[0043] Fig. 27 It is a block diagram of the three-dimensional data production device of Embodiment 5.

[0044] Fig.28 Shows the configuration of the system of Embodiment 6.

[0045] Fig.29 It is a block diagram of the client device of Embodiment 6.

[0046] Fig.30 It is a block diagram of the server of Embodiment 6.

[0047] Fig.31 It is a flowchart of the three-dimensional data production process performed by the client device of Embodiment 6.

[0048] Fig.32 It is a flowchart of the sensor information transmission process performed by the client device of Embodiment 6.

[0049] Fig.33 It is a flowchart of the three-dimensional data production process performed by the server of Embodiment 6.

[0050] Fig.34 It is a flowchart of the three-dimensional map transmission process performed by the server of Embodiment 6.

[0051] Fig.35 Shows the configuration of a modified example of the system of Embodiment 6.

[0052] Fig.36 Shows the configuration of the server and client device of Embodiment 6.

[0053] Fig.37 It is a block diagram of the three-dimensional data encoding device of Embodiment 7.

[0054] Fig.38 Shows an example of the prediction residual of Embodiment 7.

[0055] Fig.39 Shows an example of the volume of Embodiment 7.

[0056] Fig.40 Shows an example of the octree representation of the volume of Embodiment 7.

[0057] Fig.41 Shows an example of the bit string of the volume of Embodiment 7.

[0058] Fig.42 Shows an example of the octree representation of the volume of Embodiment 7.

[0059] Fig.43 Shows an example of the volume of Embodiment 7.

[0060] Fig.44 Is a diagram for explaining the intra prediction processing of Embodiment 7.

[0061] Fig.45 Is a diagram for explaining the rotation and translation processing of Embodiment 7.

[0062] Fig.46 Shows an example of the syntax of the RT application flag and RT information of Embodiment 7.

[0063] Fig.47 Is a diagram for explaining the inter prediction processing of Embodiment 7.

[0064] Fig.48 Is a block diagram of the 3D data decoding device of Embodiment 7.

[0065] Fig.49 Is a flowchart of the 3D data encoding process performed by the 3D data encoding device of Embodiment 7.

[0066] Fig.50 Is a flowchart of the 3D data decoding process performed by the 3D data decoding device of Embodiment 7.

[0067] Fig.51 Is a diagram showing an example of 3D points of Embodiment 8.

[0068] Fig.52 Is a diagram showing a setting example of LoD of Embodiment 8.

[0069] Fig.53 Is a diagram showing an example of the threshold used in the setting of LoD of Embodiment 8.

[0070] Fig.54 Is a diagram showing an example of the attribute information used in the predicted value of Embodiment 8.

[0071] Fig.55 Is a diagram showing an example of the exponential Golomb code of Embodiment 8.

[0072] Fig.56 Is a diagram showing the processing for the exponential Golomb code of Embodiment 8.

[0073] Fig.57 Is a diagram showing a syntax example of the attribute header of Embodiment 8.

[0074] Fig.58 It is a diagram showing a syntactic example of attribute data of Embodiment 8.

[0075] Fig.59 It is a flowchart of three-dimensional data encoding processing of Embodiment 8.

[0076] Fig.60 It is a flowchart of attribute information encoding processing of Embodiment 8.

[0077] Fig.61 It is a diagram showing the processing for the exponential Golomb code of Embodiment 8.

[0078] Fig.62 It is a diagram showing an example of a backtracking table representing the relationship between the remaining code and its value of Embodiment 8.

[0079] Fig.63 It is a flowchart of three-dimensional data decoding processing of Embodiment 8.

[0080] Fig.64 It is a flowchart of attribute information decoding processing of Embodiment 8.

[0081] Fig.65 It is a block diagram of a three-dimensional data encoding device of Embodiment 8.

[0082] Fig.66 It is a block diagram of a three-dimensional data decoding device of Embodiment 8.

[0083] Fig.67 It is a flowchart of three-dimensional data encoding processing of Embodiment 8.

[0084] Fig.68 It is a flowchart of three-dimensional data decoding processing of Embodiment 8.

[0085] Fig.69 It is a diagram showing the first example of a table showing the predicted values calculated in each prediction mode of Embodiment 9.

[0086] Fig.70 It is a diagram showing an example of the attribute information used in the predicted values of Embodiment 9.

[0087] Fig.71 It is a diagram showing the second example of a table showing the predicted values calculated in each prediction mode of Embodiment 9.

[0088] Fig.72 It is a diagram showing the third example of a table showing the predicted values calculated in each prediction mode of Embodiment 9.

[0089] Fig.73It is a diagram showing the 4th example of a table presenting predicted values calculated in each prediction mode of Embodiment 9.

[0090] Fig.74 It is a diagram for explaining the encoding of attribute information using the RAHT of Embodiment 10.

[0091] Fig.75 It is a diagram showing an example of setting a quantization scale for each hierarchy in Embodiment 10.

[0092] Fig.76 It is a diagram showing examples of the first coding string and the second coding string in Embodiment 10.

[0093] Fig.77 It is a diagram showing an example of truncated unary coding in Embodiment 10.

[0094] Fig.78 It is a diagram for explaining the inverse Haar transform of Embodiment 10.

[0095] Fig.79 It is a diagram showing a syntactic example of attribute information in Embodiment 10.

[0096] Fig.80 It is a diagram showing examples of coding coefficients and ZeroCnt in Embodiment 10.

[0097] Fig.81 It is a flowchart of the three-dimensional data coding process of Embodiment 10.

[0098] Fig.82 It is a flowchart of the attribute information coding process of Embodiment 10.

[0099] Fig.83 It is a flowchart of the coding coefficient coding process of Embodiment 10.

[0100] Fig.84 It is a flowchart of the three-dimensional data decoding process of Embodiment 10.

[0101] Fig.85 It is a flowchart of the attribute information decoding process of Embodiment 10.

[0102] Fig.86 It is a flowchart of the coding coefficient decoding process of Embodiment 10.

[0103] Fig.87 It is a block diagram of the attribute information coding unit of Embodiment 10.

[0104] Fig.88 It is a block diagram of the attribute information decoding unit of Embodiment 10.

[0105] Fig.89This is a diagram for explaining the processing of the quantization unit and the inverse quantization unit in Embodiment 11.

[0106] Fig.90 This is a diagram for explaining the default value of the quantization value and the quantization delta (Δ) in Embodiment 11.

[0107] Fig.91 This is a block diagram showing the configuration of the first encoding unit included in the three-dimensional data encoding device in Embodiment 11.

[0108] Fig.92 This is a block diagram showing the configuration of the segmentation unit in Embodiment 11.

[0109] Fig.93 This is a block diagram showing the configuration of the position information encoding unit and the attribute information encoding unit in Embodiment 11.

[0110] Fig.94 This is a block diagram showing the configuration of the first decoding unit in Embodiment 11.

[0111] Fig.95 This is a block diagram showing the configuration of the position information decoding unit and the attribute information decoding unit in Embodiment 11.

[0112] Fig.96 This is a flowchart showing an example of the process for determining the quantization value in the encoding of the position information or the attribute information in Embodiment 11.

[0113] Fig.97 This is a flowchart showing an example of the decoding process for the position information and the attribute information in Embodiment 11.

[0114] Fig.98 This is a diagram for explaining the first example of the transmission method of the quantization parameter in Embodiment 11.

[0115] Fig.99 This is a diagram for explaining the second example of the transmission method of the quantization parameter in Embodiment 11.

[0116] Fig.100 This is a diagram for explaining the third example of the transmission method of the quantization parameter in Embodiment 11.

[0117] Fig.101 This is a flowchart of the encoding process for the point cloud data in Embodiment 11.

[0118] Fig.102 This is a flowchart showing an example of the process for determining the QP value and updating the additional information in Embodiment 11.

[0119] Fig.103 It is a flowchart showing an example of the process of encoding the determined QP value in Embodiment 11.

[0120] Fig.104 It is a flowchart of the decoding process for the point cloud data in Embodiment 11.

[0121] Fig.105 It is a flowchart showing an example of the process of obtaining the QP value and decoding the QP value of a slice or tile in Embodiment 11.

[0122] Fig.106 It is a diagram showing a syntax example of the GPS in Embodiment 11.

[0123] Fig.107 It is a diagram showing a syntax example of the APS in Embodiment 11.

[0124] Fig.108 It is a diagram showing a syntax example of the header of the location information in Embodiment 11.

[0125] Fig.109 It is a diagram showing a syntax example of the header of the attribute information in Embodiment 11.

[0126] Fig.110 It is a diagram for explaining another example of the transmission method of the quantization parameter in Embodiment 11.

[0127] Fig.111 It is a diagram for explaining another example of the transmission method of the quantization parameter in Embodiment 11.

[0128] Fig.112 It is a diagram for explaining the 9th example of the transmission method of the quantization parameter in Embodiment 11.

[0129] Fig.113 It is a diagram for explaining the control example of the QP value in Embodiment 11.

[0130] Fig.114 It is a flowchart showing an example of the method for determining the QP value based on the object-based quality in Embodiment 11.

[0131] Fig.115 It is a flowchart showing an example of the method for determining the QP value based on rate control in Embodiment 11.

[0132] Fig.116 It is a flowchart of the encoding process in Embodiment 11.

[0133] Fig.117 It is a flowchart of the decoding process in Embodiment 11.

[0134] Fig.118 This is a diagram for explaining an example of a method for transmitting quantization parameters related to Embodiment 12.

[0135] Fig.119 This is a diagram showing a first example of the syntax of the header indicating the syntax and attribute information of the APS related to Embodiment 12.

[0136] Fig.120 This is a diagram showing a second example of the syntax of the APS related to Embodiment 12.

[0137] Fig.121 This is a diagram showing a second example of the syntax of the header of the attribute information related to Embodiment 12.

[0138] Fig.122 This is a diagram showing the relationship between the headers of the SPS, APS, and attribute information related to Embodiment 12.

[0139] Fig.123 This is a flowchart of the encoding process related to Embodiment 12.

[0140] Fig.124 This is a flowchart of the decoding process related to Embodiment 12.

[0141] Fig.125 This is a block diagram showing the structure of a three-dimensional data encoding device related to Embodiment 13.

[0142] Fig.126 This is a block diagram showing the structure of a three-dimensional data decoding device related to Embodiment 13.

[0143] Fig.127 This is a diagram showing an example of the setting of the LoD of Embodiment 13.

[0144] Fig.128 This is a diagram showing an example of the hierarchical structure of the RAHT related to Embodiment 13.

[0145] Fig.129 This is a block diagram of a three-dimensional data encoding device related to Embodiment 13.

[0146] Fig.130 This is a block diagram of the segmentation unit related to Embodiment 13.

[0147] Fig.131 This is a block diagram of the attribute information encoding unit related to Embodiment 13.

[0148] Fig.132 This is a block diagram of a three-dimensional data decoding device related to Embodiment 13.

[0149] Fig.133It is a block diagram of the attribute information decoding unit according to Embodiment 13.

[0150] Fig.134 It is a diagram showing a setting example of quantization parameters in tile and slice division according to Embodiment 13.

[0151] Fig.135 It is a diagram showing a setting example of quantization parameters according to Embodiment 13.

[0152] Fig.136 It is a diagram showing a setting example of quantization parameters according to Embodiment 13.

[0153] Fig.137 It is a diagram showing a syntax example of the attribute information header according to Embodiment 13.

[0154] Fig.138 It is a diagram showing a syntax example of the attribute information header according to Embodiment 13.

[0155] Fig.139 It is a diagram showing a setting example of quantization parameters according to Embodiment 13.

[0156] Fig.140 It is a diagram showing a syntax example of the attribute information header according to Embodiment 13.

[0157] Fig.141 It is a diagram showing a syntax example of the attribute information header according to Embodiment 13.

[0158] Fig.142 It is a flowchart of three-dimensional data encoding processing according to Embodiment 13.

[0159] Fig.143 It is a flowchart of attribute information encoding processing according to Embodiment 13.

[0160] Fig.144 It is a flowchart of ΔQP determination processing according to Embodiment 13.

[0161] Fig.145 It is a flowchart of three-dimensional data decoding processing according to Embodiment 13.

[0162] Fig.146 It is a flowchart of attribute information decoding processing according to Embodiment 13.

[0163] Fig.147 It is a block diagram of the attribute information encoding unit according to Embodiment 13.

[0164] Fig.148 It is a block diagram of the attribute information decoding unit according to Embodiment 13.

[0165] Fig.149 This is a diagram showing an example of setting quantization parameters related to Embodiment 13.

[0166] Fig.150 This is a diagram showing a syntax example of the attribute information header related to Embodiment 13.

[0167] Fig.151 This is a diagram showing a syntax example of the attribute information header related to Embodiment 13.

[0168] Fig.152 This is a flowchart of the three-dimensional data encoding process related to Embodiment 13.

[0169] Fig.153 This is a flowchart of the attribute information encoding process related to Embodiment 13.

[0170] Fig.154 This is a flowchart of the three-dimensional data decoding process related to Embodiment 13.

[0171] Fig.155 This is a flowchart of the attribute information decoding process related to Embodiment 13.

[0172] Fig.156 This is a block diagram of the attribute information encoding unit related to Embodiment 13.

[0173] Fig.157 This is a block diagram of the attribute information decoding unit related to Embodiment 13.

[0174] Fig.158 This is a diagram showing a syntax example of the attribute information header related to Embodiment 13.

[0175] Fig.159 This is a flowchart of the three-dimensional data encoding process related to Embodiment 13.

[0176] Fig.160 This is a flowchart of the three-dimensional data decoding process related to Embodiment 13.

[0177] Fig.161 This is a diagram showing an example of generating the LoD hierarchy of Embodiment 14.

[0178] Fig.162 This is a diagram showing an example of dividing the hierarchy related to Embodiment 14.

[0179] Fig.163 This is a diagram showing an example of dividing the hierarchy related to Embodiment 14.

[0180] Fig.164 This is a diagram showing an example of dividing the hierarchy related to Embodiment 14.

[0181] Fig.165 This is a diagram showing an example of hierarchical division related to Embodiment 14.

[0182] Fig.166 This is a diagram showing an example of hierarchical division related to Embodiment 14.

[0183] Fig.167 This is a flowchart of three-dimensional data encoding processing related to Embodiment 14.

[0184] Fig.168 This is a flowchart of attribute information encoding processing related to Embodiment 14.

[0185] Fig.169 This is a flowchart of LoD setting processing related to Embodiment 14.

[0186] Fig.170 This is a flowchart of three-dimensional data decoding processing related to Embodiment 14.

[0187] Fig.171 This is a flowchart of attribute information decoding processing related to Embodiment 14.

[0188] Fig.172 This is a block diagram of an attribute information encoding unit related to Embodiment 14.

[0189] Fig.173 This is a block diagram of an attribute information decoding unit related to Embodiment 14. Detailed Embodiment

[0190] A three-dimensional data encoding method according to one aspect of the present disclosure calculates a plurality of coefficient values based on a plurality of attribute information of a plurality of three-dimensional points included in point cloud data, generates a plurality of quantization values by quantizing each of the plurality of coefficient values, generates a bitstream including the plurality of quantization values, the plurality of coefficient values belong to a certain hierarchy among a plurality of hierarchies, and in the quantization, each of the plurality of coefficient values is quantized using a quantization parameter for the hierarchy to which the coefficient value belongs, and the bitstream includes first information indicating a reference quantization parameter and a plurality of second information for calculating a plurality of quantization parameters for the plurality of hierarchies based on the reference quantization parameter.

[0191] Thereby, this three-dimensional data encoding method can switch quantization parameters for each hierarchy, and thus can perform encoding appropriately. In addition, this three-dimensional data encoding method can improve encoding efficiency by encoding the first information indicating the reference quantization parameter and the plurality of second information for calculating a plurality of quantization parameters based on the reference quantization parameter.

[0192] For example, it may also be that each of the plurality of second information represents a difference between the reference quantization parameter and the quantization parameter for the hierarchy.

[0193] For example, it may also be that the bitstream further includes a first flag indicating whether the plurality of second information is included in the bitstream.

[0194] For example, it may also be that the bitstream further includes third information indicating the number of the plurality of second information included in the bitstream.

[0195] For example, it may also be that the plurality of three-dimensional points are classified into one of the plurality of hierarchies based on the position information of the plurality of three-dimensional points.

[0196] For example, it may also be that the plurality of coefficient values are generated by dividing each of the plurality of attribute information into high-frequency components and low-frequency components and hierarchically classifying them into the plurality of hierarchies.

[0197] A three-dimensional data decoding method according to an aspect of the present disclosure uses (i) first information indicating a reference quantization parameter and (ii) a plurality of second information for calculating a plurality of quantization parameters for a plurality of hierarchies included in a bitstream, calculates the quantization parameters for the plurality of hierarchies, inverse-quantizes each of the plurality of quantization values included in the bitstream using the quantization parameter for the hierarchy to which the quantization value belongs among the calculated quantization parameters for the plurality of hierarchies, generates a plurality of coefficient values, and calculates a plurality of attribute information of a plurality of three-dimensional points included in point cloud data based on the plurality of coefficient values.

[0198] Accordingly, this three-dimensional data decoding method can switch quantization parameters for each hierarchy, and thus can perform decoding appropriately. In addition, this three-dimensional data decoding method can appropriately decode a bitstream with improved coding efficiency by using the first information indicating the reference quantization parameter and the plurality of second information for calculating a plurality of quantization parameters based on the reference quantization parameter.

[0199] For example, it may also be that the plurality of second information respectively represent the difference between the reference quantization parameter and the quantization parameter for the hierarchy.

[0200] For example, it may also be that the bitstream further includes a first flag indicating whether the plurality of second information is included in the bitstream.

[0201] For example, it may also be that the bitstream further includes third information indicating the number of the plurality of second information included in the bitstream.

[0202] For example, it may also be that the plurality of three-dimensional points are classified into one of the plurality of hierarchies based on the position information of the plurality of three-dimensional points.

[0203] For example, it may also be that the plurality of coefficient values are generated by dividing each of the plurality of pieces of attribute information into a high-frequency component and a low-frequency component and hierarchically classifying them into the plurality of hierarchies.

[0204] In addition, a three-dimensional data encoding apparatus according to an aspect of the present disclosure includes a processor and a memory. The processor uses the memory to calculate a plurality of coefficient values based on a plurality of pieces of attribute information of a plurality of three-dimensional points included in point cloud data, generates a plurality of quantization values by quantizing each of the plurality of coefficient values, and generates a bitstream including the plurality of quantization values. The plurality of coefficient values belong to a certain hierarchy among a plurality of hierarchies. In the quantization, for each of the plurality of coefficient values, quantization is performed using a quantization parameter for the hierarchy to which the coefficient value belongs. The bitstream includes first information indicating a reference quantization parameter and a plurality of second information for calculating a plurality of quantization parameters for the plurality of hierarchies based on the reference quantization parameter.

[0205] Thus, the three-dimensional data encoding apparatus can switch quantization parameters for each hierarchy, and thus can perform encoding appropriately. In addition, the three-dimensional data encoding apparatus can improve encoding efficiency by encoding the first information indicating the reference quantization parameter and the plurality of second information for calculating a plurality of quantization parameters based on the reference quantization parameter.

[0206] In addition, a three-dimensional data decoding apparatus according to an aspect of the present disclosure includes a processor and a memory. The processor uses the memory to calculate quantization parameters for the plurality of hierarchies by using (i) first information indicating a reference quantization parameter and (ii) a plurality of second information for calculating a plurality of quantization parameters for the plurality of hierarchies based on the reference quantization parameter included in a bitstream, generates a plurality of coefficient values by inverse quantizing each of the plurality of quantization values included in the bitstream using the quantization parameter for the hierarchy to which the quantization value belongs among the calculated quantization parameters for the plurality of hierarchies, and calculates a plurality of pieces of attribute information of a plurality of three-dimensional points included in point cloud data based on the plurality of coefficient values.

[0207] Thus, the three-dimensional data decoding apparatus can switch quantization parameters for each hierarchy, and thus can perform decoding appropriately. In addition, the three-dimensional data decoding apparatus can appropriately decode a bitstream with improved encoding efficiency by using the first information indicating the reference quantization parameter and the plurality of second information for calculating a plurality of quantization parameters based on the reference quantization parameter.

[0208] In addition, these general or specific aspects can be implemented by a system, a method, an integrated circuit, a computer program, or a recording medium such as a computer-readable CD-ROM, and can be implemented by any combination of a system, a method, an integrated circuit, a computer program, and a recording medium.

[0209] Hereinafter, embodiments will be specifically described with reference to the accompanying drawings. In addition, all the embodiments to be described below are specific examples showing the present disclosure. The numerical values, shapes, materials, constituent elements, arrangement positions of the constituent elements, connection methods, steps, order of steps, etc. shown in the following embodiments are all examples, and their gist is not to limit the present disclosure. And, among the constituent elements of the following embodiments, the constituent elements not described in the independent claims are described as optional constituent elements.

[0210] (Embodiment 1)

[0211] First, the data structure of the encoded three-dimensional data (hereinafter also referred to as encoded data) related to the present embodiment will be described. Figure 1 The configuration of the encoded three-dimensional data related to the present embodiment is shown.

[0212] In the present embodiment, the three-dimensional space is divided into spaces (SPCs) corresponding to pictures in the encoding of moving images, and the three-dimensional data is encoded in units of space. The space is further divided into volumes (VLMs) corresponding to macroblocks and the like in the moving image encoding, and prediction and transformation are performed in units of VLM. A volume includes a plurality of voxels (VXLs), which are the smallest units corresponding to position coordinates. In addition, prediction means, similar to the prediction performed in two-dimensional images, referring to other processing units, generating predicted three-dimensional data similar to the processing unit to be processed, and encoding the difference between the predicted three-dimensional data and the processing unit to be processed. And this prediction includes not only spatial prediction referring to other prediction units at the same time, but also temporal prediction referring to prediction units at different times.

[0213] For example, when a three-dimensional data encoding device (hereinafter also referred to as an encoding device) encodes a three-dimensional space represented by point cloud data such as a point cloud, according to the size of the voxels, each point of the point cloud or a plurality of points included in the voxel is encoded together. If the voxels are subdivided, the three-dimensional shape of the point cloud can be accurately represented, and if the size of the voxels is increased, the three-dimensional shape of the point cloud can be roughly represented.

[0214] In addition, although the case where the three-dimensional data is a point cloud is described as an example below, the three-dimensional data is not limited to the point cloud and can be any form of three-dimensional data.

[0215] Moreover, hierarchical voxels can be used. In this case, in the nth level, it is possible to sequentially show whether there are sampling points in the levels below the n - 1th level (the lower levels of the nth level). For example, when only decoding the nth level, when there are sampling points in the levels below the n - 1th level, it is possible to perform decoding by regarding that there are sampling points at the center of the voxels in the nth level.

[0216] Further, the encoding device obtains point cloud data using a distance sensor, a stereo camera, a monocular camera, a gyroscope, an inertial sensor, or the like.

[0217] Regarding space, similar to the encoding of moving images, it is at least classified into any one of the following three prediction structures: Intra-Spatial Prediction (I-SPC) that can be decoded independently, Predictive-Spatial Prediction (P-SPC) that can only be referenced unidirectionally, and Bi-Directional-Spatial Prediction (B-SPC) that can be referenced bidirectionally. Also, space has two types of time information: decoding time and display time.

[0218] Further, as Figure 1 shown, as a processing unit including multiple spaces, there is a Group Of Space (GOS) which is a random access unit. Moreover, as a processing unit including multiple GOSs, there is a World Space (WLD).

[0219] The spatial region occupied by the world space is associated with an absolute position on the earth through GPS or latitude and longitude information, etc. This position information is stored as meta information. Additionally, the meta information can be included in the encoded data or transmitted separately from the encoded data.

[0220] Further, within a GOS, all SPCs can be three-dimensionally adjacent, or there can be an SPC that is not three-dimensionally adjacent to other SPCs.

[0221] In addition, hereinafter, processes such as encoding, decoding, or referencing corresponding to the three-dimensional data included in processing units such as GOS, SPC, or VLM are also simply referred to as encoding, decoding, or referencing the processing unit. And the three-dimensional data included in the processing unit includes at least one group of spatial positions such as three-dimensional coordinates and characteristic values such as color information.

[0222] Next, the prediction structure of SPCs in a GOS will be described. Multiple SPCs within the same GOS, or multiple VLMs within the same SPC, although occupying different spaces from each other, hold the same time information (decoding time and display time).

[0223] Further, within a GOS, the SPC that is the first in the decoding order is an I-SPC. And there are two types of GOSs: a closed GOS and an open GOS. A closed GOS is a GOS in which all SPCs within the GOS can be decoded when starting to decode from the first I-SPC. In an open GOS, within the GOS, some SPCs whose display time is earlier than that of the first I-SPC refer to a different GOS and can only be decoded in that GOS.

[0224] In addition, in encoded data such as map information, there are cases where the WLD is decoded from the direction opposite to the encoding order. If there is a dependency between GOSs, it is difficult to perform reverse regeneration. Therefore, in such cases, a closed GOS is basically adopted.

[0225] Moreover, the GOS has a layer structure in the height direction, and encoding or decoding is sequentially performed starting from the SPC of the bottom layer.

[0226] Figure 2 An example of the prediction structure between SPCs of the layer belonging to the bottom layer of the GOS is shown. Figure 3 An example of the inter-layer prediction structure is shown.

[0227] There is one or more I-SPCs within the GOS. Although there are objects such as people, animals, cars, bicycles, traffic lights, or buildings that serve as land marks in the three-dimensional space, it is particularly effective when encoding small-sized objects as I-SPCs. For example, when a three-dimensional data decoding device (hereinafter also referred to as the decoding device) decodes the GOS with a low processing amount or at high speed, only the I-SPCs within the GOS are decoded.

[0228] Moreover, the encoding device can switch the encoding interval or occurrence frequency of the I-SPCs according to the density of the objects within the WLD.

[0229] And, in Figure 3 In the configuration shown, the encoding device or the decoding device sequentially performs encoding or decoding for multiple layers starting from the lower layer (layer 1). Accordingly, for example, for an automatically moving vehicle or the like, it is possible to increase the priority of the data near the ground where the amount of information is large.

[0230] In addition, in the encoded data used in a drone or the like, within the GOS, encoding or decoding can be sequentially performed starting from the SPC of the upper layer in the height direction.

[0231] Moreover, the encoding device or the decoding device can also encode or decode multiple layers in such a way that the decoding device generally grasps the GOS and can gradually increase the resolution. For example, the encoding device or the decoding device can perform encoding or decoding in the order of layer 3, 8, 1, 9...

[0232] Next, the method of corresponding static objects and dynamic objects will be described.

[0233] In a three-dimensional space, there are static objects or scenes such as buildings or roads (collectively referred to as static objects hereinafter), and dynamic objects such as vehicles or people (referred to as dynamic objects hereinafter). Detection of an object can be performed separately by extracting feature points from data of a point cloud, an image captured by a stereo camera, or the like. Here, an example of an encoding method for a dynamic object will be described.

[0234] The first method is a method of encoding without distinguishing between static objects and dynamic objects. The second method is a method of distinguishing between static objects and dynamic objects by identification information.

[0235] For example, a GOS is used as an identification unit. In this case, the GOS including the SPC constituting the static object and the GOS including the SPC constituting the dynamic object are distinguished within the encoded data or by identification information stored separately from the encoded data.

[0236] Alternatively, an SPC is used as an identification unit. In this case, only the SPC including the VLM constituting the static object and the SPC including the VLM constituting the dynamic object are distinguished by the above-mentioned identification information.

[0237] Alternatively, a VLM or a VXL can be used as an identification unit. In this case, the VLM or VXL including the static object and the VLM or VXL including the dynamic object are distinguished by the above-mentioned identification information.

[0238] Furthermore, the encoding device can encode a dynamic object as one or more VLMs or SPCs, and encode the VLM or SPC including the static object and the SPC including the dynamic object as different GOSs. And when the size of the GOS becomes variable according to the size of the dynamic object, the encoding device stores the size of the GOS separately as meta information.

[0239] Furthermore, the encoding device encodes the static object and the dynamic object independently of each other, and for the world space constituted by the static object, the dynamic object can be overlapped. At this time, the dynamic object is constituted by one or more SPCs, and each SPC corresponds to one or more SPCs of the static object overlapping the SPC. In addition, the dynamic object may not be represented by an SPC, and may be represented by one or more VLMs or VXLs.

[0240] Furthermore, the encoding device can encode the static object and the dynamic object as different streams.

[0241] Further, the encoding device can also generate a GOS including one or more SPCs that constitute a dynamic object. Moreover, the encoding device can set the GOS (GOS_M) including the dynamic object and the GOS of the static object corresponding to the spatial region of GOS_M to have the same size (occupy the same spatial region). In this way, overlapping processing can be performed in units of GOS.

[0242] The P-SPC or B-SPC that constitutes the dynamic object can also refer to the SPCs included in different encoded GOSs. The position of the dynamic object changes over time. In the case where the same dynamic object is encoded as GOSs at different times, cross-GOS reference is effective from the viewpoint of compression ratio.

[0243] Also, the above first method and second method can be switched according to the use of the encoded data. For example, in the case where three-dimensional data is encoded and applied as a map, since separation from the dynamic object is desired, the encoding device adopts the second method. In addition, when the encoding device encodes three-dimensional data of an event such as a concert or sports, if separation of the dynamic object is not required, the first method is adopted.

[0244] Also, the decoding time and display time of the GOS or SPC can be stored in the encoded data or as meta-information. And the time information of the static object can all be the same. At this time, the actual decoding time and display time can be determined by the decoding device. Or, as the decoding time, different values can be assigned to each GOS or SPC, and as the display time, the same value can be assigned to all of them. Moreover, as shown in the decoder mode in dynamic image coding such as HRD (Hypothetical Reference Decoder) of HEVC, the decoder has a buffer of a specified size, and as long as the bitstream is read at a specified bit rate according to the decoding time, a model that will not be damaged and can be guaranteed to be decoded can be imported.

[0245] Next, the configuration of GOSs in the world space will be described. The coordinates of the three-dimensional space in the world space are represented by three mutually orthogonal coordinate axes (x-axis, y-axis, z-axis). By setting a specified rule in the encoding order of GOSs, GOSs that are spatially adjacent can be continuously encoded in the encoded data. For example, in Figure 4 the example shown, the GOSs in the xz plane are continuously encoded. After the encoding of all GOSs in one xz plane is completed, the value of the y-axis is updated. That is, as encoding progresses, the world space extends in the y-axis direction. And the index number of the GOS is set as the encoding order.

[0246] Here, the three-dimensional space of the world space corresponds one-to-one with the absolute coordinates such as GPS, or latitude and longitude. Alternatively, the three-dimensional space can be represented by the relative position with respect to a preset reference position. The directions of the x-axis, y-axis, and z-axis of the three-dimensional space are represented as direction vectors determined based on latitude and longitude, etc., and this direction vector is stored together with the encoded data as meta information.

[0247] Also, the size of the GOS is set to be fixed, and the encoding device stores this size as meta information. Also, the size of the GOS can be switched, for example, according to whether it is in the city, indoors, outdoors, etc. That is, the size of the GOS can be switched according to the quantity or nature of the object having the value as information. Alternatively, the encoding device can appropriately switch the size of the GOS or the interval of the I-SPC within the GOS according to the density of the object, etc. within the same world space. For example, the encoding device sets the size of the GOS to be smaller and the interval of the I-SPC within the GOS to be shorter when the density of the object is higher.

[0248] In Figure 5 In the example of, in the area of the 3rd to 10th GOS, since the density of the object is high, in order to achieve fine-grained random access, the GOS is subdivided. Also, the 7th to 10th GOSs are respectively located behind the 3rd to 6th GOSs.

[0249] Next, the configuration and operation process of the three-dimensional data encoding device according to the present embodiment will be described. Figure 6 is a block diagram of the three-dimensional data encoding device 100 according to the present embodiment. Figure 7 is a flowchart showing an operation example of the three-dimensional data encoding device 100.

[0250] Figure 6 The three-dimensional data encoding device 100 shown generates encoded three-dimensional data 112 by encoding the three-dimensional data 111. This three-dimensional data encoding device 100 includes: an acquisition unit 101, an encoding area determination unit 102, a division unit 103, and an encoding unit 104.

[0251] As Figure 7 shown, first, the acquisition unit 101 acquires the three-dimensional data 111 as point cloud data (S101).

[0252] Next, the encoding area determination unit 102 determines the area of the encoding object from the spatial area corresponding to the acquired point cloud data (S102). For example, the encoding area determination unit 102 determines the spatial area around the position as the area of the encoding object according to the position of the user or the vehicle.

[0253] Next, the segmentation unit 103 divides the point cloud data included in the region of the object to be encoded into respective processing units. Here, the processing units are the above-mentioned GOS, SPC, etc. And the region of the object to be encoded corresponds to, for example, the above-mentioned world space. Specifically, the segmentation unit 103 divides the point cloud data into processing units according to the size of the GOS set in advance, the presence or size of dynamic objects (S103). And the segmentation unit 103 determines the start position of the SPC that becomes the head in the encoding order in each GOS.

[0254] Next, the encoding unit 104 generates encoded three-dimensional data 112 by sequentially encoding a plurality of SPCs within each GOS (S104).

[0255] In addition, here, after dividing the region of the object to be encoded into GOS and SPC, an example of encoding each GOS is shown. However, the order of processing is not limited to the above. For example, after determining the composition of one GOS, the GOS can be encoded, and then the order of determining the composition of the GOS, etc. can be carried out.

[0256] In this way, the three-dimensional data encoding device 100 generates encoded three-dimensional data 112 by encoding the three-dimensional data 111. Specifically, the three-dimensional data encoding device 100 divides the three-dimensional data into random access units, that is, divides it into first processing units (GOS) corresponding to three-dimensional coordinates respectively, divides the first processing units (GOS) into a plurality of second processing units (SPC), and divides the second processing units (SPC) into a plurality of third processing units (VLM). And the third processing unit (VLM) includes one or more voxels (VXL), and the voxel (VXL) is the smallest unit corresponding to the position information.

[0257] Next, the three-dimensional data encoding device 100 generates encoded three-dimensional data 112 by encoding each of the plurality of first processing units (GOS). Specifically, the three-dimensional data encoding device 100 encodes each of the plurality of second processing units (SPC) in each first processing unit (GOS). And the three-dimensional data encoding device 100 encodes each of the plurality of third processing units (VLM) in each second processing unit (SPC).

[0258] For example, when the first processing unit (GOS) of the processing object is a closed GOS, the three-dimensional data encoding device 100 encodes the second processing unit (SPC) of the processing object included in the first processing unit (GOS) of the processing object with reference to other second processing units (SPCs) included in the first processing unit (GOS) of the processing object. That is, the three-dimensional data encoding device 100 does not refer to the second processing units (SPCs) included in the first processing unit (GOS) different from the first processing unit (GOS) of the processing object.

[0259] On the other hand, when the first processing unit (GOS) of the processing object is an open GOS, for the second processing unit (SPC) of the processing object included in the first processing unit (GOS) of the processing object, encoding is performed with reference to other second processing units (SPCs) included in the first processing unit (GOS) of the processing object or second processing units (SPCs) included in the first processing unit (GOS) different from the first processing unit (GOS) of the processing object.

[0260] Moreover, the three-dimensional data encoding device 100 selects one of the first type (I-SPC) of other second processing units (SPCs), the second type (P-SPC) of one other second processing unit (SPC), and the third type of two other second processing units (SPCs) as the type of the second processing unit (SPC) of the processing object, and encodes the second processing unit (SPC) of the processing object according to the selected type.

[0261] Next, the configuration and operation process of the three-dimensional data decoding device according to the present embodiment will be described. Figure 8 It is a block diagram of the three-dimensional data decoding device 200 according to the present embodiment. Fig. 9 It is a flowchart showing an operation example of the three-dimensional data decoding device 200.

[0262] Figure 8 The shown three-dimensional data decoding device 200 generates decoded three-dimensional data 212 by decoding the encoded three-dimensional data 211. Here, the encoded three-dimensional data 211 is, for example, the encoded three-dimensional data 112 generated by the three-dimensional data encoding device 100. The three-dimensional data decoding device 200 includes: an acquisition unit 201, a decoding start GOS determination unit 202, a decoding SPC determination unit 203, and a decoding unit 204.

[0263] First, acquisition unit 201 acquires encoded three-dimensional data 211 (S201). Next, decoding start GOS determination unit 202 determines the GOS to be decoded (S202). Specifically, decoding start GOS determination unit 202 refers to the meta information within the encoded three-dimensional data 211 or stored separately from the encoded three-dimensional data, and determines the GOS including the spatial position, object, or SPC corresponding to the time at which decoding starts as the GOS to be decoded.

[0264] Next, decoding SPC determination unit 203 determines the type (I, P, B) of the SPC to be decoded within the GOS (S203). For example, decoding SPC determination unit 203 determines (1) whether to decode only I-SPC, (2) whether to decode I-SPC and P-SPC, (3) whether to decode all types. Additionally, in cases where the type of SPC to be decoded, such as decoding all SPCs, is predefined, this step may not be performed.

[0265] Next, decoding unit 204 acquires the SPC that is the start in the decoding order (same as the encoding order) within the GOS, the address position where it starts within the encoded three-dimensional data 211, acquires the encoded data of the start SPC from this address position, and decodes each SPC in sequence starting from this start SPC (S204). And the above address position is stored in meta information or the like.

[0266] In this way, three-dimensional data decoding device 200 decodes decoded three-dimensional data 212. Specifically, three-dimensional data decoding device 200 generates decoded three-dimensional data 212 of the first processing unit (GOS) as a random access unit by decoding each of the encoded three-dimensional data 211 of the first processing unit (GOS) corresponding to the three-dimensional coordinates respectively. More specifically, three-dimensional data decoding device 200 decodes each of the multiple second processing units (SPCs) within each first processing unit (GOS). And three-dimensional data decoding device 200 decodes each of the multiple third processing units (VLM) within each second processing unit (SPC).

[0267] The meta information for random access is described below. This meta information is generated by three-dimensional data encoding device 100 and is included in the encoded three-dimensional data 112 (211).

[0268] In the random access of conventional two-dimensional moving images, decoding starts from the first frame of the random access unit near the specified time. However, in the world space, random access is envisioned not only for time but also for (coordinates or objects, etc.).

[0269] Therefore, in order to achieve at least random access to the three elements of coordinates, objects, and time, a table is prepared in which each element is associated with an index number of the GOS. Moreover, the index number of the GOS is associated with the address of the I-SPC that is the start of the GOS. Fig.10 An example of the table included in the meta-information is shown. Additionally, it is not necessary to use Fig.10 all of the tables shown. At least one table may be used.

[0270] Hereinafter, as an example, random access starting from coordinates will be described. When accessing the coordinates (x2, y2, z2), first referring to the coordinate-GOS table, it can be known that the location with the coordinates (x2, y2, z2) is included in the second GOS. Then, referring to the GOS address table, since it can be known that the address of the I-SPC at the start of the second GOS is addr(2), the decoding unit 204 obtains data from this address and starts decoding.

[0271] In addition, the address can be an address in a logical format or a physical address of an HDD or a memory. Also, information for determining a file segment can be used instead of the address. For example, a file segment is a unit obtained by segmenting one or more GOSs, etc.

[0272] Moreover, when an object spans multiple GOSs, the GOSs to which the multiple objects belong can also be shown in the object GOS table. If the multiple GOSs are closed GOSs, the encoding device and the decoding device can perform encoding or decoding in parallel. Additionally, if the multiple GOSs are open GOSs, the compression efficiency can be further improved by referring to each other among the multiple GOSs.

[0273] Examples of objects include people, animals, cars, bicycles, traffic lights, or buildings that are land marks, etc. For example, when the three-dimensional data encoding device 100 encodes in the world space, characteristic points unique to the object are extracted from a three-dimensional point cloud or the like, the object is detected based on the characteristic points, and the detected object can be set as a random access point.

[0274] In this way, the three-dimensional data encoding device 100 generates the first information, which shows a plurality of first processing units (GOSs) and the three-dimensional coordinates corresponding to each of the plurality of first processing units (GOSs). And encoding the three-dimensional data 112(211) includes this first information. And the first information further shows at least one of the object, time, and data storage destination corresponding to each of the plurality of first processing units (GOSs).

[0275] The three-dimensional data decoding device 200 obtains the first information from the encoded three-dimensional data 211, uses the first information to determine the encoded three-dimensional data 211 of the first processing unit corresponding to the specified three-dimensional coordinates, object, or time, and decodes the encoded three-dimensional data 211.

[0276] Examples of other meta-information will be described below. In addition to the meta-information for random access, the three-dimensional data encoding device 100 can also generate and store the following meta-information. Also, the three-dimensional data decoding device 200 can also use the meta-information during decoding.

[0277] In the case of using three-dimensional data as map information, etc., a profile is specified according to the use, and the information indicating the profile can be included in the meta-information. For example, a profile for urban areas or suburbs is specified, or a profile for flying objects is specified, and the maximum or minimum size of the world space, SPC, or VLM is defined respectively. For example, in the profile for urban areas, more detailed information is required than in the suburbs, so the minimum size of the VLM is set smaller.

[0278] The meta-information can also include a tag value indicating the type of object. The tag value corresponds to the VLM, SPC, or GOS that constitutes the object. The tag value can be set according to the type of object, etc. For example, the tag value "0" represents "person", the tag value "1" represents "car", and the tag value "2" represents "traffic light". Or, in the case where the type of object is difficult to judge or does not need to be judged, a tag value indicating properties such as size, or whether it is a dynamic object or a static object can also be used.

[0279] Also, the meta-information can include information indicating the range of the spatial region occupied by the world space.

[0280] Also, the meta-information can store the size of the SPC or VXL as the header information shared by the entire stream of encoded data, or multiple SPCs such as SPCs within the GOS.

[0281] Also, the meta-information can include identification information such as a distance sensor or camera used in the generation of the point cloud, or information indicating the position accuracy of the point group within the point cloud.

[0282] Also, the meta-information can include information indicating whether the world space is composed only of static objects or contains dynamic objects.

[0283] A modification example of the present embodiment will be described below.

[0284] An encoding device or a decoding device can encode or decode two or more SPCs or GOSs that are different from each other in parallel. The GOSs encoded or decoded in parallel can be determined based on meta information indicating the spatial positions of the GOSs, etc.

[0285] In a case where three-dimensional data is used as a spatial map when a vehicle or a flying object moves, or in a case where such a spatial map is generated, etc., the encoding device or the decoding device can encode or decode a GOS or an SPC included in a space determined based on GPS, path information, or magnification ratio, etc.

[0286] Moreover, the decoding device can also start decoding sequentially from a space close to its own position or travel path. The encoding device or the decoding device can also perform encoding or decoding by making the priority of a space far from its own position or travel path lower than that of a space close to it. Here, reducing the priority means reducing the processing order, reducing the resolution (post-screening processing), or reducing the image quality (improving the encoding efficiency. For example, increasing the quantization step size), etc.

[0287] Moreover, when the decoding device decodes encoded data that is hierarchically encoded in a space, it can also decode only the lower layer.

[0288] Moreover, the decoding device can also start decoding from the lower layer first according to the magnification ratio or use of the map.

[0289] Moreover, in applications such as self-position estimation or object recognition performed during the automatic travel of an automobile or a robot, the encoding device or the decoding device can also reduce the resolution of an area outside the area within a specified height from the road surface (the area to be recognized) to perform encoding or decoding.

[0290] Moreover, the encoding device can also encode the point clouds representing the spatial shapes of the indoor and outdoor spaces separately. For example, by separating the GOS representing the indoor (indoor GOS) from the GOS representing the outdoor (outdoor GOS), the decoding device can select the GOS to be decoded according to the viewpoint position when using the encoded data.

[0291] Moreover, the encoding device can make the indoor GOS and the outdoor GOS with close coordinates adjacent in the encoding stream to perform encoding. For example, the encoding device corresponds the identifiers of the two, and stores information indicating that the identifiers are corresponding in the encoding stream or in separately stored meta information. Accordingly, the decoding device can refer to the information in the meta information to identify the indoor GOS and the outdoor GOS with close coordinates.

[0292] Also, the encoding device can also switch the size of GOS or SPC between indoor GOS and outdoor GOS. For example, the encoding device sets the size of GOS to be smaller indoors compared to outdoors. Also, the encoding device can also change the accuracy when extracting feature points from the point cloud or the accuracy of object detection, etc., between indoor GOS and outdoor GOS.

[0293] Also, the encoding device can attach information for the decoding device to distinguish and display dynamic objects from static objects to the encoded data. Accordingly, the decoding device can combine and represent dynamic objects with a red frame or explanatory text, etc. In addition, the decoding device can also represent only with a red frame or explanatory text instead of the dynamic object. Also, the decoding device can represent more detailed object categories. For example, a car can be represented by a red frame, and a person can be represented by a yellow frame.

[0294] Also, the encoding device or the decoding device can determine whether to encode or decode dynamic objects and static objects as different SPCs or GOSs according to the appearance frequency of dynamic objects, or the ratio of static objects to dynamic objects, etc. For example, when the appearance frequency or ratio of dynamic objects exceeds the threshold, SPCs or GOSs in which dynamic objects and static objects are mixed are allowed, and when the appearance frequency or ratio of dynamic objects does not exceed the threshold, SPCs or GOSs in which dynamic objects and static objects are mixed are not allowed.

[0295] When a dynamic object is detected from two-dimensional image information of a camera instead of from a point cloud, the encoding device can separately obtain information (such as a frame or text) for identifying the detection result and the object position, and encode these as part of three-dimensional encoded data. In this case, for the decoding result of static objects, the decoding device overlays and displays auxiliary information (frame or text) representing dynamic objects.

[0296] Also, the encoding device can change the density of VXL or VLM according to the complexity of the shape of static objects, etc. For example, the more complex the shape of the static object, the denser the encoding device sets VXL or VLM. Moreover, the encoding device can determine the quantization step, etc., when quantizing spatial position or color information according to the density of VXL or VLM. For example, the denser VXL or VLM is, the smaller the encoding device sets the quantization step.

[0297] As described above, the encoding device or the decoding device according to this embodiment performs spatial encoding or decoding in a spatial unit having coordinate information.

[0298] Also, the encoding device and the decoding device perform encoding or decoding in volume units within the space. The volume includes voxels, which are the smallest units corresponding to position information.

[0299] Furthermore, the encoding device and the decoding device perform encoding or decoding by establishing a correspondence table that associates each element of spatial information including coordinates, objects, and time, etc. with a GOP, or a correspondence table corresponding between each element, to establish a correspondence between any elements. Also, the decoding device determines coordinates using the value of the selected element, and determines a volume, voxel, or space based on the coordinates, and decodes the space including the volume or voxel, or the determined space.

[0300] Furthermore, the encoding device determines a volume, voxel, or space that can be selected by an element through feature point extraction or object recognition, and encodes it as a volume, voxel, or space that can be randomly accessed.

[0301] The space is divided into three types, namely: I-SPC that can be encoded or decoded by the space alone, P-SPC that encodes or decodes with reference to any one processed space, and B-SPC that encodes or decodes with reference to any two processed spaces.

[0302] One or more volumes correspond to static objects or dynamic objects. The space containing static objects and the space containing dynamic objects are encoded or decoded as different GOSs from each other. That is, the SPC containing static objects and the SPC containing dynamic objects are assigned to different GOSs.

[0303] Dynamic objects are encoded or decoded for each object and correspond to one or more spaces containing only static objects. That is, multiple dynamic objects are encoded separately, and the encoded data of the multiple dynamic objects obtained corresponds to the SPC containing only static objects.

[0304] The encoding device and the decoding device perform encoding or decoding by increasing the priority of I-SPC within the GOS. For example, the encoding device performs encoding in a manner that reduces the degradation of I-SPC (after decoding, the original three-dimensional data can be reproduced more faithfully). Also, the decoding device decodes only I-SPC, for example.

[0305] The encoding device can change the frequency of using I-SPC according to the density or value (quantity) of objects in the world space to perform encoding. That is, the encoding device changes the frequency of selecting I-SPC according to the number or density of objects included in the three-dimensional data. For example, the encoding device increases the usage frequency of the I space as the density of objects in the world space is greater.

[0306] Furthermore, the encoding device sets random access points in units of GOS, and stores information indicating the spatial region corresponding to the GOS in the header information.

[0307] The encoding device, for example, uses a default value as the spatial size of the GOS. Additionally, the encoding device can also change the size of the GOS according to the value (quantity) or density of the object or dynamic object. For example, when the object or dynamic object is denser or has a larger quantity, the encoding device sets the spatial size of the GOS to be smaller.

[0308] Moreover, the space or volume includes a group of feature points derived using information obtained from sensors such as depth sensors, gyroscopes, or cameras. The coordinates of the feature points are set as the center positions of the voxels. And through the subdivision of the voxels, high-precision position information can be achieved.

[0309] The group of feature points is derived using multiple pictures. The multiple pictures have at least the following two types of time information, namely: actual time information, and the same time information in the multiple pictures corresponding to the space (for example, the encoding time for rate control, etc.).

[0310] And encoding or decoding is performed in units of GOS including one or more spaces.

[0311] The encoding device and the decoding device predict the P space or B space in the GOS of the processing object by referring to the spaces in the processed GOS.

[0312] Alternatively, the encoding device and the decoding device do not refer to different GOSs, but use the processed spaces in the GOS of the processing object to predict the P space or B space in the GOS of the processing object.

[0313] And the encoding device and the decoding device send or receive the encoded stream in units of a world space including one or more GOSs.

[0314] And the GOS has a layer structure in at least one direction within the world space. The encoding device and the decoding device perform encoding or decoding starting from the lower layer. For example, the GOS that can be randomly accessed belongs to the lowest layer. The GOS belonging to the upper layer only refers to the GOSs belonging to the layers below the same layer. That is, the GOS is spatially divided in a predefined direction and includes multiple layers each having one or more SPCs. The encoding device and the decoding device perform encoding or decoding for each SPC by referring to the SPCs included in the same layer as or lower than that SPC.

[0315] And the encoding device and the decoding device continuously perform encoding or decoding on the GOSs within the unit of the world space including multiple GOSs. The encoding device and the decoding device write or read the information indicating the order (direction) of encoding or decoding as metadata. That is, the encoded data includes the information indicating the encoding order of multiple GOSs.

[0316] Further, the encoding device and the decoding device perform encoding or decoding in parallel for two or more different spaces or GOSs.

[0317] Further, the encoding device and the decoding device perform encoding or decoding on the spatial information (coordinates, size, etc.) of the space or GOS.

[0318] Further, the encoding device and the decoding device perform encoding or decoding on the space or GOS included in a specific space determined according to external information such as GPS, path information, or magnification related to its own position or / and the area size.

[0319] The encoding device or the decoding device performs encoding or decoding by making the priority of a space far from its own position lower than that of a space close to its own position.

[0320] The encoding device sets a direction in the world space according to magnification or use, and encodes the GOS having a layer structure in that direction. And the decoding device preferentially decodes from the lower layer for the GOS having a layer structure in a direction in the world space set according to magnification or use.

[0321] The encoding device changes the feature point extraction, the accuracy of object recognition, or the size of the spatial area included in the indoor and outdoor spaces. However, the encoding device and the decoding device encode or decode the indoor GOS and the outdoor GOS with adjacent coordinates in the world space, and also encode or decode by corresponding these identifiers.

[0322] (Embodiment 2)

[0323] When using the encoded data of the point cloud for an actual device or service, in order to suppress the network bandwidth, it is desired to transmit and receive the required information according to the use. However, such a function does not exist in the encoding structure of the three-dimensional data so far, and thus there is no corresponding encoding method.

[0324] What will be described in this embodiment is a three-dimensional data encoding method and a three-dimensional data encoding device for providing a function of transmitting and receiving the required information according to the use in the encoded data of the three-dimensional point cloud, and a three-dimensional data decoding method and a three-dimensional data decoding device for decoding the encoded data.

[0325] A voxel (VXL) having a feature amount equal to or more than a certain value is defined as a feature voxel (FVXL), and a world space (WLD) composed of FVXLs is defined as a sparse world space (SWLD). Fig.11Shows a sparse world space and a composition example of the world space. In SWLD, it includes: FGOS, which is a GOS composed of FVXL; FSPC, which is an SPC composed of FVXL; and FVLM, which is a VLM composed of FVXL. The data structures and prediction structures of FGOS, FSPC, and FVLM can be the same as those of GOS, SPC, and VLM.

[0326] The feature quantity refers to a feature quantity that represents the three-dimensional position information of VXL or the visible light information of the VXL position. In particular, more feature quantities can be detected at the corners and edges of three-dimensional objects, etc. Specifically, although the feature quantity is the three-dimensional feature quantity or the visible light feature quantity described below, as long as it is a feature quantity that represents the position, brightness, or color information of VXL, etc., it can be any feature quantity.

[0327] As the three-dimensional feature quantity, the SHOT feature quantity (Signature of Histograms of Oriented Gradients), the PFH feature quantity (Point Feature Histograms), or the PPF feature quantity (Point Pair Feature) is adopted.

[0328] The SHOT feature quantity is obtained by dividing the periphery of VXL and calculating the inner product of the normal vector of the reference point and the divided area, and then performing histogramming. This SHOT feature quantity has the characteristics of high dimensionality and high feature expressiveness.

[0329] The PFH feature quantity is obtained by selecting multiple two-point groups near VXL, calculating the normal vector, etc. based on these two points, and then performing histogramming. Since this PFH feature quantity is a histogram feature, it is robust against a small amount of interference and has the characteristic of high feature expressiveness.

[0330] The PPF feature quantity is a feature quantity calculated using the normal vector, etc. according to two VXLs. In this PPF feature quantity, since all VXLs are used, it is robust against occlusion.

[0331] Moreover, as the feature quantity of visible light, SIFT (Scale-Invariant Feature Transform), SURF (Speeded Up Robust Features), or HOG (Histogram of Oriented Gradients), etc. that adopt information such as the brightness gradient information of the image can be used.

[0332] The SWLD is generated by calculating the above-described feature amounts from each VXL of the WLD and extracting the FVXL. Here, the SWLD can be updated each time the WLD is updated, or it can be updated periodically after a certain period of time regardless of the update timing of the WLD.

[0333] The SWLD can be generated for each feature amount. For example, as shown by SWLD1 based on the SHOT feature amount and SWLD2 based on the SIFT feature amount, the SWLD can be generated separately for each feature amount, and the SWLD can be distinguished and used according to the application. Also, the feature amounts of the calculated FVXLs can be held as feature amount information in the respective FVXLs.

[0334] Next, the method of using the sparse world space (SWLD) will be described. Since the SWLD only contains feature voxels (FVXL), the data size is generally smaller compared to the WLD that includes all the VXLs.

[0335] In an application that uses feature amounts to achieve a certain purpose, by using the information of the SWLD instead of the WLD, it is possible to suppress the read time from the hard disk, and it is also possible to suppress the bandwidth and transmission time during network transmission. For example, as map information, the WLD and the SWLD are held in advance in the server, and by switching the transmitted map information to the WLD or the SWLD according to the demand from the client, it is possible to suppress the network bandwidth and the transmission time. Specific examples are shown below.

[0336] Fig.12 And Fig.13 Shows the usage examples of the SWLD and the WLD. As Fig.12 shown, when the client 1 as a vehicle-mounted device needs map information for its own position determination, the client 1 sends a request (S301) for obtaining map data for its own position estimation to the server. The server sends the SWLD to the client 1 according to the acquisition request (S302). The client 1 uses the received SWLD to determine its own position (S303). At this time, the client 1 obtains the VXL information around the client 1 by various methods such as a distance sensor such as a rangefinder, a stereo camera, or a combination of multiple monocular cameras, and estimates its own position information based on the obtained VXL information and the SWLD. Here, the own position information includes the three-dimensional position information and the orientation of the client 1.

[0337] As Fig.13As shown, when the client 2, which is a vehicle-mounted device, needs map information for map rendering purposes such as a 3D map, the client 2 sends a request for obtaining map data for map rendering to the server (S311). The server sends the WLD to the client 2 according to this acquisition request (S312). The client 2 uses the received WLD for map rendering (S313). At this time, the client 2, for example, uses an image captured by its own visible light camera or the like and the WLD obtained from the server to create a concept image, and depicts the created image on a screen such as a car navigation screen.

[0338] As described above, the server sends the SWLD to the client in applications mainly requiring the feature quantities of each VXL for self-position estimation, and sends the WLD to the client in cases where detailed VXL information is required, such as map rendering. Accordingly, the map data can be efficiently transmitted and received.

[0339] In addition, the client can determine which of the SWLD and the WLD it needs and request the server to send the SWLD or the WLD. And the server can determine which of the SWLD or the WLD should be sent according to the status of the client or the network.

[0340] Next, a method for switching the transmission and reception of the sparse world space (SWLD) and the world space (WLD) will be described.

[0341] The reception of the WLD or the SWLD can be switched according to the network bandwidth. Fig.14 A working example in this case is shown. For example, when a low-speed network with a network bandwidth such as in an LTE (Long Term Evolution) environment can be used, when the client accesses the server via the low-speed network (S321), the client obtains the SWLD as map information from the server (S322). In addition, when a high-speed network with a surplus network bandwidth such as in a WiFi environment is used, the client accesses the server via the high-speed network (S323), and obtains the WLD from the server (S324). Accordingly, the client can obtain appropriate map information according to the network bandwidth of the client.

[0342] Specifically, the client receives the SWLD via LTE outdoors, and obtains the WLD via WiFi when entering an indoor area such as a facility. Accordingly, the client can obtain more detailed map information for the indoor area.

[0343] In this way, the client can request the WLD or SWLD from the server according to the frequency band of the network it uses. Alternatively, the client can send the information indicating the frequency band of the network it uses to the server, and the server sends the appropriate data (WLD or SWLD) to the client according to this information. Alternatively, the server can determine the network bandwidth of the client and send the appropriate data (WLD or SWLD) to the client.

[0344] Moreover, the reception of the WLD or SWLD can be switched according to the moving speed. Fig.15 A working example in this case is shown. For example, when the client is moving at high speed (S331), the client receives the SWLD from the server (S332). In addition, when the client is moving at low speed (S333), the client receives the WLD from the server (S334). Accordingly, the client can both suppress the network bandwidth and obtain map information according to the speed. Specifically, when the client is driving on a highway, by receiving the SWLD with less data volume, the map information can be updated at an appropriate speed approximately. In addition, when the client is driving on an ordinary road, by receiving the WLD, more detailed map information can be obtained.

[0345] In this way, the client can request the WLD or SWLD from the server according to its own moving speed. Alternatively, the client can send the information indicating its own moving speed to the server, and the server sends the appropriate data (WLD or SWLD) to the client according to this information. Alternatively, the server can determine the moving speed of the client and send the appropriate data (WLD or SWLD) to the client.

[0346] Moreover, it can also be that the client first obtains the SWLD from the server and then obtains the WLD of the important areas therein. For example, when the client obtains map data, it first obtains the general map information with the SWLD, screens out the areas where features such as buildings, signs, or people appear more, and then obtains the WLD of the screened areas. Accordingly, the client can both suppress the amount of received data from the server and obtain the detailed information of the required areas.

[0347] Moreover, it can also be that the server respectively creates the SWLD for each object according to the WLD, and the client receives them separately according to the usage. Accordingly, the network bandwidth can be suppressed. For example, the server pre-identifies people or vehicles from the WLD and creates the SWLD for people and the SWLD for vehicles. When the client wants to obtain information about the people around, it receives the SWLD for people, and when it wants to obtain information about vehicles, it receives the SWLD for vehicles. And the types of such SWLD can be distinguished according to the information (flags or types, etc.) attached to the head, etc.

[0348] Next, the configuration and operation process of the three-dimensional data encoding device (such as a server) according to this embodiment will be described. Fig.16 is a block diagram of the three-dimensional data encoding device 400 according to this embodiment. Fig.17 is a flowchart of the three-dimensional data encoding process performed by the three-dimensional data encoding device 400.

[0349] Fig.16 The three-dimensional data encoding device 400 shown encodes the input three-dimensional data 411 to generate encoded three-dimensional data 413 and 414 as an encoded stream. Here, the encoded three-dimensional data 413 is the encoded three-dimensional data corresponding to the WLD, and the encoded three-dimensional data 414 is the encoded three-dimensional data corresponding to the SWLD. The three-dimensional data encoding device 400 includes: an acquisition unit 401, an encoding region determination unit 402, an SWLD extraction unit 403, a WLD encoding unit 404, and an SWLD encoding unit 405.

[0350] As Fig.17 shown, first, the acquisition unit 401 acquires the input three-dimensional data 411 as point cloud data in a three-dimensional space (S401).

[0351] Next, the encoding region determination unit 402 determines the spatial region to be encoded according to the spatial region where the point cloud data exists (S402).

[0352] Next, the SWLD extraction unit 403 defines the spatial region to be encoded as the WLD, and calculates the feature amount according to each VXL included in the WLD. And the SWLD extraction unit 403 extracts the VXL whose feature amount is above a predetermined threshold, defines the extracted VXL as the FVXL, and generates the extracted three-dimensional data 412 by adding the FVXL to the SWLD (S403). That is, the extracted three-dimensional data 412 whose feature amount is above the threshold is extracted from the input three-dimensional data 411.

[0353] Next, the WLD encoding unit 404 encodes the input three-dimensional data 411 corresponding to the WLD to generate the encoded three-dimensional data 413 corresponding to the WLD (S404). At this time, the WLD encoding unit 404 attaches information for distinguishing that the encoded three-dimensional data 413 is a stream containing the WLD to the header of the encoded three-dimensional data 413.

[0354] And the SWLD encoding unit 405 encodes the extracted three-dimensional data 412 corresponding to the SWLD to generate the encoded three-dimensional data 414 corresponding to the SWLD (S405). At this time, the SWLD encoding unit 405 attaches information for distinguishing that the encoded three-dimensional data 414 is a stream containing the SWLD to the header of the encoded three-dimensional data 414.

[0355] Moreover, the processing order of the process of generating the encoded three-dimensional data 413 and the process of generating the encoded three-dimensional data 414 may be opposite to the above. Also, part or all of the above processes may be executed in parallel.

[0356] Information assigned to the headers of the encoded three-dimensional data 413 and 414 is defined as a parameter such as "world_type", for example. When world_type = 0, it indicates that the stream contains WLD, and when world_type = 1, it indicates that the stream contains SWLD. When defining more other categories, the assigned value can be increased as in world_type = 2. Also, a specific flag may be included in one of the encoded three-dimensional data 413 and 414. For example, the encoded three-dimensional data 414 may be assigned a flag indicating that the stream contains SWLD. In this case, the decoding device can determine whether the stream contains WLD or SWLD based on the presence or absence of the flag.

[0357] Moreover, the encoding method used by the WLD encoding unit 404 when encoding WLD may be different from the encoding method used by the SWLD encoding unit 405 when encoding SWLD.

[0358] For example, since SWLD data is decimated, the correlation with surrounding data may be lower compared to WLD. Therefore, in the encoding method for SWLD, inter-frame prediction among intra-frame prediction and inter-frame prediction is prioritized compared to the encoding method for WLD.

[0359] Also, it may be that the representation method of the three-dimensional position is different between the encoding method for SWLD and the encoding method for WLD. For example, it may be that the three-dimensional position of FVXL is represented by three-dimensional coordinates in FWLD, and the three-dimensional position is represented by an octree described later in WLD, and vice versa.

[0360] Moreover, the SWLD encoding unit 405 encodes in such a way that the data size of the encoded three-dimensional data 414 of SWLD is smaller than the data size of the encoded three-dimensional data 413 of WLD. As described above, for example, the correlation between data may be reduced in SWLD compared to WLD. Accordingly, the encoding efficiency is reduced, and the data size of the encoded three-dimensional data 414 may be larger than the data size of the encoded three-dimensional data 413 of WLD. Therefore, when the data size of the obtained encoded three-dimensional data 414 is larger than the data size of the encoded three-dimensional data 413 of WLD, the SWLD encoding unit 405 re-encodes to regenerate the encoded three-dimensional data 414 with a reduced data size.

[0361] For example, the SWLD extraction unit 403 regenerates the extracted three-dimensional data 412 with a reduced number of extracted feature points, and the SWLD encoding unit 405 encodes the extracted three-dimensional data 412. Alternatively, the quantization level in the SWLD encoding unit 405 can be made coarser. For example, in the octree structure described later, by rounding the data in the bottom layer, the quantization level can be made coarser.

[0362] Moreover, when the SWLD encoding unit 405 cannot make the data size of the encoded three-dimensional data 414 of SWLD smaller than the data size of the encoded three-dimensional data 413 of WLD, the SWLD encoding unit 405 may not generate the encoded three-dimensional data 414 of SWLD. Alternatively, the encoded three-dimensional data 413 of WLD can be copied to the encoded three-dimensional data 414 of SWLD. That is, the encoded three-dimensional data 413 of WLD can be directly used as the encoded three-dimensional data 414 of SWLD.

[0363] Next, the configuration and the working process of the three-dimensional data decoding device (e.g., client) according to the present embodiment will be described. Fig.18 is a block diagram of the three-dimensional data decoding device 500 according to the present embodiment. Fig.19 is a flowchart of the three-dimensional data decoding process performed by the three-dimensional data decoding device 500.

[0364] Fig.18 The shown three-dimensional data decoding device 500 decodes the encoded three-dimensional data 511 to generate the decoded three-dimensional data 512 or 513. Here, the encoded three-dimensional data 511 is, for example, the encoded three-dimensional data 413 or 414 generated by the three-dimensional data encoding device 400.

[0365] The three-dimensional data decoding device 500 includes: an acquisition unit 501, a header analysis unit 502, a WLD decoding unit 503, and an SWLD decoding unit 504.

[0366] As Fig.19 shown, first, the acquisition unit 501 acquires the encoded three-dimensional data 511 (S501). Next, the header analysis unit 502 analyzes the header of the encoded three-dimensional data 511 to determine whether the encoded three-dimensional data 511 includes a WLD stream or an SWLD stream (S502). For example, the determination is made with reference to the above-mentioned world_type parameter.

[0367] When the encoded three-dimensional data 511 is a stream including a WLD (Yes in S503), the WLD decoding unit 503 decodes the encoded three-dimensional data 511 to generate decoded three-dimensional data 512 of the WLD (S504). On the other hand, when the encoded three-dimensional data 511 is a stream including an SWLD (No in S503), the SWLD decoding unit 504 decodes the encoded three-dimensional data 511 to generate decoded three-dimensional data 513 of the SWLD (S505).

[0368] Moreover, similar to the encoding device, the decoding method used by the WLD decoding unit 503 when decoding the WLD and the decoding method used by the SWLD decoding unit 504 when decoding the SWLD can be different. For example, in the decoding method for the SWLD, inter-frame prediction in intra-frame prediction and inter-frame prediction can be prioritized compared to the decoding method for the WLD.

[0369] Furthermore, in the decoding method for the SWLD and the decoding method for the WLD, the representation method of three-dimensional positions can be different. For example, in the SWLD, the three-dimensional position of the FVXL can be represented by three-dimensional coordinates, and in the WLD, the three-dimensional position can be represented by an octree described later, and vice versa.

[0370] Next, the octree representation as a method of representing three-dimensional positions will be described. The VXL data included in the three-dimensional data is converted into an octree structure and then encoded. Fig. 20 An example of the VXL of the WLD is shown. Fig.21 Shows Fig. 20 The octree structure of the WLD shown. In Fig. 20 the example shown, there are three VXLs 1 to 3 as VXLs (hereinafter, valid VXLs) including a point group. As Fig.21 shown, the octree structure is composed of nodes and leaf nodes. Each node has a maximum of 8 nodes or leaf nodes. Each leaf node has VXL information. Here, Fig.21 among the leaf nodes shown, leaf nodes 1, 2, and 3 represent Fig. 20 the VXLs 1, 2, and 3 shown, respectively.

[0371] Specifically, each node and leaf node correspond to a three-dimensional position. Node 1 corresponds to Fig. 20 all the blocks shown. The block corresponding to Node 1 is divided into 8 blocks, and among the 8 blocks, the blocks including valid VXLs are set as nodes, and the other blocks are set as leaf nodes. The block corresponding to the node is further divided into 8 nodes or leaf nodes, and the number of times this process is repeated is the same as the number of levels in the tree structure. And all the blocks in the lowest layer are set as leaf nodes.

[0372] Moreover, Fig. 22 shows an example of the SWLD generated from the Fig. 20 WLD shown. Fig. 20 The results of the feature quantity extraction of the VXL1 and VXL2 shown are judged as FVXL1 and FVXL2 and added to the SWLD. In addition, since VXL3 is not judged as FVXL, it is not included in the SWLD. Fig.23 shows Fig. 22 the octree structure of the SWLD shown. In Fig.23 the octree structure shown, Fig.21 the leaf node 3 corresponding to VXL3 shown is deleted. Accordingly, Fig.21 the node 3 shown has no valid VXL and is changed to a leaf node. In this way, generally speaking, the number of leaf nodes of the SWLD is smaller than that of the WLD, and the encoded three-dimensional data of the SWLD is also smaller than the encoded three-dimensional data of the WLD.

[0373] The following describes a modified example of the present embodiment.

[0374] For example, it may also be the case where, when a client such as an in-vehicle device estimates its own position, it receives the SWLD from the server, uses the SWLD for its own position estimation, and performs obstacle detection. In this case, various methods such as a distance sensor such as a rangefinder, a stereo camera, or a combination of multiple monocular cameras are used to perform obstacle detection based on the three-dimensional information of the surrounding area obtained by itself.

[0375] And, generally speaking, it is difficult to include VXL data of a flat area in the SWLD. For this reason, the server holds a downsampled world space (SubWLD) obtained by downsampling the WLD for the detection of stationary obstacles, and can send the SWLD and the SubWLD to the client. Accordingly, both the network bandwidth can be suppressed and the client side can perform its own position estimation and obstacle detection.

[0376] And, when the client quickly depicts three-dimensional map data, it may be convenient if the map information is in a grid structure. Then, the server can generate a grid based on the WLD and hold it in advance as a grid world space (MWLD). For example, when the client needs to perform rough three-dimensional depiction, it receives the MWLD, and when it needs to perform detailed three-dimensional depiction, it receives the WLD. Accordingly, the network bandwidth can be suppressed.

[0377] Also, although the server sets the VXLs with feature amounts above the threshold as FVXLs from each VXL, the FVXLs can also be calculated by different methods. For example, if the server determines that VXLs, VLMs, SPCs, or GOSs that make up signals or intersections are required for its own position estimation, driving assistance, or autonomous driving, etc., they can be included in the SWLD as FVXLs, FVLMs, FSPCs, or FGOSs. Also, the above determination can be made manually. In addition, the FVXLs obtained by the above method can be added to the FVXLs, etc. set based on the feature amount. That is, the SWLD extraction unit 403 can further extract, from the input three-dimensional data 411, the data corresponding to the object with a predetermined attribute as the extracted three-dimensional data 412.

[0378] Also, different labels from the feature amounts can be assigned to the situations required for these uses. The server can separately hold the FVXLs required for its own position estimation, driving assistance, or autonomous driving, such as signals or intersections, as the upper layer of the SWLD (for example, the lane world space).

[0379] Also, the server can attach attributes to the VXLs in the WLD according to a random access unit or a specified unit. The attributes include, for example, information indicating whether it is required or not required for its own position estimation, or information indicating whether it is important as traffic information such as a signal or an intersection. The attributes can also include the correspondence relationship with Features (intersections or roads, etc.) in the lane information (GDF: Geographic Data Files, etc.).

[0380] Also, as a method for updating the WLD or SWLD, the following method can be adopted.

[0381] Update information indicating changes in people, construction, or street trees (facing the trajectory), etc. is loaded into the server as a point cloud or metadata. The server updates the WLD based on this loading, and then uses the updated WLD to update the SWLD.

[0382] Also, when the client detects a mismatch between the three-dimensional information generated by itself during its own position estimation and the three-dimensional information received from the server, the three-dimensional information generated by itself can be sent to the server together with an update notification. In this case, the server updates the SWLD using the WLD. If the SWLD is not updated, the server determines that the WLD itself is old.

[0383] Also, as the header information of the encoded stream, although information for distinguishing between WLD and SWLD is attached, for example, in a case where there are multiple world spaces such as a grid world space or a lane world space, information for distinguishing them can be attached to the header information. Also, in a case where there are multiple SWLDs with different feature amounts, information for distinguishing them separately can also be attached to the header information.

[0384] Also, although SWLD is composed of FVXLs, it can also include VXLs that are not determined to be FVXLs. For example, SWLD can include adjacent VXLs used when calculating the feature amounts of FVXLs. Accordingly, even in a case where no feature amount information is attached to each FVXL of SWLD, the client can calculate the feature amounts of FVXLs when receiving SWLD. Also, at this time, SWLD can include information for distinguishing whether each VXL is an FVXL or a VXL.

[0385] As described above, the three-dimensional data encoding device 400 extracts the extracted three-dimensional data 412 (second three-dimensional data) whose feature amount is equal to or greater than the threshold from the input three-dimensional data 411 (first three-dimensional data), and generates the encoded three-dimensional data 414 (first encoded three-dimensional data) by encoding the extracted three-dimensional data 412.

[0386] Accordingly, the three-dimensional data encoding device 400 generates the encoded three-dimensional data 414 obtained by encoding the data whose feature amount is equal to or greater than the threshold. In this way, compared with the case of directly encoding the input three-dimensional data 411, the data amount can be reduced. Therefore, the three-dimensional data encoding device 400 can reduce the data amount during transmission.

[0387] Also, the three-dimensional data encoding device 400 further generates the encoded three-dimensional data 413 (second encoded three-dimensional data) by encoding the input three-dimensional data 411.

[0388] Accordingly, the three-dimensional data encoding device 400 can selectively transmit the encoded three-dimensional data 413 and the encoded three-dimensional data 414 according to the usage purpose or the like.

[0389] Also, the extracted three-dimensional data 412 is encoded by the first encoding method, and the input three-dimensional data 411 is encoded by the second encoding method different from the first encoding method.

[0390] Accordingly, the three-dimensional data encoding device 400 can adopt appropriate encoding methods for the input three-dimensional data 411 and the extracted three-dimensional data 412 respectively.

[0391] Also, in the first encoding method, among intra prediction and inter prediction, inter prediction is prioritized compared with the second encoding method.

[0392] Accordingly, the three-dimensional data encoding device 400 can increase the priority of inter-frame prediction for the extracted three-dimensional data 412 where the correlation between adjacent data is likely to be low.

[0393] Moreover, in the first encoding method and the second encoding method, the representation methods of three-dimensional positions are different. For example, in the second encoding method, an octree is used to represent three-dimensional positions, and in the first encoding method, three-dimensional coordinates are used to represent three-dimensional positions.

[0394] Accordingly, the three-dimensional data encoding device 400 can adopt a more appropriate representation method of three-dimensional positions for three-dimensional data with different numbers of data (the number of VXL or FVXL).

[0395] Moreover, in at least one of the encoded three-dimensional data 413 and 414, there is an identifier indicating whether the encoded three-dimensional data is the encoded three-dimensional data obtained by encoding the input three-dimensional data 411 or the encoded three-dimensional data obtained by encoding a part of the input three-dimensional data 411. That is, this identifier indicates whether the encoded three-dimensional data is the encoded three-dimensional data 413 of WLD or the encoded three-dimensional data 414 of SWLD.

[0396] Accordingly, the decoding device can easily determine whether the acquired encoded three-dimensional data is the encoded three-dimensional data 413 or the encoded three-dimensional data 414.

[0397] Moreover, the three-dimensional data encoding device 400 encodes the extracted three-dimensional data 412 such that the data volume of the encoded three-dimensional data 414 is smaller than the data volume of the encoded three-dimensional data 413.

[0398] Accordingly, the three-dimensional data encoding device 400 can make the data volume of the encoded three-dimensional data 414 smaller than the data volume of the encoded three-dimensional data 413.

[0399] Moreover, the three-dimensional data encoding device 400 further extracts, as the extracted three-dimensional data 412, data corresponding to an object having a predetermined attribute from the input three-dimensional data 411. For example, an object having a predetermined attribute refers to an object required in self-position estimation, driving assistance, or autonomous driving, etc., such as a signal or an intersection.

[0400] Accordingly, the three-dimensional data encoding device 400 can generate the encoded three-dimensional data 414 including the data required by the decoding device.

[0401] Moreover, the three-dimensional data encoding device 400 (server) further sends one of the encoded three-dimensional data 413 and 414 to the client according to the state of the client.

[0402] Accordingly, the three-dimensional data encoding device 400 can send appropriate data according to the state of the client.

[0403] Moreover, the state of the client includes the communication status of the client (such as network bandwidth) or the moving speed of the client.

[0404] Furthermore, the three-dimensional data encoding device 400 further sends one of the encoded three-dimensional data 413 and 414 to the client according to the request of the client.

[0405] Accordingly, the three-dimensional data encoding device 400 can send appropriate data according to the request of the client.

[0406] In addition, the three-dimensional data decoding device 500 according to the present embodiment decodes the encoded three-dimensional data 413 or 414 generated by the above three-dimensional data encoding device 400.

[0407] That is, the three-dimensional data decoding device 500 decodes the encoded three-dimensional data 414 obtained by encoding the extracted three-dimensional data 412 whose feature amount extracted from the input three-dimensional data 411 is above the threshold value by the first decoding method. And the three-dimensional data decoding device 500 decodes the encoded three-dimensional data 413 obtained by encoding the input three-dimensional data 411 by using a second decoding method different from the first decoding method.

[0408] Accordingly, the three-dimensional data decoding device 500 can selectively receive, for example, according to the usage purpose, etc., the encoded three-dimensional data 414 and the encoded three-dimensional data 413 obtained by encoding the data whose feature amount is above the threshold value. Accordingly, the three-dimensional data decoding device 500 can reduce the amount of data during transmission. Moreover, the three-dimensional data decoding device 500 can adopt appropriate decoding methods for the input three-dimensional data 411 and the extracted three-dimensional data 412 respectively.

[0409] Moreover, in the first decoding method, among intra prediction and inter prediction, inter prediction is prioritized compared with the second decoding method.

[0410] Accordingly, the three-dimensional data decoding device 500 can increase the priority of inter prediction for the extracted three-dimensional data whose correlation between adjacent data is likely to become low.

[0411] In addition, in the first decoding method and the second decoding method, the representation methods of three-dimensional positions are different. For example, in the second decoding method, the three-dimensional position is represented by an octree, and in the first decoding method, the three-dimensional position is represented by three-dimensional coordinates.

[0412] Accordingly, the three-dimensional data decoding device 500 can adopt a more appropriate representation method of three-dimensional positions for three-dimensional data with different numbers of data (the number of VXL or FVXL).

[0413] Further, at least one of the encoded three-dimensional data 413 and 414 includes an identifier that indicates whether the encoded three-dimensional data is obtained by encoding the input three-dimensional data 411 or by encoding a part of the input three-dimensional data 411. The three-dimensional data decoding device 500 identifies the encoded three-dimensional data 413 and 414 with reference to this identifier.

[0414] Accordingly, the three-dimensional data decoding device 500 can easily determine whether the obtained encoded three-dimensional data is the encoded three-dimensional data 413 or the encoded three-dimensional data 414.

[0415] Further, the three-dimensional data decoding device 500 further notifies the server of the state of the client (the three-dimensional data decoding device 500). The three-dimensional data decoding device 500 receives one of the encoded three-dimensional data 413 and 414 sent from the server according to the state of the client.

[0416] Accordingly, the three-dimensional data decoding device 500 can receive appropriate data according to the state of the client.

[0417] The state of the client includes the communication status of the client (e.g., network bandwidth) or the moving speed of the client.

[0418] Further, the three-dimensional data decoding device 500 further requests one of the encoded three-dimensional data 413 and 414 from the server and receives one of the encoded three-dimensional data 413 and 414 sent from the server according to this request.

[0419] Accordingly, the three-dimensional data decoding device 500 can receive appropriate data corresponding to the use.

[0420] (Embodiment 3)

[0421] In this embodiment, a method for transmitting and receiving three-dimensional data between vehicles will be described. For example, three-dimensional data is transmitted and received between the host vehicle and surrounding vehicles.

[0422] Fig.24 It is a block diagram of a three-dimensional data production device 620 according to this embodiment. The three-dimensional data production device 620 is included in the host vehicle, for example, and produces denser third three-dimensional data 636 by synthesizing the received second three-dimensional data 635 and the first three-dimensional data 632 produced by the three-dimensional data production device 620.

[0423] The three-dimensional data production device 620 includes: a three-dimensional data production unit 621, a request range determination unit 622, a search unit 623, a reception unit 624, a decoding unit 625, and a synthesis unit 626.

[0424] First, the 3D data creation unit 621 creates the first 3D data 632 using the sensor information 631 detected by the sensors equipped on its own vehicle. Next, the request range determination unit 622 determines the request range, which refers to the 3D space range where the data in the created first 3D data 632 is insufficient.

[0425] Next, the search unit 623 searches for surrounding vehicles that hold the 3D data within the request range, and sends the request range information 633 indicating the request range to the surrounding vehicles identified through the search. Next, the receiving unit 624 receives the encoded 3D data 634 (S624), which is the encoded stream of the request range, from the surrounding vehicles. Additionally, the search unit 623 can send requests to all vehicles within the determined range without discrimination, and receive the encoded 3D data 634 from the responding counterparts. Also, the search unit 623 is not limited to vehicles, and can also send requests to objects such as traffic lights or signs, and receive the encoded 3D data 634 from such objects.

[0426] Next, the decoding unit 625 decodes the received encoded 3D data 634 to obtain the second 3D data 635. Next, the synthesizing unit 626 synthesizes the first 3D data 632 and the second 3D data 635 to create the denser third 3D data 636.

[0427] Next, the configuration and operation of the 3D data transmission device 640 according to the present embodiment will be described. Fig.25 It is a block diagram of the 3D data transmission device 640.

[0428] The 3D data transmission device 640 is included, for example, among the above-mentioned surrounding vehicles, processes the fifth 3D data 652 created by the surrounding vehicles into the sixth 3D data 654 requested by its own vehicle, generates the encoded 3D data 634 by encoding the sixth 3D data 654, and sends the encoded 3D data 634 to its own vehicle.

[0429] The 3D data transmission device 640 includes: a 3D data creation unit 641, a receiving unit 642, an extraction unit 643, an encoding unit 644, and a sending unit 645.

[0430] First, the 3D data creation unit 641 creates the fifth 3D data 652 using the sensor information 651 detected by the sensors equipped on the surrounding vehicles. Next, the receiving unit 642 receives the request range information 633 sent from its own vehicle.

[0431] Next, the extraction unit 643 extracts the three-dimensional data of the requested range represented by the request range information 633 from the fifth three-dimensional data 652, and processes the fifth three-dimensional data 652 into the sixth three-dimensional data 654. Next, the encoding unit 644 encodes the sixth three-dimensional data 654 to generate the encoded three-dimensional data 634 as an encoded stream. Then, the transmission unit 645 transmits the encoded three-dimensional data 634 to its own vehicle.

[0432] In addition, here, although an example in which the own vehicle is equipped with the three-dimensional data production device 620 and the surrounding vehicles are equipped with the three-dimensional data transmission device 640 has been described, each vehicle may also have the functions of the three-dimensional data production device 620 and the three-dimensional data transmission device 640.

[0433] (Embodiment 4)

[0434] In the present embodiment, the operation related to the abnormal situation in the own position estimation based on the three-dimensional map will be described.

[0435] The use of autonomous movement of moving bodies such as the autonomous driving of motor vehicles, robots, or flying objects such as drones will expand in the future. As an example of a method for realizing such autonomous movement, there is a method in which the moving body estimates its own position in the three-dimensional map (own position estimation) and travels according to the map.

[0436] The own position estimation is realized by matching the three-dimensional map with the three-dimensional information around the own vehicle (hereinafter referred to as the own vehicle detection three-dimensional data) obtained by sensors such as a distance measuring instrument (LIDAR, etc.) or a stereo camera mounted on the own vehicle, and estimating the position of the own vehicle in the three-dimensional map.

[0437] As shown in the HD map proposed by HERE company, the three-dimensional map can include not only three-dimensional point clouds, but also two-dimensional map data such as the shapes of roads and intersections, or information that changes in real time such as traffic jams and accidents. The three-dimensional map is composed of multiple levels such as three-dimensional data, two-dimensional data, and metadata that changes in real time. The device can obtain only the required data, or can also refer to the required data.

[0438] The data of the point cloud can be the above-mentioned SWLD, or can also include point group data that is not feature points. And the transmission and reception of the data of the point cloud are basically performed in one or more random access units.

[0439] As a method for matching a three-dimensional map with three-dimensional data detected by the vehicle itself, the following method can be adopted. For example, the device compares the shapes of point groups in the point clouds of each other, and determines the part with a high similarity between feature points as the same position. Moreover, when the three-dimensional map is composed of SWLD, the device compares the feature points constituting the SWLD with the three-dimensional feature points extracted from the three-dimensional data detected by the vehicle itself and performs matching.

[0440] Here, in order to estimate the vehicle's own position with high accuracy, the following (A) and (B) need to be satisfied. (A) The three-dimensional map and the three-dimensional data detected by the vehicle itself can already be obtained. (B) Their accuracy meets a predetermined standard. However, in the following abnormal situations, (A) or (B) cannot be satisfied.

[0441] (1) The three-dimensional map cannot be obtained through the communication path.

[0442] (2) There is no three-dimensional map, or the obtained three-dimensional map is damaged.

[0443] (3) The sensor of the vehicle itself fails, or due to bad weather, the generation accuracy of the three-dimensional data detected by the vehicle itself is insufficient.

[0444] The operations for coping with these abnormal situations will be described below. Although the operations are described below taking a vehicle as an example, the following methods can also be applied to all moving objects such as robots or drones that perform autonomous movement.

[0445] What will be described below is the configuration and operation of the three-dimensional information processing device according to the present embodiment for coping with abnormal situations in the three-dimensional map or the three-dimensional data detected by the vehicle itself. Fig.26 It is a block diagram showing a configuration example of the three-dimensional information processing device 700 according to the present embodiment.

[0446] The three-dimensional information processing device 700 is mounted on a moving object such as a motor vehicle, for example. As Fig.26 shown, the three-dimensional information processing device 700 includes: a three-dimensional map acquisition unit 701, a vehicle's own detection data acquisition unit 702, an abnormal situation determination unit 703, a coping operation determination unit 704, and an operation control unit 705.

[0447] In addition, the three-dimensional information processing device 700 may include a camera that acquires a two-dimensional image, or may include a two-dimensional or one-dimensional sensor (not shown) such as a sensor that uses ultrasonic waves or lasers for one-dimensional data for detecting structural objects or moving objects around the vehicle itself. Moreover, the three-dimensional information processing device 700 may include a communication unit (not shown) that is used to acquire a three-dimensional map through a mobile communication network such as 4G or 5G, or vehicle-to-vehicle communication, or road-to-vehicle communication.

[0448] The three-dimensional map acquisition unit 701 acquires a three-dimensional map 711 near the driving route. For example, the three-dimensional map acquisition unit 701 acquires the three-dimensional map 711 through a mobile communication network, vehicle-to-vehicle communication, or road-to-vehicle communication.

[0449] Next, the own vehicle detection data acquisition unit 702 acquires own vehicle detection three-dimensional data 712 based on sensor information. For example, the own vehicle detection data acquisition unit 702 generates own vehicle detection three-dimensional data 712 based on the sensor information obtained by the sensors provided in the own vehicle.

[0450] Next, the abnormal situation determination unit 703 detects an abnormal situation by performing a pre-determined check on at least one of the acquired three-dimensional map 711 and the own vehicle detection three-dimensional data 712. That is, the abnormal situation determination unit 703 determines whether at least one of the acquired three-dimensional map 711 and the own vehicle detection three-dimensional data 712 is abnormal.

[0451] When an abnormal situation is detected, the response work determination unit 704 determines the response work for the abnormal situation. Next, the work control unit 705 controls the work of each processing unit required in the implementation of the response work, such as the three-dimensional map acquisition unit 701.

[0452] In addition, when no abnormal situation is detected, the three-dimensional information processing device 700 ends the processing.

[0453] Furthermore, the three-dimensional information processing device 700 estimates the own position of the vehicle equipped with the three-dimensional information processing device 700 by using the three-dimensional map 711 and the own vehicle detection three-dimensional data 712. Next, the three-dimensional information processing device 700 uses the result of the own position estimation to make the vehicle perform autonomous driving.

[0454] Accordingly, the three-dimensional information processing device 700 acquires map data (three-dimensional map 711) including first three-dimensional position information via a channel. For example, the first three-dimensional position information is encoded in units of partial spaces having three-dimensional coordinate information, the first three-dimensional position information includes a plurality of random access units, each of the plurality of random access units is an aggregate of one or more partial spaces, and can be independently decoded. For example, the first three-dimensional position information is data (SWLD) in which feature points where three-dimensional feature amounts exceed a prescribed threshold are encoded.

[0455] Furthermore, the three-dimensional information processing device 700 generates second three-dimensional position information (own vehicle detection three-dimensional data 712) based on the information detected by the sensors. Next, the three-dimensional information processing device 700 determines whether the first three-dimensional position information or the second three-dimensional position information is abnormal by performing an abnormality determination process on the first three-dimensional position information or the second three-dimensional position information.

[0456] When the three-dimensional information processing device 700 determines that the first three-dimensional position information or the second three-dimensional position information is abnormal, it determines the response operation for the abnormality. Next, the three-dimensional information processing device 700 executes the control required for the implementation of the response operation.

[0457] Accordingly, the three-dimensional information processing device 700 can detect the abnormality of the first three-dimensional position information or the second three-dimensional position information and can perform the response operation.

[0458] (Embodiment 5)

[0459] In this embodiment, a method for transmitting three-dimensional data to a following vehicle and the like will be described.

[0460] Fig. 27 FIG. is a block diagram showing a configuration example of a three-dimensional data production device 810 according to this embodiment. The three-dimensional data production device 810 is mounted on a vehicle, for example. The three-dimensional data production device 810 transmits and receives three-dimensional data to and from external traffic cloud monitoring, a preceding vehicle, or a following vehicle, and at the same time produces and stores the three-dimensional data.

[0461] The three-dimensional data production device 810 includes: a data reception unit 811, a communication unit 812, a reception control unit 813, a format conversion unit 814, a plurality of sensors 815, a three-dimensional data production unit 816, a three-dimensional data synthesis unit 817, a three-dimensional data storage unit 818, a communication unit 819, a transmission control unit 820, a format conversion unit 821, and a data transmission unit 822.

[0462] The data reception unit 811 receives three-dimensional data 831 from traffic cloud monitoring or a preceding vehicle. The three-dimensional data 831 includes, for example, a point cloud containing information on an area that cannot be detected by the sensors 815 of the own vehicle, a visible light image, depth information, sensor position information, or speed information.

[0463] The communication unit 812 communicates with traffic cloud monitoring or a preceding vehicle and sends a data transmission request or the like to traffic cloud monitoring or a preceding vehicle.

[0464] The reception control unit 813 exchanges information such as corresponding formats with the communication partner via the communication unit 812 to establish communication with the communication partner.

[0465] The format conversion unit 814 generates three-dimensional data 832 by performing format conversion on the three-dimensional data 831 received by the data reception unit 811. Further, when the three-dimensional data 831 is compressed or encoded, the format conversion unit 814 performs decompression or decoding processing.

[0466] The plurality of sensors 815 are a group of sensors such as LiDAR, visible light cameras, or infrared cameras that obtain information about the outside of the vehicle, and generate sensor information 833. For example, when the sensor 815 is a laser sensor such as LiDAR, the sensor information 833 is three-dimensional data such as point clouds (point group data). Additionally, the sensor 815 may not be plural.

[0467] The three-dimensional data creation unit 816 generates three-dimensional data 834 based on the sensor information 833. The three-dimensional data 834 includes, for example, information such as point clouds, visible light images, depth information, sensor position information, or speed information.

[0468] The three-dimensional data synthesis unit 817 synthesizes the three-dimensional data 832 created by traffic cloud monitoring or a preceding vehicle, etc., into the three-dimensional data 834 created based on the sensor information 833 of its own vehicle, thereby enabling the construction of three-dimensional data 835 that includes the space in front of the preceding vehicle that cannot be detected by the sensors 815 of its own vehicle.

[0469] The three-dimensional data storage unit 818 stores the generated three-dimensional data 835, etc.

[0470] The communication unit 819 communicates with traffic cloud monitoring or a following vehicle, and sends a data transmission request, etc., to traffic cloud monitoring or a following vehicle.

[0471] The transmission control unit 820 exchanges information such as the corresponding format, etc., with the communication partner via the communication unit 819, and establishes communication with the communication partner. Further, the transmission control unit 820 determines the transmission area of the space of the three-dimensional data to be transmitted based on the three-dimensional data construction information of the three-dimensional data 832 generated in the three-dimensional data synthesis unit 817 and the data transmission request from the communication partner.

[0472] Specifically, the transmission control unit 820 determines the transmission area that includes the space in front of its own vehicle that cannot be detected by the sensors of the following vehicle in accordance with the data transmission request from traffic cloud monitoring or a following vehicle. Further, the transmission control unit 820 determines the transmission area by judging, based on the three-dimensional data construction information, whether there is an update to the space that can be transmitted or the space that has already been transmitted, etc. For example, the transmission control unit 820 determines as the transmission area the area that is both specified by the data transmission request and where the corresponding three-dimensional data 835 exists. Further, the transmission control unit 820 notifies the format conversion unit 821 of the format corresponding to the communication partner and the transmission area.

[0473] The format conversion unit 821 generates the three-dimensional data 837 by converting the three-dimensional data 836 of the transmission area in the three-dimensional data 835 stored in the three-dimensional data storage unit 818 into a format corresponding to the receiving side. Additionally, the format conversion unit 821 can also compress or encode the three-dimensional data 837 to reduce the data volume.

[0474] The data transmission unit 822 transmits the three-dimensional data 837 to the traffic cloud monitoring or the following vehicle. The three-dimensional data 837 includes, for example, the point cloud, visible light image, depth information, or sensor position information in front of the host vehicle that contains information on the area that is a blind spot for the following vehicle.

[0475] In addition, although the format conversion units 814 and 821 are taken as examples for format conversion and the like here, format conversion may not be performed.

[0476] With this configuration, the three-dimensional data production device 810 obtains the three-dimensional data 831 of the area that cannot be detected by the sensor 815 of the host vehicle from the outside, and generates the three-dimensional data 835 by synthesizing the three-dimensional data 831 and the three-dimensional data 834 based on the sensor information 833 detected by the sensor 815 of the host vehicle. Accordingly, the three-dimensional data production device 810 can generate the three-dimensional data of the range that cannot be detected by the sensor 815 of the host vehicle.

[0477] Moreover, the three-dimensional data production device 810 can transmit the three-dimensional data of the space in front of the host vehicle that cannot be detected by the sensors of the following vehicle to the traffic cloud monitoring or the following vehicle, etc., in accordance with the data transmission request from the traffic cloud monitoring or the following vehicle.

[0478] (Embodiment 6)

[0479] In the example to be described in Embodiment 5, the client device such as a vehicle transmits the three-dimensional data to other vehicles or servers such as the traffic cloud monitoring. In this embodiment, the client device transmits the sensor information obtained by the sensor to the server or other client devices.

[0480] First, the configuration of the system according to this embodiment will be described. Fig.28 The configuration of the three-dimensional map and the transceiver system of the sensor information according to this embodiment is shown. The system includes a server 901, client devices 902A and 902B. Additionally, when not specifically distinguishing between the client devices 902A and 902B, they are also denoted as the client device 902.

[0481] The client device 902 is, for example, an in-vehicle device mounted on a moving body such as a vehicle. The server 901 is, for example, a traffic cloud monitor or the like and can communicate with a plurality of client devices 902.

[0482] The server 901 sends a three-dimensional map composed of point clouds to the client device 902. In addition, the composition of the three-dimensional map is not limited to point clouds and can also be represented by other three-dimensional data such as a grid structure.

[0483] The client device 902 sends sensor information obtained by the client device 902 to the server 901. The sensor information includes, for example, at least one of LiDAR acquired information, visible light image, infrared image, depth image, sensor position information, and speed information.

[0484] Regarding the data transmitted and received between the server 901 and the client device 902, it can be compressed when reducing data is desired, and can be not compressed when maintaining the accuracy of the data is desired. When compressing the data, for example, an octree-based three-dimensional compression method can be adopted in the point cloud. And, a two-dimensional image compression method can be adopted in the visible light image, infrared image, and depth image. The two-dimensional image compression method is, for example, MPEG-4 AVC or HEVC standardized by MPEG.

[0485] And, the server 901 sends the three-dimensional map managed by the server 901 to the client device 902 in accordance with a transmission request for the three-dimensional map from the client device 902. In addition, the server 901 may send the three-dimensional map without waiting for a transmission request for the three-dimensional map from the client device 902. For example, the server 901 may broadcast the three-dimensional map to one or more client devices 902 in a pre-specified space. And, the server 901 may send a three-dimensional map suitable for the position of the client device 902 to the client device 902 that has received a transmission request once at regular intervals. And, the server 901 may send the three-dimensional map to the client device 902 whenever the three-dimensional map managed by the server 901 is updated.

[0486] The client device 902 issues a transmission request for the three-dimensional map to the server 901. For example, when the client device 902 wants to estimate its own position while driving, the client device 902 sends a transmission request for the three-dimensional map to the server 901.

[0487] In addition, in the following cases, the client device 902 may also send a request to the server 901 to send a 3D map. When the 3D map held by the client device 902 is relatively old, the client device 902 may also send a request to the server 901 to send a 3D map. For example, when a certain period of time has passed since the client device 902 obtained the 3D map, the client device 902 may also send a request to the server 901 to send a 3D map.

[0488] It may also be that, before a certain moment when the client device 902 is about to leave the space shown in the 3D map held by the client device 902, the client device 902 sends a request to the server 901 to send a 3D map. For example, it may also be that when the client device 902 is within a pre-specified distance from the boundary of the space shown in the 3D map held by the client device 902, the client device 902 sends a request to the server 901 to send a 3D map. Also, when the movement path and movement speed of the client device 902 are known, the moment when the client device 902 leaves the space shown in the 3D map held by the client device 902 can be predicted based on the known movement path and movement speed.

[0489] When the error in the position comparison between the 3D data generated by the client device 902 based on sensor information and the 3D map is above a certain range, the client device 902 may send a request to the server 901 to send a 3D map.

[0490] The client device 902 sends the sensor information to the server 901 in accordance with the request to send the sensor information sent from the server 901. In addition, the client device 902 may also send the sensor information to the server 901 without waiting for the request to send the sensor information from the server 901. For example, when the client device 902 has received a request to send the sensor information from the server 901 once, it may regularly send the sensor information to the server 901 within a certain period. It may also be that when the error in the position comparison between the 3D data generated by the client device 902 based on sensor information and the 3D map obtained from the server 901 is above a certain range, the client device 902 determines that there is a possibility that the 3D map around the client device 902 has changed, and sends this judgment result together with the sensor information to the server 901.

[0491] The server 901 sends a request to the client device 902 to send sensor information. For example, the server 901 receives the location information of the client device 902 such as GPS from the client device 902. When the server 901 determines, based on the location information of the client device 902, that the client device 902 is approaching a space with less information in the three-dimensional map managed by the server 901, in order to regenerate the three-dimensional map, the server 901 sends a request to the client device 902 to send sensor information. Also, the server 901 may send a request to send sensor information when it wants to update the three-dimensional map, when it wants to confirm road conditions such as during snow accumulation or disasters, or when it wants to confirm congestion conditions or accident conditions.

[0492] Also, the client device 902 may set the amount of sensor information to be sent to the server 901 according to the communication state or frequency band at the time of receiving the request to send sensor information from the server 901. Setting the amount of sensor information to be sent to the server 901, for example, means increasing or decreasing the data itself, or selecting an appropriate compression method.

[0493] Fig.29 It is a block diagram showing a configuration example of the client device 902. The client device 902 receives a three-dimensional map composed of point clouds or the like from the server 901, and estimates its own position based on the three-dimensional data created from the sensor information of the client device 902. Then, the client device 902 sends the acquired sensor information to the server 901.

[0494] The client device 902 includes: a data receiving unit 1011, a communication unit 1012, a reception control unit 1013, a format conversion unit 1014, a plurality of sensors 1015, a three-dimensional data creation unit 1016, a three-dimensional image processing unit 1017, a three-dimensional data storage unit 1018, a format conversion unit 1019, a communication unit 1020, a transmission control unit 1021, and a data transmission unit 1022.

[0495] The data receiving unit 1011 receives the three-dimensional map 1031 from the server 901. The three-dimensional map 1031 is data including point clouds such as WLD or SWLD. The three-dimensional map 1031 may include either compressed data or uncompressed data.

[0496] The communication unit 1012 communicates with the server 901 and sends a data transmission request (for example, a request to send a three-dimensional map) or the like to the server 901.

[0497] The reception control unit 1013 exchanges information such as corresponding formats with the communication partner via the communication unit 1012 to establish communication with the communication partner.

[0498] The format conversion unit 1014 generates a 3D map 1032 by performing format conversion and the like on the 3D map 1031 received by the data reception unit 1011. Further, when the 3D map 1031 is compressed or encoded, the format conversion unit 1014 performs decompression or decoding processing. In addition, when the 3D map 1031 is uncompressed data, the format conversion unit 1014 does not perform decompression or decoding processing.

[0499] The plurality of sensors 1015 are a group of sensors mounted on the client device 902 such as a LiDAR, a visible light camera, an infrared camera, or a depth sensor, which are used to obtain information on the outside of the vehicle, and generate sensor information 1033. For example, when the sensor 1015 is a laser sensor such as a LiDAR, the sensor information 1033 is 3D data such as a point cloud (point group data). In addition, the sensor 1015 may not be plural.

[0500] The 3D data production unit 1016 produces 3D data 1034 around its own vehicle based on the sensor information 1033. For example, the 3D data production unit 1016 uses the information obtained by the LiDAR and the visible light image obtained by the visible light camera to produce point cloud data with color information around its own vehicle.

[0501] The 3D image processing unit 1017 performs its own position estimation processing and the like of its own vehicle by using the received 3D map 1032 such as a point cloud and the 3D data 1034 around its own vehicle generated based on the sensor information 1033. Alternatively, the 3D image processing unit 1017 may synthesize the 3D map 1032 and the 3D data 1034 to produce 3D data 1035 around its own vehicle, and use the produced 3D data 1035 to perform its own position estimation processing.

[0502] The 3D data storage unit 1018 stores the 3D map 1032, the 3D data 1034, the 3D data 1035, and the like.

[0503] The format conversion unit 1019 generates sensor information 1037 by converting the sensor information 1033 into a format corresponding to the receiving side. In addition, the format conversion unit 1019 may reduce the data amount by compressing or encoding the sensor information 1037. Further, when format conversion is not required, the format conversion unit 1019 may omit the processing. Moreover, the format conversion unit 1019 may control the data amount of the data transmitted according to the specified transmission range.

[0504] The communication unit 1020 communicates with the server 901 and receives a data transmission request (a transmission request for sensor information) and the like from the server 901.

[0505] The transmission control unit 1021 exchanges information such as corresponding formats with the communication partner via the communication unit 1020 to establish communication.

[0506] The data transmission unit 1022 transmits the sensor information 1037 to the server 901. The sensor information 1037 includes, for example, information obtained by LiDAR, luminance images (visible light images) obtained by visible light cameras, infrared images obtained by infrared cameras, depth images obtained by depth sensors, sensor position information, speed information, and other information obtained by a plurality of sensors 1015.

[0507] Next, the configuration of the server 901 will be described. Fig.30 It is a block diagram showing a configuration example of the server 901. The server 901 receives the sensor information sent from the client device 902, and creates three-dimensional data based on the received sensor information. The server 901 updates the three-dimensional map managed by the server 901 using the created three-dimensional data. Further, the server 901 sends the updated three-dimensional map to the client device 902 in accordance with a request for sending the three-dimensional map from the client device 902.

[0508] The server 901 includes: a data reception unit 1111, a communication unit 1112, a reception control unit 1113, a format conversion unit 1114, a three-dimensional data creation unit 1116, a three-dimensional data synthesis unit 1117, a three-dimensional data storage unit 1118, a format conversion unit 1119, a communication unit 1120, a transmission control unit 1121, and a data transmission unit 1122.

[0509] The data reception unit 1111 receives the sensor information 1037 from the client device 902. The sensor information 1037 includes, for example, information obtained by LiDAR, luminance images (visible light images) obtained by visible light cameras, infrared images obtained by infrared cameras, depth images obtained by depth sensors, sensor position information, and speed information.

[0510] The communication unit 1112 communicates with the client device 902 and sends a data transmission request (for example, a request for sending sensor information) to the client device 902.

[0511] The reception control unit 1113 exchanges information such as corresponding formats with the communication partner via the communication unit 1112 to establish communication.

[0512] When the received sensor information 1037 is compressed or encoded, the format conversion unit 1114 generates the sensor information 1132 by performing decompression or decoding processing. Additionally, when the sensor information 1037 is uncompressed data, the format conversion unit 1114 does not perform decompression or decoding processing.

[0513] Based on the sensor information 1132, the three-dimensional data creation unit 1116 creates three-dimensional data 1134 of the surroundings of the client device 902. For example, the three-dimensional data creation unit 1116 uses the information obtained by LiDAR and the visible light images obtained by the visible light camera to create point cloud data with color information of the surroundings of the client device 902.

[0514] The three-dimensional data synthesis unit 1117 synthesizes the three-dimensional data 1134 created based on the sensor information 1132 with the three-dimensional map 1135 managed by the server 901, and thereby updates the three-dimensional map 1135.

[0515] The three-dimensional data storage unit 1118 stores the three-dimensional map 1135 and the like.

[0516] The format conversion unit 1119 generates the three-dimensional map 1031 by converting the three-dimensional map 1135 into a format corresponding to the receiving side. Additionally, the format conversion unit 1119 can also reduce the data volume by compressing or encoding the three-dimensional map 1135. And when format conversion is not required, the format conversion unit 1119 can also omit the processing. And the format conversion unit 1119 can control the data volume to be sent according to the specified sending range.

[0517] The communication unit 1120 communicates with the client device 902 and receives a data sending request (a sending request for the three-dimensional map) and the like from the client device 902.

[0518] The transmission control unit 1121 exchanges information such as the corresponding format with the communication partner via the communication unit 1120, thereby establishing communication.

[0519] The data transmission unit 1122 sends the three-dimensional map 1031 to the client device 902. The three-dimensional map 1031 is data including point clouds such as WLD or SWLD. Either compressed data or uncompressed data can also be included in the three-dimensional map 1031.

[0520] Next, the operation flow of the client device 902 will be described. Fig.31 It is a flowchart showing the operations when the client device 902 obtains the three-dimensional map.

[0521] First, the client device 902 requests the server 901 to send a three-dimensional map (such as a point cloud) (S1001). At this time, the client device 902 also sends the position information of the client device 902 obtained through GPS or the like. Accordingly, the client device 902 can request the server 901 to send a three-dimensional map related to the position information.

[0522] Next, the client device 902 receives the three-dimensional map from the server 901 (S1002). If the received three-dimensional map is compressed data, the client device 902 decodes the received three-dimensional map to generate an uncompressed three-dimensional map (S1003).

[0523] Next, the client device 902 creates three-dimensional data 1034 of the surroundings of the client device 902 based on the sensor information 1033 obtained from multiple sensors 1015 (S1004). Next, the client device 902 estimates its own position of the client device 902 by using the three-dimensional map 1032 received from the server 901 and the three-dimensional data 1034 created based on the sensor information 1033 (S1005).

[0524] Fig.32 This is a flowchart showing the operation when the client device 902 sends sensor information. First, the client device 902 receives a request to send sensor information from the server 901 (S1011). The client device 902 that has received the send request sends the sensor information 1037 to the server 901 (S1012). In addition, when the sensor information 1033 includes multiple pieces of information obtained through multiple sensors 1015, the client device 902 compresses each piece of information in a compression method suitable for each piece of information, thereby generating the sensor information 1037.

[0525] Next, the operation process of the server 901 will be described. Fig.33 This is a flowchart showing the operation when the server 901 obtains sensor information. First, the server 901 requests the client device 902 to send sensor information (S1021). Next, the server 901 receives the sensor information 1037 sent from the client device 902 in accordance with this request (S1022). Next, the server 901 creates three-dimensional data 1134 by using the received sensor information 1037 (S1023). Next, the server 901 reflects the created three-dimensional data 1134 in the three-dimensional map 1135 (S1024).

[0526] Fig.34It is a flowchart showing the operations when the server 901 sends a 3D map. First, the server 901 receives a 3D map sending request from the client device 902 (S1031). The server 901 that has received the 3D map sending request sends the 3D map 1031 to the client device 902 (S1032). At this time, the server 901 can extract the 3D map in its vicinity corresponding to the location information of the client device 902 and send the extracted 3D map. And it can be that the server 901 compresses the 3D map composed of point clouds, for example, by using a compression method such as an octree, and sends the compressed 3D map.

[0527] Hereinafter, a modified example of the present embodiment will be described.

[0528] The server 901 uses the sensor information 1037 received from the client device 902 to create 3D data 1134 near the location of the client device 902. Next, the server 901 matches the created 3D data 1134 with the 3D map 1135 of the same area managed by the server 901 and calculates the difference between the 3D data 1134 and the 3D map 1135. When the difference is equal to or greater than a predetermined threshold, the server 901 determines that some abnormality has occurred around the client device 902. For example, when there is a surface subsidence or the like due to a natural disaster such as an earthquake, it can be considered that a large difference will occur between the 3D map 1135 managed by the server 901 and the 3D data 1134 created based on the sensor information 1037.

[0529] The sensor information 1037 may also include at least one of the type of the sensor, the performance of the sensor, and the model of the sensor. Also, it may be that a class ID corresponding to the performance of the sensor is attached to the sensor information 1037. For example, when the sensor information 1037 is information obtained by LiDAR, it is possible to consider allocating identifiers according to the performance of the sensor. For example, class 1 is allocated to a sensor capable of obtaining information with an accuracy of several millimeters, class 2 is allocated to a sensor capable of obtaining information with an accuracy of several centimeters, and class 3 is allocated to a sensor capable of obtaining information with an accuracy of several meters. Also, the server 901 may estimate the performance information of the sensor from the model of the client device 902. For example, when the client device 902 is mounted on a vehicle, the server 901 may determine the specification information of the sensor based on the model of the vehicle. In this case, the server 901 may obtain the information of the model of the vehicle in advance or include this information in the sensor information. Also, it may be that the server 901 uses the obtained sensor information 1037 to switch the degree of correction for the three-dimensional data 1134 created using the sensor information 1037. For example, when the sensor performance is high accuracy (class 1), the server 901 does not perform correction on the three-dimensional data 1134. When the sensor performance is low accuracy (class 3), the server 901 applies correction suitable for the accuracy of the sensor to the three-dimensional data 1134. For example, the server 901 increases the degree (intensity) of correction as the accuracy of the sensor becomes lower.

[0530] The server 901 may also send a request to send sensor information to multiple client devices 902 existing in a certain space at the same time. When the server 901 receives multiple sensor information from the multiple client devices 902, it is not necessary to use all the sensor information for creating the three-dimensional data 1134. For example, the sensor information to be used can be selected according to the performance of the sensor. For example, when updating the three-dimensional map 1135, the server 901 may select high-accuracy sensor information (class 1) from the received multiple sensor information and use the selected sensor information to create the three-dimensional data 1134.

[0531] The server 901 is not limited to servers such as traffic cloud monitoring, and may also be other client devices (in-vehicle). Fig.35 The system configuration in this case is shown.

[0532] For example, the client device 902C sends a request to the nearby client device 902A to send sensor information, and obtains the sensor information from the client device 902A. Then, the client device 902C uses the obtained sensor information of the client device 902A to create three-dimensional data and updates the three-dimensional map of the client device 902C. In this way, the client device 902C can utilize the performance of the client device 902C to generate a three-dimensional map of the space that can be obtained from the client device 902A. For example, this may occur when the performance of the client device 902C is high.

[0533] Moreover, in this case, the client device 902A that provided the sensor information is given the right to obtain the highly accurate three-dimensional map generated by the client device 902C. The client device 902A receives the highly accurate three-dimensional map from the client device 902C according to this right.

[0534] Alternatively, the client device 902C may send a request to send sensor information to a plurality of nearby client devices 902 (client device 902A and client device 902B). When the sensor of the client device 902A or the client device 902B has high performance, the client device 902C can use the sensor information obtained through this high-performance sensor to create three-dimensional data.

[0535] Fig.36 It is a block diagram showing the functional configurations of the server 901 and the client device 902. The server 901 includes, for example: a three-dimensional map compression / decoding processing unit 1201 that compresses and decodes a three-dimensional map, and a sensor information compression / decoding processing unit 1202 that compresses and decodes sensor information.

[0536] The client device 902 includes: a three-dimensional map decoding processing unit 1211 and a sensor information compression processing unit 1212. The three-dimensional map decoding processing unit 1211 receives the encoded data of the compressed three-dimensional map, decodes the encoded data, and obtains the three-dimensional map. The sensor information compression processing unit 1212 does not compress the three-dimensional data created from the obtained sensor information, but compresses the sensor information itself, and sends the encoded data of the compressed sensor information to the server 901. According to this configuration, the client device 902 can keep the processing unit (device or LSI) for decoding the three-dimensional map (point cloud, etc.) inside, without having to keep the processing unit for compressing the three-dimensional data of the three-dimensional map (point cloud, etc.) inside. In this way, the cost and power consumption of the client device 902 can be suppressed.

[0537] As described above, the client device 902 according to the present embodiment is mounted on a moving body, and creates three-dimensional data 1034 of the periphery of the moving body based on sensor information 1033 indicating the peripheral situation of the moving body obtained by a sensor 1015 mounted on the moving body. The client device 902 estimates the own position of the moving body using the created three-dimensional data 1034. The client device 902 transmits the obtained sensor information 1033 to the server 901 or other moving bodies 902.

[0538] Accordingly, the client device 902 transmits the sensor information 1033 to the server 901 or the like. In this way, there is a possibility that the amount of data to be transmitted can be reduced compared with the case of transmitting three-dimensional data. And since it is not necessary to perform processes such as compression or encoding of three-dimensional data on the client device 902, the amount of processing of the client device 902 can be reduced. Therefore, the client device 902 can achieve a reduction in the amount of transmitted data or a simplification of the device configuration.

[0539] In addition, the client device 902 further sends a transmission request for a three-dimensional map to the server 901, and receives a three-dimensional map 1031 from the server 901. The client device 902 estimates its own position using the three-dimensional data 1034 and the three-dimensional map 1032 in the estimation of its own position.

[0540] Moreover, the sensor information 1033 includes at least one of information obtained by a laser sensor, a luminance image (visible light image), an infrared image, a depth image, position information of the sensor, and speed information of the sensor.

[0541] And the sensor information 1033 includes information indicating the performance of the sensor.

[0542] In addition, the client device 902 encodes or compresses the sensor information 1033, and transmits the encoded or compressed sensor information 1037 to the server 901 or other moving bodies 902 in the transmission of the sensor information. Accordingly, the client device 902 can reduce the amount of data transmitted.

[0543] For example, the client device 902 includes a processor and a memory, and the processor uses the memory to perform the above processing.

[0544] And the server 901 according to the present embodiment can communicate with the client device 902 mounted on the moving body, and receives sensor information 1037 indicating the peripheral situation of the moving body obtained by a sensor 1015 mounted on the moving body from the client device 902. The server 901 creates three-dimensional data 1134 of the periphery of the moving body based on the received sensor information 1037.

[0545] Accordingly, the server 901 uses the sensor information 1037 sent from the client device 902 to create three-dimensional data 1134. In this way, compared with the case where the client device 902 sends three-dimensional data, there is a possibility of reducing the amount of data to be sent. Also, since there is no need to perform processing such as compression or encoding of the three-dimensional data on the client device 902, the processing amount of the client device 902 can be reduced. Thus, the server 901 can achieve a reduction in the amount of data transmitted or a simplification of the device configuration.

[0546] Furthermore, the server 901 further sends a transmission request for the sensor information to the client device 902.

[0547] Furthermore, the server 901 further uses the created three-dimensional data 1134 to update the three-dimensional map 1135, and sends the three-dimensional map 1135 to the client device 902 according to the transmission request of the three-dimensional map 1135 from the client device 902.

[0548] Moreover, the sensor information 1037 includes at least one of the information obtained by a laser sensor, a luminance image (visible light image), an infrared image, a depth image, the position information of the sensor, and the speed information of the sensor.

[0549] Moreover, the sensor information 1037 includes information indicating the performance of the sensor.

[0550] Furthermore, the server 901 corrects the three-dimensional data according to the performance of the sensor. Accordingly, this method for creating three-dimensional data can improve the quality of the three-dimensional data.

[0551] Moreover, when receiving the sensor information, the server 901 receives a plurality of sensor information 1037 from a plurality of client devices 902, and selects the sensor information 1037 to be used in the creation of the three-dimensional data 1134 according to a plurality of information indicating the performance of the sensor included in the plurality of sensor information 1037. Accordingly, the server 901 can improve the quality of the three-dimensional data 1134.

[0552] Moreover, the server 901 decodes or decompresses the received sensor information 1037, and creates the three-dimensional data 1134 according to the decoded or decompressed sensor information 1132. Accordingly, the server 901 can reduce the amount of data transmitted.

[0553] For example, the server 901 includes a processor and a memory, and the processor uses the memory to perform the above processing.

[0554] (Embodiment 7)

[0555] In this embodiment, an encoding method and a decoding method for three-dimensional data using inter-frame prediction processing will be described.

[0556] Fig.37 FIG. 4 is a block diagram of a three-dimensional data encoding apparatus 1300 according to this embodiment. The three-dimensional data encoding apparatus 1300 generates an encoded bitstream (hereinafter also simply referred to as a bitstream) as an encoded signal by encoding three-dimensional data. As Fig.37 shown, the three-dimensional data encoding apparatus 1300 includes: a segmentation unit 1301, a subtraction unit 1302, a transformation unit 1303, a quantization unit 1304, an inverse quantization unit 1305, an inverse transformation unit 1306, an addition unit 1307, a reference volume memory 1308, an intra-frame prediction unit 1309, a reference space memory 1310, an inter-frame prediction unit 1311, a prediction control unit 1312, and an entropy encoding unit 1313.

[0557] The segmentation unit 1301 divides each space (SPC) included in the three-dimensional data into a plurality of volumes (VLM) as encoding units. Further, the segmentation unit 1301 performs octree representation (octree conversion) on the voxels within each volume. In addition, the segmentation unit 1301 may make the space and the volume the same size and perform octree representation on the space. Further, the segmentation unit 1301 may attach information (depth information, etc.) required for octree conversion to the head of the bitstream or the like.

[0558] The subtraction unit 1302 calculates the difference between the volume (volume to be encoded) output from the segmentation unit 1301 and the predicted volume generated by intra-frame prediction or inter-frame prediction described later, and outputs the calculated difference as a prediction residual to the transformation unit 1303. Fig.38 FIG. 5 shows an example of calculating the prediction residual. In addition, the bit strings of the volume to be encoded and the predicted volume shown here are, for example, position information indicating the positions of three-dimensional points (for example, point clouds) included in the volume.

[0559] Hereinafter, the octree representation and the voxel scanning order will be described. After the volume is transformed into an octree structure (octree conversion), it is encoded. The octree structure is composed of nodes and leaf nodes. Each node has eight nodes or leaf nodes, and each leaf node has voxel (VXL) information. Fig.39 FIG. 6 shows a configuration example of a volume including a plurality of voxels. Fig.40 FIG. 7 shows Fig.39 an example of transforming the volume shown in FIG. 6 into an octree structure. Here, Fig.40 among the leaf nodes shown in FIG. 7, leaf nodes 1, 2, and 3 respectively represent Fig.39 the voxels VXL1, VXL2, and VXL3 shown in FIG. 8, and represent VXLs (hereinafter referred to as valid VXLs) including a point group.

[0560] An octree is represented, for example, by a binary sequence of 0s and 1s. For example, when a node or a valid VXL is set to the value 1 and the rest are set to the value 0, the binary sequence shown is assigned to each node and leaf node. Then, the binary sequence is scanned in a breadth-first or depth-first scan order. For example, when scanned in a breadth-first manner, the binary sequence shown in A is obtained. When scanned in a depth-first manner, the binary sequence shown in B is obtained. The binary sequence obtained by this scan is encoded by entropy coding, thereby reducing the amount of information. Fig.40 Fig.41 Fig.41

[0561] Next, the depth information in the octree representation is described. The depth in the octree representation is used for controlling up to which granularity the point cloud information contained in the volume is retained. If the depth is set large, the point cloud information can be reproduced at a finer level, but the amount of data for representing the nodes and leaf nodes will increase. On the contrary, if the depth is set small, although the amount of data can be reduced, point cloud information at multiple different positions and with different colors will be regarded as being at the same position and having the same color, so the information originally possessed by the point cloud information will be lost.

[0562] For example, Fig.42 shows an example of representing the octree with depth = 2 shown in Fig.40 as an octree with depth = 1. Fig.42 The octree shown in Fig.40 has less data volume than the octree shown in Fig.42 That is, the octree shown in Fig.42 has fewer bits after binary serialization than the octree shown in Fig.40 Here, leaf node 1 and leaf node 2 shown in Fig.41 are represented as leaf node 1 shown in Fig.40 That is, the information that leaf node 1 and leaf node 2 shown in

[0563] Fig.43 shows the volume corresponding to the octree shown in Fig.42 Fig.39 VXL1 and VXL2 shown in Fig.43 correspond to VXL12 shown in Fig.39 In this case, the three-dimensional data encoding device 1300 generates Fig.43 ​​​​The color information of VXL12 shown. For example, the three-dimensional data encoding device 1300 calculates the average value, median value, or weighted average value of the color information of VXL1 and VXL2 as the color information of VXL12. In this way, the three-dimensional data encoding device 1300 can control the reduction of the data amount by changing the depth of the octree.

[0564] The three-dimensional data encoding device 1300 can also set the depth information of the octree using any one of the world space unit, space unit, and volume unit. And, at this time, the three-dimensional data encoding device 1300 can also attach the depth information to the header information of the world space, the header information of the space, or the header information of the volume. Also, the same value can be used as the depth information in all the world spaces, spaces, and volumes at different times. In this case, the three-dimensional data encoding device 1300 can also attach the depth information to the header information for managing the world space for all times.

[0565] In the case where the voxel contains color information, the transformation unit 1303 applies a frequency transformation such as an orthogonal transformation to the prediction residual of the color information of the voxel in the volume. For example, the transformation unit 1303 scans the prediction residual in a certain scan order to create a one-dimensional arrangement. After that, the transformation unit 1303 transforms the created one-dimensional arrangement into the frequency domain by applying a one-dimensional orthogonal transformation. Accordingly, when the values of the prediction residuals in the volume are close, the values of the frequency components in the low-frequency band become larger, and the values of the frequency components in the high-frequency band become smaller. Therefore, the quantization unit 1304 can reduce the encoding amount more effectively.

[0566] Also, the transformation unit 1303 can use an orthogonal transformation of two or more dimensions instead of using a one-dimensional orthogonal transformation. For example, the transformation unit 1303 maps the prediction residual to a two-dimensional arrangement in a certain scan order and applies a two-dimensional orthogonal transformation to the obtained two-dimensional arrangement. Also, the transformation unit 1303 can select the orthogonal transformation method to be used from among a plurality of orthogonal transformation methods. In this case, the three-dimensional data encoding device 1300 attaches information indicating which orthogonal transformation method is used to the bitstream. And it can be that the transformation unit 1303 selects the orthogonal transformation method to be used from among a plurality of orthogonal transformation methods with different dimensions. In this case, the three-dimensional data encoding device 1300 attaches information indicating which dimension of the orthogonal transformation method is used to the bitstream.

[0567] For example, the transformation unit 1303 aligns the scan order of the prediction residual with the scan order (such as breadth-first or depth-first) in the octree within the volume. Accordingly, since there is no need to attach information indicating the scan order of the prediction residual to the bitstream, the overhead can be reduced. Also, the transformation unit 1303 may apply a scan order different from the scan order of the octree. In this case, the three-dimensional data encoding device 1300 attaches information indicating the scan order of the prediction residual to the bitstream. Accordingly, the three-dimensional data encoding device 1300 can efficiently encode the prediction residual. Also, it may be that the three-dimensional data encoding device 1300 attaches information (such as a flag) indicating whether the scan order of the octree is applied to the bitstream, and in the case where the scan order of the octree is not applied, attaches information indicating the scan order of the prediction residual to the bitstream.

[0568] The transformation unit 1303 can transform not only the prediction residual of color information but also other attribute information possessed by the voxels. For example, it may be that the transformation unit 1303 transforms and encodes information such as reflectance obtained when acquiring point clouds through LiDAR or the like.

[0569] When the space does not have attribute information such as color information, the transformation unit 1303 can skip the processing. Also, the three-dimensional data encoding device 1300 can attach information (a flag) indicating whether to skip the processing of the transformation unit 1303 to the bitstream.

[0570] The quantization unit 1304 quantizes the frequency components of the prediction residual generated by the transformation unit 1303 using quantization control parameters to generate quantization coefficients. Accordingly, the amount of information is reduced. The generated quantization coefficients are output to the entropy encoding unit 1313. The quantization unit 1304 can control the quantization control parameters in world space units, space units, or volume units. At this time, the three-dimensional data encoding device 1300 attaches the quantization control parameters to respective header information and the like. Also, the quantization unit 1304 can change the weights for quantization control according to the frequency components of each prediction residual. For example, the quantization unit 1304 can perform detailed quantization on low-frequency components and rough quantization on high-frequency components. In this case, the three-dimensional data encoding device 1300 can attach parameters indicating the weights of the respective frequency components to the header.

[0571] When the space does not have attribute information such as color information, the quantization unit 1304 can skip the processing. Also, the three-dimensional data encoding device 1300 can attach information (a flag) indicating whether to skip the processing of the quantization unit 1304 to the bitstream.

[0572] The inverse quantization unit 1305 performs inverse quantization on the quantization coefficients generated by the quantization unit 1304 using the quantization control parameters, thereby generating the inverse quantization coefficients of the prediction residual, and outputs the generated inverse quantization coefficients to the inverse transform unit 1306.

[0573] The inverse transform unit 1306 applies an inverse transform to the inverse quantization coefficients generated in the inverse quantization unit 1305, thereby generating the prediction residual after the inverse transform is applied. Since the prediction residual after the inverse transform is applied is the prediction residual generated after quantization, it may not be exactly the same as the prediction residual output by the transform unit 1303.

[0574] The addition unit 1307 adds the prediction volume generated by the inverse transform unit 1306 after the inverse transform is applied and the prediction volume used in the generation of the prediction residual before quantization and generated by intra-frame prediction or inter-frame prediction described later to generate the reconstructed volume. The reconstructed volume is stored in the reference volume memory 1308 or the reference space memory 1310.

[0575] The intra-frame prediction unit 1309 generates the prediction volume of the volume to be encoded using the attribute information of the adjacent volumes stored in the reference volume memory 1308. The attribute information includes the color information or reflectivity of the voxels. The intra-frame prediction unit 1309 generates the predicted values of the color information or reflectivity of the volume to be encoded.

[0576] Fig.44 It is a diagram for explaining the operation of the intra-frame prediction unit 1309. For example, Fig.44 As shown, the intra-frame prediction unit 1309 generates the prediction volume of the volume to be encoded (volume idx = 3) based on the adjacent volume (volume idx = 0). Here, the volume idx is the identifier information attached to the volumes in the space, and different values are assigned to each volume. The order of assignment of the volume idx may be the same as the encoding order or different from the encoding order. For example, as Fig.44 the predicted value of the color information of the volume to be encoded shown, the intra-frame prediction unit 1309 uses the average value of the color information of the voxels included in the adjacent volume with volume idx = 0 as the adjacent volume. In this case, by subtracting the predicted value of the color information from the color information of each voxel included in the volume to be encoded, the prediction residual is generated. The processing after the transform unit 1303 is performed on the prediction residual. And, in this case, the three-dimensional data encoding device 1300 attaches the adjacent volume information and the prediction mode information to the bitstream. Here, the adjacent volume information is the information showing the adjacent volume used in the prediction, for example, showing the volume idx of the adjacent volume used in the prediction. And, the prediction mode information shows the mode used in the generation of the prediction volume. The mode is, for example, the average value mode that generates the predicted value based on the average value of the voxels in the adjacent volume, or the median value mode that generates the predicted value based on the median value of the voxels in the adjacent volume, etc.

[0577] The intra prediction unit 1309 can also generate a prediction volume based on multiple adjacent volumes. For example, in the Fig.44 configuration shown, the intra prediction unit 1309 generates prediction volume 0 based on the volume with volume idx = 0, and generates prediction volume 1 based on the volume with volume idx = 1. Then, the intra prediction unit 1309 generates the average of prediction volume 0 and prediction volume 1 as the final prediction volume. In this case, the three-dimensional data encoding device 1300 can also attach the multiple volume idxs of the multiple volumes used in the generation of the prediction volume to the bitstream.

[0578] Fig.45 The inter prediction process according to the present embodiment is shown in terms of the mode. The inter prediction unit 1311 encodes (inter prediction) the space (SPC) at a certain time T_Cur using the encoded spaces at different times T_LX. In this case, the inter prediction unit 1311 applies rotation and translation processing to the encoded spaces at different times T_LX for encoding processing.

[0579] Furthermore, the three-dimensional data encoding device 1300 attaches RT information related to the rotation and translation processing of the spaces at different times T_LX to the bitstream. The different times T_LX are, for example, the time T_L0 before the certain time T_Cur. At this time, the three-dimensional data encoding device 1300 can also attach the RT information RT_L0 related to the rotation and translation processing of the space at time T_L0 to the bitstream.

[0580] Alternatively, the different times T_LX are, for example, the time T_L1 after the certain time T_Cur. At this time, the three-dimensional data encoding device 1300 can attach the RT information RT_L1 related to the rotation and translation processing of the space at time T_L1 to the bitstream.

[0581] Alternatively, the inter prediction unit 1311 encodes (dual prediction) by referring to the spaces at both different times T_L0 and time T_L1. In this case, the three-dimensional data encoding device 1300 can attach both the RT information RT_L0 and RT_L1 related to the rotation and translation applied to the spaces respectively to the bitstream.

[0582] In addition, although T_L0 is set as the time before T_Cur and T_L1 is set as the time after T_Cur above, it is not limited thereto. For example, both T_L0 and T_L1 can be the times before T_Cur. Or, both T_L0 and T_L1 can be the times after T_Cur.

[0583] Alternatively, when the 3D data encoding device 1300 performs encoding with reference to spaces at multiple different times, the RT information related to the rotation and translation applicable to each space may be appended to the bitstream. For example, the 3D data encoding device 1300 manages the multiple encoded spaces to be referred to through two reference lists (L0 list and L1 list). When the first reference space in the L0 list is set as L0R0, the second reference space in the L0 list is set as L0R1, the first reference space in the L1 list is set as L1R0, and the second reference space in the L1 list is set as L1R1, the 3D data encoding device 1300 appends the RT information RT_L0R0 of L0R0, the RT information RT_L0R1 of L0R1, the RT information RT_L1R0 of L1R0, and the RT information RT_L1R1 of L1R1 to the bitstream. For example, the 3D data encoding device 1300 appends this RT information to the header of the bitstream or the like.

[0584] Alternatively, when the 3D data encoding device 1300 performs encoding with reference to reference spaces at multiple different times, it determines whether rotation and translation are applied for each reference space. At this time, the 3D data encoding device 1300 may append information (such as an RT application flag) indicating whether rotation and translation are applied for each reference space to the header information of the bitstream or the like. For example, the 3D data encoding device 1300 calculates the RT information and the ICP error value for each reference space to be referred to according to the space to be encoded, using the ICP (Interactive Closest Point) algorithm. When the ICP error value is equal to or less than a predetermined fixed value, the 3D data encoding device 1300 determines that rotation and translation are not required and sets the RT application flag to OFF (invalid). In addition, when the ICP error value is greater than the above-mentioned fixed value, the 3D data encoding device 1300 sets the RT application flag to ON (valid) and appends the RT information to the bitstream.

[0585] Fig.46 A syntax example of appending the RT information and the RT application flag to the header is shown. In addition, the number of bits allocated to each syntax may be determined according to the range that the syntax can take. For example, when the number of reference spaces included in the reference list L0 is 8, 3 bits may be allocated to MaxRefSpc_l0. The number of allocated bits may be changed according to the values that each syntax can take, or the number of allocated bits may be fixed regardless of the values that can be taken. When the number of allocated bits is fixed, the 3D data encoding device 1300 may append this fixed number of bits to other header information.

[0586] Here, Fig.46The shown MaxRefSpc_l0 indicates the number of reference spaces included in the reference list L0. RT_flag_l0[i] is the RT application flag for the reference space i in the reference list L0. When RT_flag_l0[i] is 1, rotation and translation are applied to the reference space i. When RT_flag_l0[i] is 0, rotation and translation are not applied to the reference space i.

[0587] R_l0[i] and T_l0[i] are the RT information for the reference space i in the reference list L0. R_l0[i] is the rotation information of the reference space i in the reference list

[0588] L0. The rotation information indicates the content of the applied rotation process, such as a rotation

[0589] matrix or quaternion, etc. T_l0[i] is the translation information of the reference space i in the reference list L0. The translation information indicates the content of the applied translation process, such as a translation vector, etc.

[0590] The shown MaxRefSpc_l1 indicates the number of reference spaces included in the reference list L1. RT_flag_l1[i] is the RT application flag for the reference space i in the reference list L1. When RT_flag_l1[i] is 1, rotation and translation are applied to the reference space i. When RT_flag_l1[i] is 0, rotation and translation are not applied to the reference space i.

[0591] R_l1[i] and T_l1[i] are the RT information for the reference space i in the reference list L1. R_l1[i] is the rotation information of the reference space i in the reference list

[0592] L1. The rotation information indicates the content of the applied rotation process, such as a rotation

[0593] matrix or quaternion, etc. T_l1[i] is the translation information of the reference space i in the reference list L1. The translation information indicates the content of the applied translation process, such as a translation vector, etc.

[0594] The inter-frame prediction unit 1311 generates a predicted volume of the volume to be encoded by using the information of the encoded reference space stored in the reference space memory 1310. As described above, before generating the predicted volume of the volume to be encoded, the inter-frame prediction unit 1311 uses the ICP (Interactive Closest Point) algorithm in the volume to be encoded space and the reference space to obtain the RT information in order to make the positional relationship between the entire volume to be encoded space and the reference space closer. Then, the inter-frame prediction unit 1311 applies rotation and translation processing to the reference space by using the obtained RT information, thereby obtaining the reference space B. After that, the inter-frame prediction unit 1311 generates a predicted volume of the volume to be encoded in the volume to be encoded space by using the information in the reference space B. Here, the three-dimensional data encoding device 1300 attaches the RT information used to obtain the reference space B to the header information of the volume to be encoded space and the like.

[0595] In this way, the inter-frame prediction unit 1311 applies rotation and translation processing to the reference space, so that after making the positional relationship between the volume to be encoded space and the reference space closer as a whole, it uses the information of the reference space to generate the predicted volume. In this way, the accuracy of the predicted volume can be improved. And since the prediction residual can be suppressed, the encoding amount can be reduced. In addition, although an example of using the volume to be encoded space and the reference space for ICP is shown here, it is not limited thereto. For example, in order to reduce the processing amount, the inter-frame prediction unit 1311 can also use at least one of the volume to be encoded space with the voxel or point cloud number extracted and the reference space with the voxel or point cloud number extracted to perform ICP, thereby obtaining the RT information.

[0596] And, when the ICP error value obtained from the result of ICP is smaller than a predetermined first threshold, that is, for example, when the positional relationship between the volume to be encoded space and the reference space is close, the inter-frame prediction unit 1311 can determine that rotation and translation processing are not required and does not perform rotation and translation. In this case, the three-dimensional data encoding device 1300 can not attach the RT information to the bitstream, thereby being able to suppress the overhead.

[0597] And, when the ICP error value is larger than a predetermined second threshold, the inter-frame prediction unit 1311 determines that the shape change in space is large, and can apply intra-frame prediction to all volumes of the volume to be encoded space. Hereinafter, the space to which intra-frame prediction is applied is called the intra-frame space. And the second threshold is a value larger than the above-mentioned first threshold. And it is not limited to ICP, and any method can be applied as long as it is a method for obtaining RT information from two voxel sets or two point cloud sets.

[0598] Further, in the case where attribute information such as shape or color is included in the three-dimensional data, as the predicted volume of the coded object volume within the coding object space, the inter-frame prediction unit 1311 searches, for example, for the volume in the reference space that is closest to the shape or color attribute information of the coded object volume. And this reference space is, for example, the reference space after the above-mentioned rotation and translation processes. The inter-frame prediction unit 1311 generates a predicted volume based on the volume (reference volume) obtained through the search. Fig.47 is a diagram for explaining the generation operation of the predicted volume. When the inter-frame prediction unit 1311 performs coding on the Fig.47 shown coded object volume (volume idx = 0) using inter-frame prediction, while sequentially scanning the reference volumes in the reference space, it searches for the volume with the smallest prediction residual, which is the difference between the coded object volume and the reference volume. The inter-frame prediction unit 1311 selects the volume with the smallest prediction residual as the predicted volume. The prediction residual between the coded object volume and the predicted volume is coded by the processing after the transform unit 1303. Here, the prediction residual refers to the difference between the attribute information of the coded object volume and the attribute information of the predicted volume. And the three-dimensional data coding device 1300 attaches the volume idx of the reference volume in the reference space that is used as the predicted volume to the head of the bitstream, etc.

[0599] In Fig.47 the shown example, the reference volume with volume idx = 4 in the reference space L0R0 is selected as the predicted volume of the coded object volume. Then, the prediction residual between the coded object volume and the reference volume and the reference volume idx = 4 are coded and attached to the bitstream.

[0600] In addition, although the generation of the predicted volume of the attribute information has been described as an example here, the same processing can also be performed for the predicted volume of the position information.

[0601] The prediction control unit 1312 controls which of intra prediction and inter prediction is used to encode the encoding target volume. Here, the mode including intra prediction and inter prediction is called the prediction mode. For example, the prediction control unit 1312 calculates, as evaluation values, the prediction residual when the encoding target volume is predicted by intra prediction and the prediction residual when it is predicted by inter prediction, and selects the prediction mode with the smaller evaluation value. Alternatively, the prediction control unit 1312 may apply orthogonal transformation, quantization, and entropy encoding to the prediction residual of intra prediction and the prediction residual of inter prediction, respectively, to calculate the actual encoding amount, and use the calculated encoding amount as the evaluation value to select the prediction mode. Also, additional overhead information other than the prediction residual (refer to volume idx information, etc.) may be added to the evaluation value. Further, when the encoding target space is predetermined to be encoded in the intra space, the prediction control unit 1312 may generally select intra prediction.

[0602] The entropy encoding unit 1313 generates an encoded signal (encoded bitstream) by performing variable-length encoding on the quantization coefficients, which are the input from the quantization unit 1304. Specifically, for example, the entropy encoding unit 1313 binarizes the quantization coefficients and performs arithmetic encoding on the obtained binary signal.

[0603] Next, a three-dimensional data decoding device that decodes the encoded signal generated by the three-dimensional data encoding device 1300 will be described. Fig.48 FIG. is a block diagram of the three-dimensional data decoding device 1400 according to the present embodiment. The three-dimensional data decoding device 1400 includes: an entropy decoding unit 1401, an inverse quantization unit 1402, an inverse transformation unit 1403, an addition unit 1404, a reference volume memory 1405, an intra prediction unit 1406, a reference space memory 1407, an inter prediction unit 1408, and a prediction control unit 1409.

[0604] The entropy decoding unit 1401 performs variable-length decoding on the encoded signal (encoded bitstream). For example, the entropy decoding unit 1401 performs arithmetic decoding on the encoded signal to generate a binary signal, and generates quantization coefficients based on the generated binary signal.

[0605] The inverse quantization unit 1402 performs inverse quantization on the quantization coefficients input from the entropy decoding unit 1401 using quantization parameters attached to the bitstream or the like, thereby generating inverse quantization coefficients.

[0606] The inverse transformation unit 1403 performs inverse transformation on the inverse quantization coefficients input from the inverse quantization unit 1402, thereby generating a prediction residual. For example, the inverse transformation unit 1403 performs inverse orthogonal transformation on the inverse quantization coefficients according to the information attached to the bitstream, thereby generating a prediction residual.

[0607] The addition unit 1404 adds the prediction residual generated by the inverse transformation unit 1403 and the prediction volume generated by intra prediction or inter prediction to generate a reconstructed volume. This reconstructed volume is output as decoded three-dimensional data and stored in the reference volume memory 1405 or the reference space memory 1407.

[0608] The intra prediction unit 1406 generates a prediction volume by intra prediction using the reference volume in the reference volume memory 1405 and the information attached to the bitstream. Specifically, the intra prediction unit 1406 obtains prediction mode information and adjacent volume information (e.g., volume idx) attached to the bitstream, and uses the adjacent volume indicated by the adjacent volume information to generate a prediction volume in the mode indicated by the prediction mode information. In addition, the details of these processes are the same as those of the intra prediction unit 1309 described above, except that the information attached to the bitstream is used.

[0609] The inter prediction unit 1408 generates a prediction volume by inter prediction using the reference space in the reference space memory 1407 and the information attached to the bitstream. Specifically, the inter prediction unit 1408 uses the RT information for each reference space attached to the bitstream, applies rotation and translation processing to the reference space, and uses the processed reference space to generate a prediction volume. In addition, when the RT application flag for each reference space exists in the bitstream, the inter prediction unit 1408 applies rotation and translation processing to the reference space according to the RT application flag. In addition, the details of the above processes are the same as those of the inter prediction unit 1311 described above, except that the information attached to the bitstream is used.

[0610] Whether to decode the volume to be decoded by intra prediction or inter prediction will be controlled by the prediction control unit 1409. For example, the prediction control unit 1409 selects intra prediction or inter prediction according to the information attached to the bitstream and indicating the prediction mode to be used. In addition, the prediction control unit 1409 may usually select intra prediction when it is predetermined that the object space to be decoded is decoded as an intra space.

[0611] A modification example of this embodiment will be described below. In this embodiment, although rotation and translation are applied in units of space as an example, rotation and translation can also be applied in smaller units. For example, the three-dimensional data encoding device 1300 can divide the space into sub-spaces and apply rotation and translation in units of sub-spaces. In this case, the three-dimensional data encoding device 1300 generates RT information for each sub-space and attaches the generated RT information to the head of the bitstream or the like. Further, the three-dimensional data encoding device 1300 can apply rotation and translation in units of volume as the encoding unit. In this case, the three-dimensional data encoding device 1300 generates RT information in units of encoding volume and attaches the generated RT information to the head of the bitstream or the like. Moreover, the above can be combined. That is, the three-dimensional data encoding device 1300 can apply rotation and translation in a large unit first and then apply rotation and translation in a smaller unit. For example, the three-dimensional data encoding device 1300 can apply rotation and translation in units of space and apply different rotations and translations to each of a plurality of volumes included in the obtained space.

[0612] Further, in this embodiment, although rotation and translation are applied to the reference space as an example, it is not limited thereto. For example, the three-dimensional data encoding device 1300 can apply a scaling process to change the size of the three-dimensional data. Further, the three-dimensional data encoding device 1300 can also apply any one or two of rotation, translation, and scaling. Further, as described above, in the case where processing is applied in different units in multiple stages, the types of processing applied in each unit can be different. For example, rotation and translation can be applied in units of space, and translation can be applied in units of volume.

[0613] In addition, regarding these modification examples, the three-dimensional data decoding device 1400 can be similarly applied.

[0614] As described above, the three-dimensional data encoding device 1300 according to this embodiment performs the following processing. Fig.48 It is a flowchart of the inter-frame prediction process performed by the three-dimensional data encoding device 1300.

[0615] First, the three-dimensional data encoding device 1300 generates prediction position information (for example, a prediction volume) by using the position information of three-dimensional points included in the target three-dimensional data (for example, the encoding target space) and the reference three-dimensional data (for example, the reference space) at different times (S1301). Specifically, the three-dimensional data encoding device 1300 generates prediction position information by applying rotation and translation processing to the position information of the three-dimensional points included in the reference three-dimensional data.

[0616] In addition, the three-dimensional data encoding device 1300 performs rotation and translation processing in a first unit (e.g., a space), and generates predicted position information in a second unit (e.g., a volume) that is finer than the first unit. For example, the three-dimensional data encoding device 1300 searches for a volume in the plurality of volumes included in the reference space after rotation and translation processing, where the difference between the encoded object volume included in the encoded object space and the position information is minimized, and uses the obtained volume as the predicted volume. In addition, the three-dimensional data encoding device 1300 may perform rotation and translation processing and generation of predicted position information in the same unit.

[0617] Alternatively, the three-dimensional data encoding device 1300 may apply first rotation and translation processing to the position information of the three-dimensional points included in the reference three-dimensional data in a first unit (e.g., a space), and apply second rotation and translation processing to the position information of the three-dimensional points obtained by the first rotation and translation processing in a second unit (e.g., a volume) that is finer than the first unit, thereby generating predicted position information.

[0618] Here, the position information of the three-dimensional points and the predicted position information are represented in an octree structure as Fig.41 shown. For example, the position information of the three-dimensional points and the predicted position information are represented in a scan order that gives priority to the width over the depth in the octree structure. Alternatively, the position information of the three-dimensional points and the predicted position information are represented in a scan order that gives priority to the depth over the width in the octree structure.

[0619] Moreover, as Fig.46 shown, the three-dimensional data encoding device 1300 encodes an RT application flag indicating whether rotation and translation processing is applied to the position information of the three-dimensional points included in the reference three-dimensional data. That is, the three-dimensional data encoding device 1300 generates an encoded signal (encoded bitstream) including the RT application flag. In addition, the three-dimensional data encoding device 1300 encodes RT information indicating the content of the rotation and translation processing. That is, the three-dimensional data encoding device 1300 generates an encoded signal (encoded bitstream) including the RT information. Alternatively, the three-dimensional data encoding device 1300 may encode the RT information when the RT application flag indicates that rotation and translation processing is applied, and may not encode the RT information when the RT application flag indicates that rotation and translation processing is not applied.

[0620] Furthermore, the three-dimensional data includes, for example, the position information of the three-dimensional points and the attribute information (such as color information) of each three-dimensional point. The three-dimensional data encoding device 1300 generates predicted attribute information (S1302) using the attribute information of the three-dimensional points included in the reference three-dimensional data.

[0621] Next, the three-dimensional data encoding device 1300 uses the predicted position information to encode the position information of the three-dimensional points included in the target three-dimensional data. For example, as shown in Fig.38 the three-dimensional data encoding device 1300 calculates the difference between the position information of the three-dimensional points included in the target three-dimensional data and the predicted position information, that is, the differential position information (S1303).

[0622] In addition, the three-dimensional data encoding device 1300 uses the predicted attribute information to encode the attribute information of the three-dimensional points included in the target three-dimensional data. For example, the three-dimensional data encoding device 1300 calculates the difference between the attribute information of the three-dimensional points included in the target three-dimensional data and the predicted attribute information, that is, the differential attribute information (S1304). Next, the three-dimensional data encoding device 1300 performs transformation and quantization on the calculated differential attribute information (S1305).

[0623] Finally, the three-dimensional data encoding device 1300 encodes the differential position information and the quantized differential attribute information (for example, entropy encoding) (S1306). That is, the three-dimensional data encoding device 1300 generates an encoded signal (encoded bitstream) including the differential position information and the differential attribute information.

[0624] In addition, when the attribute information is not included in the three-dimensional data, the three-dimensional data encoding device 1300 may not perform steps S1302, S1304, and S1305. Also, the three-dimensional data encoding device 1300 may only perform one of the encoding of the position information of the three-dimensional points and the encoding of the attribute information of the three-dimensional points.

[0625] And Fig.49 the order of the processes shown is only an example and is not limited thereto. For example, since the processing for the position information (S1301, S1303) and the processing for the attribute information (S1302, S1304, S1305) are independent of each other, they can be executed in any order, or a part of them can be processed in parallel.

[0626] As described above, in the present embodiment, the three-dimensional data encoding device 1300 uses the position information of the three-dimensional points included in the target three-dimensional data and the reference three-dimensional data at different times to generate the predicted position information, and encodes the difference between the position information of the three-dimensional points included in the target three-dimensional data and the predicted position information, that is, the differential position information. Accordingly, since the data amount of the encoded signal can be reduced, the encoding efficiency can be improved.

[0627] Further, in the present embodiment, the three-dimensional data encoding device 1300 generates predicted attribute information by using the attribute information of the three-dimensional points included in the reference three-dimensional data, and encodes the differential attribute information, which is the difference between the attribute information of the three-dimensional points included in the target three-dimensional data and the predicted attribute information. Accordingly, since the data amount of the encoded signal can be reduced, the encoding efficiency can be improved.

[0628] For example, the three-dimensional data encoding device 1300 includes a processor and a memory, and the processor uses the memory to perform the above processing.

[0629] Fig.48 It is a flowchart of the inter-frame prediction process performed by the three-dimensional data decoding device 1400.

[0630] First, the three-dimensional data decoding device 1400 decodes (e.g., entropy decodes) the differential position information and the differential attribute information from the encoded signal (encoded bitstream) (S1401).

[0631] Further, the three-dimensional data decoding device 1400 decodes the RT application flag indicating whether rotation and translation processing are applicable to the position information of the three-dimensional points included in the reference three-dimensional data from the encoded signal. And the three-dimensional data decoding device 1400 decodes the RT information indicating the content of the rotation and translation processing. In addition, the three-dimensional data decoding device 1400 decodes the RT information when the RT application flag indicates that rotation and translation processing are applicable, and does not decode the RT information when the RT application flag indicates that rotation and translation processing are not applicable.

[0632] Next, the three-dimensional data decoding device 1400 performs inverse quantization and inverse transformation on the decoded differential attribute information (S1402).

[0633] Next, the three-dimensional data decoding device 1400 generates predicted position information (e.g., predicted volume) by using the position information of the three-dimensional points included in the target three-dimensional data (e.g., decoding target space) and the reference three-dimensional data (e.g., reference space) at different times (S1403). Specifically, the three-dimensional data decoding device 1400 generates predicted position information by applying rotation and translation processing to the position information of the three-dimensional points included in the reference three-dimensional data.

[0634] More specifically, the three-dimensional data decoding device 1400 applies rotation and translation processing to the position information of the three-dimensional points included in the reference three-dimensional data indicated by the RT information when the RT application flag indicates that rotation and translation processing are applicable. And when the RT application flag indicates that rotation and translation processing are not applicable, the three-dimensional data decoding device 1400 does not apply rotation and translation processing to the position information of the three-dimensional points included in the reference three-dimensional data.

[0635] In addition, the three-dimensional data decoding device 1400 can perform rotation and translation processing in a first unit (e.g., space), and can generate predicted position information in a second unit (e.g., volume) that is finer than the first unit. In addition, the three-dimensional data decoding device 1400 can also perform rotation and translation processing, and generation of predicted position information, in the same unit.

[0636] Specifically, the three-dimensional data decoding device 1400 can apply first rotation and translation processing to the position information of the three-dimensional points included in the reference three-dimensional data in a first unit (e.g., space), and apply second rotation and translation processing to the position information of the three-dimensional points obtained through the first rotation and translation processing in a second unit (e.g., volume) that is finer than the first unit, thereby generating predicted position information.

[0637] Here, the position information of the three-dimensional points and the predicted position information are, for example, Fig.41 represented in an octree structure. For example, the position information of the three-dimensional points and the predicted position information are represented in a scan order that gives priority to the width among the depth and width in the octree structure. Alternatively, the position information of the three-dimensional points and the predicted position information are represented in a scan order that gives priority to the depth among the depth and width in the octree structure.

[0638] The three-dimensional data decoding device 1400 generates predicted attribute information by using the attribute information of the three-dimensional points included in the reference three-dimensional data (S1404).

[0639] Next, the three-dimensional data decoding device 1400 decodes the encoded position information included in the encoded signal by using the predicted position information, thereby restoring the position information of the three-dimensional points included in the target three-dimensional data. Here, the encoded position information is, for example, differential position information, and the three-dimensional data decoding device 1400 restores the position information of the three-dimensional points included in the target three-dimensional data by adding the differential position information and the predicted position information (S1405).

[0640] In addition, the three-dimensional data decoding device 1400 decodes the encoded attribute information included in the encoded signal by using the predicted attribute information, thereby restoring the attribute information of the three-dimensional points included in the target three-dimensional data. Here, the encoded attribute information is, for example, differential attribute information, and the three-dimensional data decoding device 1400 restores the attribute information of the three-dimensional points included in the target three-dimensional data by adding the differential attribute information and the predicted attribute information (S1406).

[0641] Alternatively, when the attribute information is not included in the three-dimensional data, the three-dimensional data decoding device 1400 may also not execute steps S1402, S1404, and S1406. Moreover, the three-dimensional data decoding device 1400 may also perform only one of the decoding of the position information of the three-dimensional points and the decoding of the attribute information of the three-dimensional points.

[0642] Moreover, Fig.50 The order of the processes shown is an example and is not limited thereto. For example, since the processes for the position information (S1403, S1405) and the processes for the attribute information (S1402, S1404, S1406) are independent of each other, they can be performed in any order, and a part of them can also be processed in parallel.

[0643] (Embodiment 8)

[0644] The information of the three-dimensional point group includes position information (geometry) and attribute information (attribute). The position information includes coordinates (x coordinate, y coordinate, z coordinate) based on a certain point. When encoding the position information, instead of directly encoding the coordinates of each three-dimensional point, a method is used in which the positions of each three-dimensional point are represented by an octree representation and the information of the octree is encoded to reduce the amount of encoding.

[0645] On the other hand, the attribute information includes information such as color information (RGB, YUV, etc.), reflectance, and normal vector representing each three-dimensional point. For example, the three-dimensional data encoding device can encode the attribute information using an encoding method different from the position information.

[0646] In this embodiment, an encoding method for the attribute information will be described. In addition, in this embodiment, integer values are used as the values of the attribute information for description. For example, when each color component of the color information RGB or YUV has an 8-bit precision, each color component takes an integer value from 0 to 255. When the value of the reflectance has a 10-bit precision, the value of the reflectance takes an integer value from 0 to 1023. In addition, when the bit precision of the attribute information is a fractional precision, the three-dimensional data encoding device may also multiply the value by a scaling value and then round it to an integer value so that the value of the attribute information becomes an integer value. In addition, the three-dimensional data encoding device may also attach the scaling value to the head of the bitstream or the like.

[0647] As a method for encoding attribute information of three-dimensional points, consider calculating a predicted value of the attribute information of the three-dimensional points, and encoding the difference (prediction residual) between the value of the original attribute information and the predicted value. For example, when the value of the attribute information of the three-dimensional point p is Ap and the predicted value is Pp, the three-dimensional data encoding device encodes the absolute value of the difference Diffp = |Ap - Pp|. In this case, if the predicted value Pp can be generated with high accuracy, the value of the absolute difference Diffp becomes smaller. Therefore, for example, by using an encoding table in which the smaller the value, the smaller the number of bits generated, to perform entropy encoding on the absolute difference Diffp, the amount of encoding can be reduced.

[0648] As a method for generating a predicted value of attribute information, consider using the attribute information of other three-dimensional points, that is, reference three-dimensional points, located around the object three-dimensional point to be encoded. Here, the reference three-dimensional points refer to three-dimensional points within a pre-specified distance range from the object three-dimensional point. For example, when there are an object three-dimensional point p = (x1, y1, z1) and a three-dimensional point q = (x2, y2, z2), the three-dimensional data encoding device calculates the Euclidean distance d(p, q) between the three-dimensional point p and the three-dimensional point q shown in (Equation A1).

[0649]

Equation 1

[0650]

[0651] When the Euclidean distance d(p, q) is less than a predetermined threshold THd, the three-dimensional data encoding device determines that the position of the three-dimensional point q is close to the position of the object three-dimensional point p, and determines to use the value of the attribute information of the three-dimensional point q in the generation of the predicted value of the attribute information of the object three-dimensional point p. In addition, the distance calculation method can also be other methods, for example, the Mahalanobis distance can also be used. In addition, the three-dimensional data encoding device can also determine not to use three-dimensional points outside the pre-specified distance range from the object three-dimensional point for prediction processing. For example, when there is a three-dimensional point r and the distance d(p, r) between the object three-dimensional point p and the three-dimensional point r is equal to or greater than the threshold THd, the three-dimensional data encoding device can also determine not to use the three-dimensional point r for prediction. In addition, the three-dimensional data encoding device can also attach information indicating the threshold THd to the head of the bitstream, etc.

[0652] Fig.51 It is a diagram showing an example of three-dimensional points. In this example, the distance d(p, q) between the object three-dimensional point p and the three-dimensional point q is less than the threshold THd. Therefore, the three-dimensional data encoding device determines the three-dimensional point q as a reference three-dimensional point of the object three-dimensional point p, and determines to use the value of the attribute information Aq of the three-dimensional point q in the generation of the predicted value Pp of the attribute information Ap of the object three-dimensional point p.

[0653] On the other hand, the distance d(p, r) between the object three-dimensional point p and the three-dimensional point r is equal to or greater than the threshold THd. Therefore, the three-dimensional data encoding device determines that the three-dimensional point r is not the reference three-dimensional point of the object three-dimensional point p, and determines that the value of the attribute information Ar of the three-dimensional point r is not used in the generation of the predicted value Pp of the attribute information Ap of the object three-dimensional point p.

[0654] In addition, in the case of encoding the attribute information of the object three-dimensional point using the predicted value, the three-dimensional data encoding device uses the three-dimensional point for which the attribute information has been encoded and decoded as the reference three-dimensional point. Similarly, in the case of decoding the attribute information of the object three-dimensional point of the decoded object using the predicted value, the three-dimensional data decoding device uses the three-dimensional point for which the attribute information has been decoded as the reference three-dimensional point. Thereby, the same predicted value can be generated at the time of encoding and decoding, and thus the bit stream of the three-dimensional point generated by encoding can be correctly decoded on the decoding side.

[0655] In addition, in the case of encoding the attribute information of the three-dimensional point, it is considered to classify each three-dimensional point into a plurality of levels using the position information of the three-dimensional point and then perform encoding. Here, each classified level is referred to as LoD (Level of Detail). Fig.52 A method for generating LoD will be described.

[0656] First, the three-dimensional data encoding device selects the initial point a0 and assigns it to LoD0. Next, the three-dimensional data encoding device extracts the point a1 whose distance from the point a0 is greater than the threshold Thres_LoD[0] of LoD0 and assigns it to LoD0. Next, the three-dimensional data encoding device extracts the point a2 whose distance from the point a1 is greater than the threshold Thres_LoD[0] of LoD0 and assigns it to LoD0. In this way, the three-dimensional data encoding device constructs LoD0 such that the distance between each point within LoD0 is greater than the threshold Thres_LoD[0].

[0657] Next, the three-dimensional data encoding device selects the point b0 that has not been assigned LoD and assigns it to LoD1. Next, the three-dimensional data encoding device extracts the point b1 whose distance from the point b0 is greater than the threshold Thres_LoD[1] of LoD1 and that has not been assigned LoD and assigns it to LoD1. Next, the three-dimensional data encoding device extracts the point b2 whose distance from the point b1 is greater than the threshold Thres_LoD[1] of LoD1 and that has not been assigned LoD and assigns it to LoD1. In this way, the three-dimensional data encoding device constructs LoD1 such that the distance between each point within LoD1 is greater than the threshold Thres_LoD[1].

[0658] Next, the 3D data encoding device selects a point c0 that has not been assigned an LoD and assigns it to LoD2. Next, the 3D data encoding device extracts a point c1 that is at a distance greater than the threshold Thres_LoD[2] of LoD2 from point c0 and that has not been assigned an LoD, and assigns it to LoD2. Next, the 3D data encoding device extracts a point c2 that is at a distance greater than the threshold Thres_LoD[2] of LoD2 from point c1 and that has not been assigned an LoD, and assigns it to LoD2. In this way, the 3D data encoding device constructs LoD2 such that the distance between each point within LoD2 is greater than the threshold Thres_LoD[2]. For example, as Fig.53 shown, the thresholds Thres_LoD[0], Thres_LoD[1], and Thres_LoD[2] for each LoD are set.

[0659] In addition, the 3D data encoding device may also attach information representing the thresholds of each LoD to the header of the bitstream or the like. For example, in the case of the example Fig.53 shown, the 3D data encoding device may also attach the thresholds Thres_LoD[0], Thres_LoD[1], and Thres_LoD[2] to the header.

[0660] In addition, the 3D data encoding device may also assign all 3D points that have not been assigned an LoD to the lowest layer of the LoD. In this case, the 3D data encoding device can reduce the encoding amount of the header by not attaching the threshold of the lowest layer of the LoD to the header. For example, in the case of the example Fig.53 shown, the 3D data encoding device attaches the thresholds Thres_LoD[0] and Thres_LoD[1] to the header and does not attach Thres_LoD[2] to the header. In this case, the 3D data decoding device may also estimate the value of Thres_LoD[2] as 0. In addition, the 3D data encoding device may also attach the number of layers of the LoD to the header. Thereby, the 3D data decoding device can use the number of layers of the LoD to determine the lowest layer of the LoD.

[0661] In addition, as Fig.53 shown, the values of the thresholds for each layer of the LoD are set to be larger for the upper layers, so that the higher the layer (the layer closer to LoD0), the sparser the group of points with a greater distance between 3D points becomes, and the lower the layer, the denser the group of points with a closer distance between 3D points becomes. In addition, in the example Fig.53 shown, LoD0 is the topmost layer.

[0662] In addition, the method of selecting the initial three-dimensional points when setting each LoD can also depend on the encoding order during the encoding of the position information. For example, the three-dimensional data encoding device selects the three-dimensional point that was first encoded during the encoding of the position information as the initial point a0 of LoD0, and selects points a1 and a2 with the initial point a0 as the base point to form LoD0. Moreover, the three-dimensional data encoding device can also select the three-dimensional point with the earliest encoded position information among the three-dimensional points that do not belong to LoD0 as the initial point b0 of LoD1. That is, the three-dimensional data encoding device can also select the three-dimensional point with the earliest encoded position information among the three-dimensional points in the upper layer (LoD0 to LoDn-1) that do not belong to LoDn as the initial point n0 of LoDn. Thus, the three-dimensional data decoding device can form the same LoD as during encoding by using the same initial point selection method during decoding, and thus can appropriately decode the bitstream. Specifically, the three-dimensional data decoding device selects the three-dimensional point with the earliest decoded position information among the three-dimensional points in the upper layer that do not belong to LoDn as the initial point n0 of LoDn.

[0663] Hereinafter, a method of generating a predicted value of the attribute information of the three-dimensional points using the LoD information will be described. For example, in the case of encoding the three-dimensional points included in LoD0 in sequence, the three-dimensional data encoding device uses the encoded and decoded (hereinafter, also simply referred to as "encoded") attribute information included in LoD0 and LoD1 to generate the target three-dimensional points included in LoD1. In this way, the three-dimensional data encoding device uses the encoded attribute information included in LoDn' (n' <= n) to generate a predicted value of the attribute information of the three-dimensional points included in LoDn. That is, the three-dimensional data encoding device does not use the attribute information of the three-dimensional points included in the lower layer of LoDn in the calculation of the predicted value of the attribute information of the three-dimensional points included in LoDn.

[0664] For example, the three-dimensional data encoding device generates a predicted value of the attribute information of the three-dimensional points by calculating the average value of the attribute values of N or fewer three-dimensional points among the encoded three-dimensional points around the target three-dimensional points to be encoded. In addition, the three-dimensional data encoding device can attach the value of N to the head of the bitstream or the like. In addition, the three-dimensional data encoding device can also change the value of N for each three-dimensional point and attach the value of N to each three-dimensional point. Thus, an appropriate N can be selected for each three-dimensional point, so that the accuracy of the predicted value can be improved. Therefore, the prediction residual can be reduced. In addition, the three-dimensional data encoding device can also attach the value of N to the head of the bitstream and fix the value of N within the bitstream. Thus, it is not necessary to encode or decode the value of N for each three-dimensional point, so that the processing amount can be reduced. In addition, the three-dimensional data encoding device can also encode the value of N separately for each LoD. Thus, by selecting an appropriate N for each LoD, the encoding efficiency can be improved.

[0665] Alternatively, the 3D data encoding device can also calculate the predicted value of the attribute information of a 3D point by the weighted average of the attribute information of the N encoded 3D points around it. For example, the 3D data encoding device calculates the weights using the distance information between the target 3D point and each of the N surrounding 3D points.

[0666] When the 3D data encoding device encodes the value of N for each LoD, for example, the value of N is set larger for the upper layer of the LoD and smaller for the lower layer. In the upper layer of the LoD, the distance between the 3D points belonging to this layer is large. Therefore, by setting the value of N large and selecting multiple surrounding 3D points for averaging, it is possible to improve the prediction accuracy. In addition, since in the lower layer of the LoD, the distance between the 3D points belonging to this layer is small, it is possible to perform efficient prediction while suppressing the processing amount of averaging by setting the value of N small.

[0667] Fig.54 It is a diagram showing an example of the attribute information used in the predicted value. As described above, the predicted value of the point P included in LoDn is generated using the encoded surrounding points P' included in LoD N' (N' <= N). Here, the surrounding points P' are selected based on the distance from the point P. For example, the attribute information of points a0, a1, a2, b0, b1 is used to generate Fig.54 the predicted value of the attribute information of the point b2 shown.

[0668] According to the above value of N, the selected surrounding points change. For example, when N = 5, as the surrounding points of the point b2, a0, a1, a2, b0, b1 are selected. When N = 4, based on the distance information, points a0, a1, a2, b1 are selected.

[0669] The predicted value is calculated by weighted average depending on the distance. For example, in Fig.54 the example shown, the predicted value a2p of the point a2 is calculated by the weighted average of the attribute information of the points a0 and a1 as shown in (Equation A2) and (Equation A3). In addition, A i is the value of the attribute information of the point ai.

[0670] [Equation 2]

[0671]

[0672]

[0673] In addition, the predicted value b2p of the point b2 is calculated by the weighted average of the attribute information of the points a0, a1, a2, b0, b1 as shown in (Equation A4) to (Equation A6). In addition, B i is the value of the attribute information of the point bi.

[0674]

Equation 3

[0675]

[0676]

[0677]

[0678] In addition, the three-dimensional data encoding device can also calculate the difference value (prediction residual) between the value of the attribute information of the three-dimensional point and the predicted value generated from the surrounding points, and quantize the calculated prediction residual. For example, the three-dimensional data encoding device quantizes by dividing the prediction residual by a quantization scale (also referred to as a quantization step). In this case, the smaller the quantization scale, the smaller the error (quantization error) generated due to quantization. On the contrary, the larger the quantization scale, the larger the quantization error.

[0679] In addition, the three-dimensional data encoding device can also change the quantization scale used for each LoD. For example, the higher the layer of the three-dimensional data encoding device, the smaller the quantization scale, and the lower the layer, the larger the quantization scale. The value of the attribute information of the three-dimensional points belonging to the upper layer may be used as the predicted value of the attribute information of the three-dimensional points belonging to the lower layer. Therefore, the quantization scale of the upper layer can be reduced to suppress the quantization error generated in the upper layer, and by improving the accuracy of the predicted value, the encoding efficiency can be improved. In addition, the three-dimensional data encoding device can also attach the quantization scale used for each LoD to the header or the like. As a result, the three-dimensional data decoding device can correctly decode the quantization scale, and thus can appropriately decode the bitstream.

[0680] In addition, the three-dimensional data encoding device can also transform the signed integer value (signed quantization value) that is the quantized prediction residual into an unsigned integer value (unsigned quantization value). As a result, in the case of entropy encoding the prediction residual, there is no need to consider the generation of negative integers. In addition, the three-dimensional data encoding device does not necessarily need to transform the signed integer value into an unsigned integer value. For example, it can also perform entropy encoding on the sign bit separately.

[0681] The prediction residual is calculated by subtracting the predicted value from the original value. For example, as shown in (Equation A7), the prediction residual a2r of point a2 is calculated by subtracting the predicted value a2p of point a2 from the value A of the attribute information of point a2 2 minus the predicted value a2p of point a2. As shown in (Equation A8), the prediction residual b2r of point b2 is calculated by subtracting the predicted value b2p of point b2 from the value B 2 minus the predicted value b2p of point b2.

[0682] a2r = A 2 - a2p…(Equation A7)

[0683] b2r = B 2 -b2p…(Equation A8)

[0684] In addition, the prediction residual is quantized by dividing it by QS (Quantization Step). For example, the quantized value a2q of point a2 is calculated by (Equation A9). The quantized value b2q of point b2 is calculated by (Equation A10). Here, QS_LoD0 is the QS for LoD0, and QS_LoD1 is the QS for LoD1. That is, the QS can be changed according to LoD.

[0685] a2q = a2r / QS_LoD0…(Equation A9)

[0686] b2q = b2r / QS_LoD1…(Equation A10)

[0687] In addition, as described below, the three-dimensional data encoding device transforms the signed integer value, which is the above-mentioned quantized value, into an unsigned integer value. When the signed integer value a2q is less than 0, the three-dimensional data encoding device sets the unsigned integer value a2u to -1 - (2 × a2q). When the signed integer value a2q is 0 or more, the three-dimensional data encoding device sets the unsigned integer value a2u to 2 × a2q.

[0688] Similarly, when the signed integer value b2q is less than 0, the three-dimensional data encoding device sets the unsigned integer value b2u to -1 - (2 × b2q). When the signed integer value b2q is 0 or more, the three-dimensional data encoding device sets the unsigned integer value b2u to 2 × b2q.

[0689] In addition, the three-dimensional data encoding device can also encode the quantized prediction residual (unsigned integer value) by entropy encoding. For example, binary arithmetic coding can also be applied after binarizing the unsigned integer value.

[0690] In addition, in this case, the three-dimensional data encoding device can also switch the binarization method according to the value of the prediction residual. For example, when the prediction residual pu is less than the threshold R_TH, the three-dimensional data encoding device binarizes the prediction residual pu with a fixed number of bits required to represent the threshold R_TH. In addition, when the prediction residual pu is equal to or greater than the threshold R_TH, the three-dimensional data encoding device binarizes the binarized data of the threshold R_TH and the value of (pu - R_TH) using Exponential-Golomb or the like.

[0691] For example, when the threshold R_TH is 63 and the prediction residual pu is less than 63, the three-dimensional data encoding device binarizes the prediction residual pu with 6 bits. Additionally, when the prediction residual pu is 63 or more, the three-dimensional data encoding device performs arithmetic coding by binarizing the binary data (111111) of the threshold R_TH and (pu - 63) using Golomb code.

[0692] In a more specific example, when the prediction residual pu is 32, the three-dimensional data encoding device generates 6-bit binary data (100000) and performs arithmetic coding on this bit string. Additionally, when the prediction residual pu is 66, the three-dimensional data encoding device generates a bit string (00100) representing the binary data (111111) of the threshold R_TH and the value 3 (66 - 63) using Golomb code, and performs arithmetic coding on this bit string (111111 + 00100).

[0693] In this way, the three-dimensional data encoding device switches the binarization method according to the size of the prediction residual, thereby enabling coding while suppressing a sharp increase in the number of binarization bits when the prediction residual becomes large. In addition, the three-dimensional data encoding device may also attach the threshold R_TH to the head of the bit stream or the like.

[0694] For example, when encoding at a high bit rate, that is, when the quantization scale is small, the quantization error becomes small and the prediction accuracy becomes high. As a result, the prediction residual may not become large. Therefore, in this case, the three-dimensional data encoding device sets the threshold R_TH large. Thereby, the possibility of encoding the binary data of the threshold R_TH becomes low and the encoding efficiency is improved. On the contrary, when encoding at a low bit rate, that is, when the quantization scale is large, the quantization error becomes large and the prediction accuracy deteriorates. As a result, it is possible that the prediction residual becomes large. Therefore, in this case, the three-dimensional data encoding device sets the threshold R_TH small. Thereby, a sharp increase in the bit length of the binary data can be prevented.

[0695] In addition, the three-dimensional data encoding device may also switch the threshold R_TH for each LoD and attach the threshold R_TH of each LoD to the head or the like. That is, the three-dimensional data encoding device may also switch the binarization method for each LoD. For example, in the upper layer, since the distance between three-dimensional points is far, the prediction accuracy deteriorates. As a result, it is possible that the prediction residual becomes large. Therefore, the three-dimensional data encoding device sets the threshold R_TH small for the upper layer to prevent a sharp increase in the bit length of the binary data. Additionally, in the lower layer, since the distance between three-dimensional points is close, the prediction accuracy becomes high. As a result, it is possible that the prediction residual becomes small. Therefore, the three-dimensional data encoding device sets the threshold R_TH large for the layer to improve the encoding efficiency.

[0696] Fig.55 This is a diagram showing an example of an exponential Golomb code, which represents the relationship between the values before binarization (multi-valued) and the bits (codes) after binarization. Additionally, it is also possible to Fig.55 invert the 0 and 1 shown.

[0697] Furthermore, the three-dimensional data encoding device applies arithmetic coding to the binarized data of the prediction residual. As a result, the coding efficiency can be improved. In addition, when applying arithmetic coding, in the binarized data, in the part binarized with n bits, i.e., the n-bit code, and the part binarized using the exponential Golomb code, i.e., the remaining code, the tendency of the occurrence probabilities of 0 and 1 for each bit may be different. Therefore, the three-dimensional data encoding device can also switch the application method of arithmetic coding based on the n-bit code and the remaining code.

[0698] For example, for the n-bit code, the three-dimensional data encoding device performs arithmetic coding on each bit using a different coding table (probability table). At this time, the three-dimensional data encoding device can also change the number of coding tables used for each bit. For example, the three-dimensional data encoding device uses 1 coding table to perform arithmetic coding on the leading bit b0 of the n-bit code. Additionally, the three-dimensional data encoding device uses 2 coding tables for the next bit b1. Furthermore, the three-dimensional data encoding device switches the coding table used in the arithmetic coding of bit b1 according to the value of b0 (0 or 1). Similarly, the three-dimensional data encoding device uses 4 coding tables for the next bit b2. Additionally, the three-dimensional data encoding device switches the coding table used in the arithmetic coding of bit b2 according to the values of b0 and b1 (0 to 3).

[0699] In this way, when the three-dimensional data encoding device performs arithmetic coding on each bit bn-1 of the n-bit code, it uses 2 n-1 coding tables. Additionally, the three-dimensional data encoding device switches the coding table used according to the values (occurrence patterns) of the bits before bn-1. As a result, the three-dimensional data encoding device can use an appropriate coding table for each bit, thus improving the coding efficiency.

[0700] Furthermore, the three-dimensional data encoding device can also reduce the number of coding tables used for each bit. For example, when performing arithmetic coding on each bit bn-1, the three-dimensional data encoding device can also switch 2 mA coding table. Thus, the number of coding tables used in each bit can be suppressed, and the coding efficiency can be improved. In addition, the three-dimensional data encoding device can also update the occurrence probabilities of 0 and 1 in each coding table according to the value of the actually generated binarized data. Additionally, the three-dimensional data encoding device can also fix the occurrence probabilities of 0 and 1 in the coding tables of a part of the bits. Thus, the number of updates of the occurrence probability can be suppressed, and therefore the processing amount can be reduced.

[0701] For example, when n bits are encoded as b0b1b2…bn-1, the coding table used for b0 is 1 (CTb0). The coding tables used for b1 are 2 (CTb10, CTb11). Additionally, the coding table to be used is switched according to the value of b0 (0 to 1). The coding tables used for b2 are 4 (CTb20, CTb21, CTb22, CTb23). Furthermore, the coding table to be used is switched according to the values of b0 and b1 (0 to 3). The coding tables used for bn-1 are 2 n-1 (CTbn0, CTbn1, …, CTbn(2 n-1 -1)). Additionally, the coding table to be used is switched according to the value of b0b1…bn-2 (0 to 2 n-1 -1).

[0702] In addition, the three-dimensional data encoding device can also apply m-ary arithmetic coding (m = 2 n ) that sets values from 0 to 2 n -1 without binarizing the n-bit encoding. In addition, when the three-dimensional data encoding device performs arithmetic coding on the n-bit encoding using m-ary, the three-dimensional data decoding device can also restore the n-bit encoding through m-ary arithmetic decoding.

[0703] Fig.56 is a diagram for explaining the processing when, for example, the residual coding is an exponential Golomb code. As Fig.56 shown, the part where binarization is performed using exponential Golomb, that is, the residual coding, includes a prefix part and a suffix part. For example, the three-dimensional data encoding device switches the coding table in the prefix part and the suffix part. That is, the three-dimensional data encoding device performs arithmetic coding on each bit included in the prefix part using the coding table for the prefix, and performs arithmetic coding on each bit included in the suffix part using the coding table for the suffix.

[0704] In addition, the three-dimensional data encoding device can also update the occurrence probabilities of 0 and 1 in each encoding table according to the values of the actually generated binarized data. Alternatively, the three-dimensional data encoding device can also fix the occurrence probabilities of 0 and 1 in a certain encoding table. Thereby, the number of times of updating the occurrence probability can be suppressed, and thus the processing amount can be reduced. For example, the three-dimensional data encoding device can update the occurrence probability for the prefix part and fix the occurrence probability for the suffix part.

[0705] In addition, the three-dimensional data encoding device decodes the quantized prediction residual through inverse quantization and reconstruction, and uses the decoded prediction residual, that is, the decoded value, for prediction after the three-dimensional points to be encoded. Specifically, the three-dimensional data encoding device calculates the inverse quantization value by multiplying the quantized prediction residual (quantized value) by the quantization scale, and obtains the decoded value (reconstructed value) by adding the inverse quantization value and the predicted value.

[0706] For example, the inverse quantization value a2iq of point a2 is calculated by using the quantized value a2q of point a2 through (Equation A11). The inverse quantization value b2iq of point b2 is calculated by using the quantized value b2q of point b2 through (Equation A12). Here, QS_LoD0 is the QS for LoD0, and QS_LoD1 is the QS for LoD1. That is, the QS can be changed according to the LoD.

[0707] a2iq = a2q × QS_LoD0…(Equation A11)

[0708] b2iq = b2q × QS_LoD1…(Equation A12)

[0709] For example, as shown in (Equation A13), the decoded value a2rec of point a2 is calculated by adding the inverse quantization value a2iq of point a2 to the predicted value a2p of point a2. As shown in (Equation A14), the decoded value b2rec of point b2 is calculated by adding the inverse quantization value b2iq of point b2 to the predicted value b2p of point b2.

[0710] a2rec = a2iq + a2p…(Equation A13)

[0711] b2rec = b2iq + b2p…(Equation A14)

[0712] Hereinafter, a syntax example of the bitstream of the present embodiment will be described. Fig.57 is a diagram showing a syntax example of the attribute header of the present embodiment. The attribute header is the header information of the attribute information. As Fig.57As shown, the attribute header includes the number of levels information (NumLoD), the three-dimensional point number information (NumOfPoint[i]), the level threshold (Thres_Lod[i]), the surrounding point number information (NumNeighborPoint[i]), the prediction threshold (THd[i]), the quantization scale (QS[i]), and the binarization threshold (R_TH[i]).

[0713] The number of levels information (NumLoD) represents the number of levels of the LoD used.

[0714] The three-dimensional point number information (NumOfPoint[i]) represents the number of three-dimensional points belonging to level i. In addition, the three-dimensional data encoding device may also attach the three-dimensional point total number information (AllNumOfPoint) representing the total number of three-dimensional points to another header. In this case, the three-dimensional data encoding device may not attach NumOfPoint[NumLoD - 1] representing the number of three-dimensional points belonging to the bottommost level to the header. In this case, the three-dimensional data decoding device can calculate NumOfPoint[NumLoD - 1] through (Equation A15). Thus, the encoding amount of the header can be reduced.

[0715]

Equation 4

[0716]

[0717] The level threshold (Thres_Lod[i]) is a threshold for setting level i. The three-dimensional data encoding device and the three-dimensional data decoding device construct LoDi such that the distance between each point within LoDi is greater than the threshold Thres_LoD[i]. In addition, the three-dimensional data encoding device may not attach the value of Thres_Lod[NumLoD - 1] (the bottommost level) to the header. In this case, the three-dimensional data decoding device estimates the value of Thres_Lod[NumLoD - 1] as 0. Thus, the encoding amount of the header can be reduced.

[0718] The surrounding point number information (NumNeighborPoint[i]) represents the upper limit value of the number of surrounding points used in the generation of the predicted value of the three-dimensional points belonging to level i. When the number of surrounding points M is less than NumNeighborPoint[i] (M < NumNeighborPoint[i]), the three-dimensional data encoding device may also use M surrounding points to calculate the predicted value. In addition, when it is not necessary to separate the value of NumNeighborPoint[i] in each LoD, the three-dimensional data encoding device may attach 1 surrounding point number information (NumNeighborPoint) used in all LoDs to the header.

[0719] The prediction threshold (THd[i]) represents the upper limit value of the distance between the surrounding three-dimensional points used in the prediction of the three-dimensional points of the object to be encoded or decoded at level i and the three-dimensional points of the object. The three-dimensional data encoding device and the three-dimensional data decoding device do not use three-dimensional points whose distance from the three-dimensional points of the object is farther than THd[i] for prediction. Additionally, when it is not necessary to separate the values of THd[i] for each LoD, the three-dimensional data encoding device may also attach one prediction threshold (THd) used in all LoDs to the header.

[0720] The quantization scale (QS[i]) represents the quantization scale used in quantization and inverse quantization at level i.

[0721] The binarization threshold (R_TH[i]) is a threshold for switching the binarization method of the prediction residual of the three-dimensional points belonging to level i. For example, when the prediction residual is less than the threshold R_TH, the three-dimensional data encoding device binarizes the prediction residual pu with a fixed number of bits, and when the prediction residual is equal to or greater than the threshold R_TH, it binarizes the binarized data of the threshold R_TH and the value of (pu - R_TH) using exponential Golomb. Additionally, when it is not necessary to switch the values of R_TH[i] for each LoD, the three-dimensional data encoding device may also attach one binarization threshold (R_TH) used in all LoDs to the header.

[0722] Furthermore, R_TH[i] may also be the maximum value represented by nbit. For example, in 6bit, R_TH is 63, and in 8bit, R_TH is 255. Additionally, instead of encoding the maximum value represented by nbit as the binarization threshold, the three-dimensional data encoding device may encode the number of bits. For example, the three-dimensional data encoding device may attach the value 6 to the header when R_TH[i] = 63, and attach the value 8 to the header when R_TH[i] = 255. Additionally, the three-dimensional data encoding device may also define the minimum value (minimum number of bits) representing the number of bits of R_TH[i] and attach the relative number of bits based on the minimum value to the header. For example, the three-dimensional data encoding device may attach the value 0 to the header when R_TH[i] = 63 and the minimum number of bits is 6, and attach the value 2 to the header when R_TH[i] = 255 and the minimum number of bits is 6.

[0723] Additionally, the three-dimensional data encoding device may also perform entropy encoding on at least one of NumLoD, Thres_Lod[i], NumNeighborPoint[i], THd[i], QS[i], and R_TH[i] and attach it to the header. For example, the three-dimensional data encoding device may also perform arithmetic encoding by binarizing each value. Additionally, in order to suppress the processing amount, the three-dimensional data encoding device may also encode each value with a fixed length.

[0724] In addition, the three-dimensional data encoding device may not attach at least one of NumLoD, Thres_Lod[i], NumNeighborPoint[i], THd[i], QS[i], and R_TH[i] to the header. For example, the value of at least one of them may also be specified by a profile or level of a standard or the like. As a result, the bit amount of the header can be reduced.

[0725] Fig.58 FIG. is a diagram showing a syntax example of attribute data (attribute_data) related to the present embodiment. The attribute data includes encoded data of attribute information of a plurality of three-dimensional points. As Fig.58 shown, the attribute data includes an n-bit code and a remaining code.

[0726] The n-bit code is encoded data of a prediction residual of the value of the attribute information or a part thereof. The bit length of the n-bit code depends on the value of R_TH[i]. For example, when the value shown by R_TH[i] is 63, the n-bit code is 6 bits, and when the value shown by R_TH[i] is 255, the n-bit code is 8 bits.

[0727] The remaining code is the encoded data after exponential Golomb coding in the encoded data of the prediction residual of the value of the attribute information. When the n-bit code is the same as R_TH[i], the remaining code is encoded or decoded. In addition, the three-dimensional data decoding device adds the value of the n-bit code and the value of the remaining code to decode the prediction residual. In addition, when the n-bit code is not the same value as R_TH[i], the remaining code may not be encoded or decoded.

[0728] Hereinafter, the flow of processing in the three-dimensional data encoding device will be described. Fig.59 FIG. is a flowchart of three-dimensional data encoding processing performed by the three-dimensional data encoding device.

[0729] First, the three-dimensional data encoding device encodes the position information (geometry) (S3001). For example, the three-dimensional data encoding device performs encoding using an octree representation.

[0730] After the three-dimensional data encoding device encodes the position information, when the positions of the three-dimensional points change due to quantization or the like, the three-dimensional data encoding device reassigns the attribute information of the original three-dimensional points to the changed three-dimensional points (S3002). For example, the three-dimensional data encoding device performs reassignment by interpolating the values of the attribute information according to the amount of change in the position. For example, the three-dimensional data encoding device detects N three-dimensional points before the change that are close to the changed three-dimensional position, and performs weighted averaging on the values of the attribute information of the N three-dimensional points. For example, in the weighted averaging, the three-dimensional data encoding device determines the weights based on the distances from the changed three-dimensional position to each of the N three-dimensional points. Then, the three-dimensional data encoding device determines the value obtained by the weighted averaging as the value of the attribute information of the changed three-dimensional point. In addition, when two or more three-dimensional points change to the same three-dimensional position due to quantization or the like, the three-dimensional data encoding device may also assign the average value of the attribute information of the two or more three-dimensional points before the change as the value of the attribute information of the changed three-dimensional point.

[0731] Next, the three-dimensional data encoding device encodes the reassigned attribute information (S3003). For example, when encoding a plurality of pieces of attribute information, the three-dimensional data encoding device may also encode the plurality of pieces of attribute information in sequence. For example, when encoding color and reflectance as the attribute information, the three-dimensional data encoding device may also generate a bit stream in which the encoding result of the reflectance is appended after the encoding result of the color. In addition, the order of the plurality of encoding results of the attribute information appended to the bit stream is not limited to this order and may be any order.

[0732] In addition, the three-dimensional data encoding device may also append information indicating the start position of the encoded data of each attribute information in the bit stream to the header or the like. As a result, the three-dimensional data decoding device can selectively decode the attribute information that needs to be decoded, and thus can omit the decoding process of the attribute information that does not need to be decoded. Therefore, the processing amount of the three-dimensional data decoding device can be reduced. In addition, the three-dimensional data encoding device may also encode a plurality of pieces of attribute information in parallel and merge the encoding results into one bit stream. As a result, the three-dimensional data encoding device can encode a plurality of pieces of attribute information at high speed.

[0733] Fig.60 It is a flowchart of the attribute information encoding process (S3003). First, the three-dimensional data encoding device sets the LoD (S3011). That is, the three-dimensional data encoding device assigns each three-dimensional point to any one of a plurality of LoDs.

[0734] Next, the three-dimensional data encoding device starts a loop in units of LoD (S3012). That is, the three-dimensional data encoding device repeatedly performs the processes of steps S3013 to S3021 for each LoD.

[0735] Next, the three-dimensional data encoding device starts a loop in units of three-dimensional points (S3013). That is, the three-dimensional data encoding device repeatedly performs the processes of steps S3014 to S3020 for each three-dimensional point.

[0736] First, the three-dimensional data encoding device searches for a plurality of surrounding points, which are three-dimensional points existing around the object three-dimensional point used in the calculation of the predicted value of the object three-dimensional point to be processed (S3014). Next, the three-dimensional data encoding device calculates the weighted average of the values of the attribute information of the plurality of surrounding points, and sets the obtained value as the predicted value P (S3015). Next, the three-dimensional data encoding device calculates the difference between the attribute information of the object three-dimensional point and the predicted value, that is, the prediction residual (S3016). Next, the three-dimensional data encoding device calculates the quantization value by quantizing the prediction residual (S3017). Next, the three-dimensional data encoding device performs arithmetic coding on the quantization value (S3018).

[0737] In addition, the three-dimensional data encoding device calculates the inverse quantization value by inverse quantizing the quantization value (S3019). Next, the three-dimensional data encoding device generates the decoded value by adding the inverse quantization value to the predicted value (S3020). Next, the three-dimensional data encoding device ends the loop in units of three-dimensional points (S3021). In addition, the three-dimensional data encoding device ends the loop in units of LoD (S3022).

[0738] Hereinafter, the three-dimensional data decoding process in the three-dimensional data decoding device that decodes the bitstream generated by the above three-dimensional data encoding device will be described.

[0739] The three-dimensional data decoding device generates the decoded binarized data by performing arithmetic decoding on the binarized data of the attribute information in the bitstream generated by the three-dimensional data encoding device in the same manner as the three-dimensional data encoding device. In addition, in the three-dimensional data encoding device, when the application method of arithmetic coding is switched between the part binarized with n bits (n-bit coding) and the part binarized with exponential Golomb (remaining coding), the three-dimensional data decoding device performs decoding accordingly when applying arithmetic decoding.

[0740] For example, in the arithmetic decoding method of n-bit encoding, a three-dimensional data decoding device performs arithmetic decoding on each bit using a different encoding table (decoding table). At this time, the three-dimensional data decoding device can also change the number of encoding tables used for each bit. For example, for the first bit b0 of the n-bit encoding, 1 encoding table is used for arithmetic decoding. In addition, the three-dimensional data decoding device uses 2 encoding tables for the next bit b1. Furthermore, the three-dimensional data decoding device switches the encoding table used in the arithmetic decoding of bit b1 according to the value (0 or 1) of b0. Similarly, the three-dimensional data decoding device further uses 4 encoding tables for the next bit b2. In addition, the three-dimensional data decoding device switches the encoding table used in the arithmetic decoding of bit b2 according to the values (0 to 3) of b0 and b1.

[0741] In this way, when the three-dimensional data decoding device performs arithmetic decoding on each bit bn-1 of the n-bit encoding, it uses 2 n-1 encoding tables. In addition, the three-dimensional data decoding device switches the encoding table used according to the values (occurrence patterns) of the bits before bn-1. Thereby, the three-dimensional data decoding device can use an appropriate encoding table for each bit to appropriately decode a bit stream with improved encoding efficiency.

[0742] In addition, the three-dimensional data decoding device can also reduce the number of encoding tables used for each bit. For example, the three-dimensional data decoding device can also switch 2 m encoding tables according to the values (occurrence patterns) of the m bits (m < n-1) before bn-1 when performing arithmetic decoding on each bit bn-1. Thereby, the three-dimensional data decoding device can appropriately decode a bit stream with improved encoding efficiency while suppressing the number of encoding tables used for each bit. In addition, the three-dimensional data decoding device can also update the occurrence probabilities of 0 and 1 in each encoding table according to the values of the actually generated binarized data. In addition, the three-dimensional data decoding device can also fix the occurrence probabilities of 0 and 1 in the encoding tables of a part of the bits. Thereby, the number of times of updating the occurrence probability can be suppressed, and thus the processing amount can be reduced.

[0743] For example, when the n-bit encoding is b0b1b2…bn-1, the encoding table used for b0 is 1 (CTb0). The encoding tables used for b1 are 2 (CTb10, CTb11). In addition, the encoding table is switched according to the value (0 to 1) of b0. The encoding tables used for b2 are 4 (CTb20, CTb21, CTb22, CTb23). In addition, the encoding table is switched according to the values (0 to 3) of b0 and b1. The encoding tables used for bn-1 are 2 n-1 (CTbn0, CTbn1, …, CTbn(2 n-1 -1)). In addition, according to the values (0 to 2 n-1-1) to switch the code table.

[0744] For example, Fig.61 is a diagram for explaining the processing in the case where the remaining code is the Exponential Golomb code. As Fig.61 shown, the part (remaining code) binarized and encoded by the three-dimensional data encoding device using the Exponential Golomb code includes a prefix part and a suffix part. For example, the three-dimensional data decoding device switches the code table between the prefix part and the suffix part. That is, the three-dimensional data decoding device performs arithmetic decoding on each bit included in the prefix part using the prefix code table, and performs arithmetic decoding on each bit included in the suffix part using the suffix code table.

[0745] In addition, the three-dimensional data decoding device may update the occurrence probabilities of 0 and 1 in each code table according to the value of the binarized data generated during decoding. Alternatively, the three-dimensional data decoding device may fix the occurrence probabilities of 0 and 1 in a certain code table. Thereby, the number of updates of the occurrence probabilities can be suppressed, and thus the processing amount can be reduced. For example, the three-dimensional data decoding device may update the occurrence probability for the prefix part and fix the occurrence probability for the suffix part.

[0746] In addition, the three-dimensional data decoding device multivalues the binarized data of the predicted residual obtained by arithmetic decoding in accordance with the coding method used in the three-dimensional data encoding device, thereby decoding the quantized predicted residual (unsigned integer value). The three-dimensional data decoding device first calculates the value of the n-bit code decoded by performing arithmetic decoding on the binarized data encoded by n bits. Next, the three-dimensional data decoding device compares the value of the n-bit code with the value of R_TH.

[0747] When the value of the n-bit code is equal to the value of R_TH, the three-dimensional data decoding device determines that there are bits encoded by the Exponential Golomb code next, and performs arithmetic decoding on the remaining code, which is the binarized data encoded by the Exponential Golomb code. Then, the three-dimensional data decoding device calculates the value of the remaining code using the inverse table showing the relationship between the remaining code and the value. Fig.62 is a diagram showing an example of the inverse table representing the relationship between the remaining code and its value. Next, the three-dimensional data decoding device obtains the multivalued quantized predicted residual by adding the value of the obtained remaining code to R_TH.

[0748] On the other hand, when the value of the n-bit encoding does not match the value of R_TH (the value is less than R_TH), the three-dimensional data decoding device directly determines the value of the n-bit encoding as the quantized prediction residual after multi-valued quantization. Thus, the three-dimensional data decoding device can appropriately decode the bitstream generated by switching the binarization method according to the value of the prediction residual in the three-dimensional data encoding device.

[0749] In addition, when the threshold R_TH is attached to the head of the bitstream or the like, the three-dimensional data decoding device can also decode the value of the threshold R_TH from the head and use the decoded value of the threshold R_TH to switch the decoding method. Additionally, when the threshold R_TH is attached to the head or the like for each LoD, the three-dimensional data decoding device switches the decoding method for each LoD using the decoded threshold R_TH.

[0750] For example, when the threshold R_TH is 63 and the decoded value of the n-bit encoding is 63, the three-dimensional data decoding device decodes the remaining encoding using exponential Golomb to obtain the value of the remaining encoding. For example, in Fig.62 the example shown, the remaining encoding is 00100, and 3 is obtained as the value of the remaining encoding. Then, the three-dimensional data decoding device adds the value 63 of the threshold R_TH and the value 3 of the remaining encoding to obtain the value 66 of the prediction residual.

[0751] In addition, when the decoded value of the n-bit encoding is 32, the three-dimensional data decoding device sets the value 32 of the n-bit encoding as the value of the prediction residual.

[0752] In addition, the three-dimensional data decoding device transforms the decoded quantized prediction residual from an unsigned integer value to a signed integer value through, for example, a process opposite to that in the three-dimensional data encoding device. Thus, when performing entropy coding on the prediction residual, the three-dimensional data decoding device can appropriately decode the bitstream generated without considering the generation of negative integers. Additionally, the three-dimensional data decoding device does not necessarily need to transform the unsigned integer value to a signed integer value. For example, when decoding a bitstream generated by separately performing entropy coding on the sign bit, it can also decode the sign bit.

[0753] The three-dimensional data decoding device decodes the quantized prediction residual transformed into a signed integer value through inverse quantization and reconstruction, thereby generating a decoded value. In addition, the three-dimensional data decoding device uses the generated decoded value for prediction after the three-dimensional point to be decoded. Specifically, the three-dimensional data decoding device calculates the inverse quantization value by multiplying the quantized prediction residual by the decoded quantization scale, and adds the inverse quantization value and the prediction value to obtain the decoded value.

[0754] The decoded unsigned integer value (unsigned quantization value) is transformed into a signed integer value through the following process. When the least significant bit (LSB) of the decoded unsigned integer value a2u is 1, the 3D data decoding device sets the signed integer value a2q to -((a2u + 1) >> 1). When the LSB of the unsigned integer value a2u is not 1, the 3D data decoding device sets the signed integer value a2q to (a2u >> 1).

[0755] Similarly, when the LSB of the decoded unsigned integer value b2u is 1, the 3D data decoding device sets the signed integer value b2q to -((b2u + 1) >> 1). When the LSB of the unsigned integer value n2u is not 1, the 3D data decoding device sets the signed integer value b2q to (b2u >> 1).

[0756] In addition, the details of the inverse quantization and reconstruction processing performed by the 3D data decoding device are the same as those of the inverse quantization and reconstruction processing in the 3D data encoding device.

[0757] Hereinafter, the processing flow in the 3D data decoding device will be described. Fig.63 This is a flowchart of the 3D data decoding process performed by the 3D data decoding device. First, the 3D data decoding device decodes the position information (geometry) from the bitstream (S3031). For example, the 3D data decoding device performs decoding using an octree representation.

[0758] Next, the 3D data decoding device decodes the attribute information (Attribute) from the bitstream (S3032). For example, when decoding multiple pieces of attribute information, the 3D data decoding device may decode the multiple pieces of attribute information sequentially. For example, when decoding color and reflectance as attribute information, the 3D data decoding device decodes the encoding result of the color and the encoding result of the reflectance in the order attached to the bitstream. For example, when the encoding result of the reflectance is attached after the encoding result of the color in the bitstream, the 3D data decoding device decodes the encoding result of the color and then decodes the encoding result of the reflectance. In addition, the 3D data decoding device can decode the encoding results of the attribute information attached to the bitstream in any order.

[0759] In addition, the three-dimensional data decoding device can also obtain information indicating the start position of the representation encoded data of each attribute information in the bitstream by decoding the header and the like. Thus, the three-dimensional data decoding device can selectively decode the attribute information that needs to be decoded, and therefore can omit the decoding process of the attribute information that does not need to be decoded. Therefore, the processing amount of the three-dimensional data decoding device can be reduced. In addition, the three-dimensional data decoding device can also decode a plurality of attribute information in parallel and merge the decoding results into one three-dimensional point cloud. Thus, the three-dimensional data decoding device can decode a plurality of attribute information at high speed.

[0760] Fig.64 It is a flowchart of the attribute information decoding process (S3032). First, the three-dimensional data decoding device sets the LoD (S3041). That is, the three-dimensional data decoding device assigns each of the plurality of three-dimensional points having the decoded position information to any one of the plurality of LoDs. For example, this assignment method is the same method as the assignment method used in the three-dimensional data encoding device.

[0761] Next, the three-dimensional data decoding device starts a loop in units of LoD (S3042). That is, the three-dimensional data decoding device repeatedly performs the processes of steps S3043 to S3049 for each LoD.

[0762] Next, the three-dimensional data decoding device starts a loop in units of three-dimensional points (S3043). That is, the three-dimensional data decoding device repeatedly performs the processes of steps S3044 to S3048 for each three-dimensional point.

[0763] First, the three-dimensional data decoding device searches for a plurality of surrounding points, which are three-dimensional points existing around the object three-dimensional point used in the calculation of the predicted value of the object three-dimensional point to be processed (S3044). Next, the three-dimensional data decoding device calculates the weighted average of the values of the attribute information of the plurality of surrounding points and sets the obtained value as the predicted value P (S3045). In addition, these processes are the same as the processes in the three-dimensional data encoding device.

[0764] Next, the three-dimensional data decoding device performs arithmetic decoding on the quantization value from the bitstream (S3046). In addition, the three-dimensional data decoding device calculates the inverse quantization value by inverse quantizing the decoded quantization value (S3047). Next, the three-dimensional data decoding device generates a decoded value by adding the inverse quantization value to the predicted value (S3048). Next, the three-dimensional data decoding device ends the loop in units of three-dimensional points (S3049). In addition, the three-dimensional data decoding device ends the loop in units of LoD (S3050).

[0765] Next, the structures of the three-dimensional data encoding device and the three-dimensional data decoding device of the present embodiment will be described. Fig.65FIG. 0 is a block diagram showing the structure of a three-dimensional data encoding device 3000 according to the present embodiment. The three-dimensional data encoding device 3000 includes a position information encoding unit 3001, an attribute information redistribution unit 3002, and an attribute information encoding unit 3003.

[0766] The attribute information encoding unit 3003 encodes the position information (geometry) of a plurality of three-dimensional points included in the input point cloud. The attribute information redistribution unit 3002 redistributes the values of the attribute information of the plurality of three-dimensional points included in the input point cloud using the encoding and decoding results of the position information. The attribute information encoding unit 3003 encodes the redistributed attribute information (attribute). In addition, the three-dimensional data encoding device 3000 generates a bitstream including the encoded position information and the encoded attribute information.

[0767] Fig.66 FIG. 7 is a block diagram showing the structure of a three-dimensional data decoding device 3010 according to the present embodiment. The three-dimensional data decoding device 3010 includes a position information decoding unit 3011 and an attribute information decoding unit 3012.

[0768] The position information decoding unit 3011 decodes the position information (geometry) of a plurality of three-dimensional points from the bitstream. The attribute information decoding unit 3012 decodes the attribute information (attribute) of the plurality of three-dimensional points from the bitstream. In addition, the three-dimensional data decoding device 3010 generates an output point cloud by combining the decoded position information and the decoded attribute information.

[0769] As described above, the three-dimensional data encoding device according to the present embodiment performs Fig.67 the processing shown in FIG. The three-dimensional data encoding device encodes three-dimensional points having attribute information. First, the three-dimensional data encoding device calculates a predicted value of the attribute information of the three-dimensional points (S3061). Next, the three-dimensional data encoding device calculates the difference between the attribute information of the three-dimensional points and the predicted value, that is, the prediction residual (S3062). Next, the three-dimensional data encoding device generates binary data by binarizing the prediction residual (S3063). Next, the three-dimensional data encoding device performs arithmetic coding on the binary data (S3064).

[0770] Thereby, the three-dimensional data encoding device can reduce the amount of encoded data of the attribute information by calculating the prediction residual of the attribute information and then binarizing and arithmetically coding the prediction residual.

[0771] For example, in the arithmetic coding (S3064), the three-dimensional data encoding device uses different coding tables for each bit of the binary data. Thereby, the three-dimensional data encoding device can improve the coding efficiency.

[0772] For example, in arithmetic coding (S3064), the lower bits of the binary data use a larger number of coding tables.

[0773] For example, in arithmetic coding (S3064), the three-dimensional data coding device selects the coding table used in the arithmetic coding of the target bit according to the value of the upper bit of the target bit included in the binary data. Thus, the three-dimensional data coding device can select the coding table according to the value of the upper bit, and thus can improve the coding efficiency.

[0774] For example, in binarization (S3063), when the prediction residual is less than the threshold (R_TH), the three-dimensional data coding device generates binary data by binarizing the prediction residual with a fixed number of bits. When the prediction residual is equal to or greater than the threshold (R_TH), the three-dimensional data coding device generates binary data including a first code (n-bit code) representing the threshold (R_TH) with a fixed number of bits and a second code (remaining code) obtained by binarizing the value obtained by subtracting the threshold (R_TH) from the prediction residual using exponential Golomb. The three-dimensional data coding device uses different arithmetic coding methods for the first code and the second code in arithmetic coding (S3064).

[0775] Thus, the three-dimensional data coding device can, for example, perform arithmetic coding on the first code and the second code by arithmetic coding methods respectively suitable for the first code and the second code, and thus can improve the coding efficiency.

[0776] For example, the three-dimensional data coding device quantizes the prediction residual and binarizes the quantized prediction residual in binarization (S3063). The threshold (R_TH) is changed according to the quantization scale in quantization. Thus, the three-dimensional data coding device can use an appropriate threshold corresponding to the quantization scale, and thus can improve the coding efficiency.

[0777] For example, the second code includes a prefix part and a suffix part. The three-dimensional data coding device uses different coding tables for the prefix part and the suffix part in arithmetic coding (S3064). Thus, the three-dimensional data coding device can improve the coding efficiency.

[0778] For example, the three-dimensional data coding device includes a processor and a memory, and the processor uses the memory to perform the above processing.

[0779] In addition, the three-dimensional data decoding device of the present embodiment performs Fig.68The processing shown below. The 3D data decoding device decodes 3D points with attribute information. First, the 3D data decoding device calculates a predicted value of the attribute information of the 3D points (S3071). Next, the 3D data decoding device generates binary data by performing arithmetic decoding on the encoded data included in the bitstream (S3072). Next, the 3D data decoding device generates a prediction residual by multi-valuing the binary data (S3073). Next, the 3D data decoding device calculates a decoded value of the attribute information of the 3D points by adding the predicted value and the prediction residual (S3074).

[0780] Thus, the 3D data decoding device can calculate the prediction residual of the attribute information, and further appropriately decode the bitstream of the attribute information generated by binarizing and arithmetic encoding the prediction residual.

[0781] For example, in the arithmetic decoding (S3072), the 3D data decoding device uses different coding tables for each bit of the binary data. Thus, the 3D data decoding device can appropriately decode the bitstream with improved coding efficiency.

[0782] For example, in the arithmetic decoding (S3072), the more lower-order bits of the binary data, the larger the number of coding tables used.

[0783] For example, in the arithmetic decoding (S3072), the 3D data decoding device selects the coding table used in the arithmetic decoding of the target bit according to the value of the upper-order bit of the target bit included in the binary data. Thus, the 3D data decoding device can appropriately decode the bitstream with improved coding efficiency.

[0784] For example, in the multi-valuing (S3073), the 3D data decoding device generates a first value by multi-valuing the first coding (n-bit coding) with a fixed number of bits included in the binary data. When the first value is less than the threshold (R_TH), the 3D data decoding device determines the first value as the prediction residual. When the first value is equal to or greater than the threshold (R_TH), the 3D data decoding device generates a second value by multi-valuing the exponential Golomb code, i.e., the second coding (remaining coding) included in the binary data, and adds the first value and the second value to generate the prediction residual. The 3D data decoding device uses different arithmetic decoding methods for the first coding and the second coding in the arithmetic decoding (S3072).

[0785] Thus, the 3D data decoding device can appropriately decode the bitstream with improved coding efficiency.

[0786] For example, the three-dimensional data decoding device performs inverse quantization on the prediction residual, and in the addition operation (S3074), adds the predicted value and the inversely quantized prediction residual. The threshold value (R_TH) is changed according to the quantization scale in the inverse quantization. Thus, the three-dimensional data decoding device can appropriately decode the bitstream with improved coding efficiency.

[0787] For example, the second coding includes a prefix part and a suffix part. The three-dimensional data decoding device uses different coding tables for the prefix part and the suffix part in the arithmetic decoding (S3072). Thus, the three-dimensional data decoding device can appropriately decode the bitstream with improved coding efficiency.

[0788] For example, the three-dimensional data decoding device includes a processor and a memory, and the processor uses the memory to perform the above processing.

[0789] (Embodiment 9)

[0790] The predicted value may also be generated by a method different from that of Embodiment 8. Hereinafter, the three-dimensional points to be encoded may sometimes be referred to as the first three-dimensional points, and the surrounding three-dimensional points may be referred to as the second three-dimensional points.

[0791] For example, in the generation of the predicted value of the attribute information of the three-dimensional points, the attribute value of the three-dimensional point closest in distance among the encoded and decoded surrounding three-dimensional points of the three-dimensional point to be encoded may be directly used as the predicted value. In addition, in the generation of the predicted value, prediction mode information (PredMode) may be attached to each three-dimensional point, and the predicted value may be generated by selecting one predicted value from among a plurality of predicted values. That is, for example, it may be considered that in a total of M prediction modes, the average value is assigned to prediction mode 0, the attribute value of three-dimensional point A is assigned to prediction mode 1,..., the attribute value of three-dimensional point Z is assigned to prediction mode M - 1, and the prediction mode used in the prediction is attached to the bitstream for each three-dimensional point. In this way, it may also be that the first prediction mode value indicating the first prediction mode in which the average of the attribute information of the surrounding three-dimensional points is calculated as the predicted value is less than the second prediction mode value indicating the second prediction mode in which the attribute information of the surrounding three-dimensional points itself is calculated as the predicted value. Here, the predicted value "average value" calculated in prediction mode 0 is the average of the attribute values of the surrounding three-dimensional points of the three-dimensional point to be encoded.

[0792] Fig.69 It is a first example of a diagram showing the predicted values calculated in each prediction mode of Embodiment 9. Fig.70 It is a diagram showing an example of the attribute information used in the predicted value of Embodiment 9. Fig.71 It is a second example of a diagram showing the predicted values calculated in each prediction mode of Embodiment 9.

[0793] The number of prediction modes M can also be appended to the bitstream. Additionally, the number of prediction modes M may not be appended to the bitstream, and the value may be specified by the standard profile, level, etc. Moreover, the number of prediction modes M can also use a value calculated based on the number of three-dimensional points N used in the prediction. For example, the number of prediction modes M can be calculated by M = N + 1.

[0794] In addition, Fig.69 The table shown is an example where the number of three-dimensional points N used in the prediction is 4, and the number of prediction modes M is 5. The attribute information of point b2 can be predicted using the attribute information of points a0, a1, a2, and b1. When selecting one prediction mode from multiple prediction modes, the prediction mode that generates the attribute values of points a0, a1, a2, and b1 as prediction values can also be selected based on the distance information from point b2 to each of points a0, a1, a2, and b1. A prediction mode is appended to each three-dimensional point of the coding object. The prediction value is calculated based on the value corresponding to the appended prediction mode.

[0795] Fig.71 The table shown is the same as Fig.69 Similarly, it is an example when the number of three-dimensional points N used in the prediction is 4, and the number of prediction modes M is 5. The predicted value of the attribute information of point a2 can be generated using the attribute information of points a0 and a1.

[0796] When selecting one prediction mode from multiple prediction modes, the prediction mode that generates the attribute values of points a0 and a1 as prediction values can also be selected based on the distance information from point a2 to each of points a0 and a1. A prediction mode is appended to each three-dimensional point of the coding object. The prediction value is calculated based on the value corresponding to the appended prediction mode.

[0797] Furthermore, in the case where the number of adjacent points, i.e., the number of surrounding three-dimensional points N, is less than 4 as in point a2 above, the prediction mode for which no prediction value is assigned in the table can be set to not available.

[0798] In addition, the assignment of the prediction mode values can also be determined in the order of the distance from the three-dimensional points of the coding object. For example, the closer the distance from the three-dimensional points of the coding object to the surrounding three-dimensional points with the attribute information used as the prediction value, the smaller the prediction mode value representing the multiple prediction modes. In Fig.69In the example, it means that the distances to point b2 of the three-dimensional points to be encoded are close in the order of points b1, a2, a1, and a0. For example, in the calculation of the predicted value, the attribute information of point b1 is calculated as the predicted value in the prediction mode where the prediction mode value in two or more prediction modes is as shown in "1", and the attribute information of point a2 is calculated as the predicted value in the prediction mode where the prediction mode value is as shown in "2". In this way, it indicates that the prediction mode value representing the prediction mode in which the attribute information of point b1 is calculated as the predicted value is smaller than the prediction mode value representing the prediction mode in which the attribute information of point a2 is calculated as the predicted value, and point a2 is located at a position farther from point b2 than point b1.

[0799] Thus, a small prediction mode value can be assigned to a certain point that is likely to be easily selected and is easy to predict due to its close distance, and the number of bits used to encode the prediction mode value can be reduced. In addition, small prediction mode values can also be preferentially assigned to three-dimensional points belonging to the same LoD as the three-dimensional points to be encoded.

[0800] Figure 72 It is a diagram showing a third example of a table indicating the predicted values calculated in each prediction mode of Embodiment 9. Specifically, the third example is an example where the attribute information used in the predicted value is a value based on the color information (YUV) of surrounding three-dimensional points. In this way, the attribute information for the predicted value can also be color information representing the color of the three-dimensional points.

[0801] As Figure 72 shown, the predicted value calculated in the prediction mode where the prediction mode value is "0" is the average of the respective components of YUV that define the YUV color space. Specifically, this predicted value includes: the weighted average Yave of the Y component values Yb1, Ya2, Ya1, and Ya0 corresponding to points b1, a2, a1, and a0 respectively; the weighted average Uave of the U component values Ub1, Ua2, Ua1, and Ua0 corresponding to points b1, a2, a1, and a0 respectively; and the weighted average Vave of the V component values Vb1, Va2, Va1, and Va0 corresponding to points b1, a2, a1, and a0 respectively. In addition, the predicted values calculated in the prediction modes where the prediction mode values are "1" to "4" respectively include the color information of the surrounding three-dimensional points b1, a2, a1, and a0. The color information is represented by a combination of the values of the Y component, U component, and V component.

[0802] In addition, in Figure 72 , the color information is represented by values defined by the YUV color space, but is not limited to the YUV color space, and can also be represented by values defined by the RGB color space or values defined by other color spaces.

[0803] Thus, it is also possible that in the calculation of the predicted value, two or more averages or attribute information are calculated as the predicted value of the prediction mode. Additionally, the two or more averages or attribute information may also respectively represent the values of two or more components that define a color space.

[0804] In addition, for example, when Figure 72 the prediction mode shown by the prediction mode value "2" is selected in the table of, it is also possible to use the Y component, U component, and V component of the attribute value of the three-dimensional point of the coding object as the predicted values Ya2, Ua2, Va2 respectively for coding. In this case, the "2" as the prediction mode value is appended to the bitstream.

[0805] Figure 73 FIG. is a fourth example of a table showing the predicted values calculated in each prediction mode of Embodiment 9. Specifically, the fourth example is an example where the attribute information used in the predicted value is a value based on the reflectance information of surrounding three-dimensional points. The reflectance information is, for example, information indicating the reflectance R.

[0806] As Figure 73 shown, the predicted value calculated in the prediction mode shown by the prediction mode value "0" is the weighted average Rave of the reflectances Rb1, Ra2, Ra1, Ra0 corresponding to the points b1, a2, a1, a0 respectively. Additionally, the predicted values calculated in the prediction modes shown by the prediction mode values "1" to "4" are the reflectances Rb1, Ra2, Ra1, Ra0 of the surrounding three-dimensional points b1, a2, a1, a0 respectively.

[0807] In addition, for example, when Figure 73 the prediction mode shown by the prediction mode value "3" is selected in the table of, it is also possible to use the reflectance of the attribute value of the three-dimensional point of the coding object as the predicted value Ra1 for coding. In this case, the "3" as the prediction mode value is appended to the bitstream.

[0808] As Figure 72 and Figure 73 shown, the attribute information may include first attribute information and second attribute information of a different type from the first attribute information. The first attribute information is, for example, color information. The second attribute information is, for example, reflectance information. In the calculation of the predicted value, it is also possible to use the first attribute information to calculate the first predicted value and use the second attribute information to calculate the second predicted value.

[0809] (Embodiment 10)

[0810] Next, as another method for encoding the attribute information of three-dimensional points, a method using RAHT (Region Adaptive Hierarchical Transform) will be described. Figure 74 This is a diagram for explaining the encoding of attribute information using RAHT.

[0811] First, the three-dimensional data encoding device generates a Morton code based on the position information of the three-dimensional points, and sorts the attribute information of the three-dimensional points in the order of the Morton code. For example, the three-dimensional data encoding device can sort in ascending order of the Morton code. In addition, the sorting order is not limited to the Morton code order, and other orders can also be used.

[0812] Next, the three-dimensional data encoding device generates a high-frequency component and a low-frequency component of level L by applying the Haar transform to the attribute information of two adjacent three-dimensional points in the order of the Morton code. For example, the three-dimensional data encoding device can also use the Haar transform of a 2×2 matrix. The generated high-frequency component is included in the encoding coefficients as the high-frequency component of level L, and the generated low-frequency component is used as the input value for the upper level L+1.

[0813] After generating the high-frequency component of level L using the attribute information of level L, the three-dimensional data encoding device continues the processing of level L+1. In the processing of level L+1, the three-dimensional data encoding device generates a high-frequency component and a low-frequency component of level L+1 by applying the Haar transform to the two low-frequency components obtained by the Haar transform of the attribute information of level L. The generated high-frequency component is included in the encoding coefficients as the high-frequency component of level L+1, and the generated low-frequency component is used as the input value for the upper level L+2 of level L+1.

[0814] The three-dimensional data encoding device repeatedly performs such hierarchical processing, and when the low-frequency component input to the level becomes 1, it is determined that the top level Lmax has been reached. The three-dimensional data encoding device includes the low-frequency component of level Lmax-1 input to level Lmax in the encoding coefficients. Then, the values of the low-frequency components or high-frequency components included in the encoding coefficients are quantized and encoded using entropy encoding or the like.

[0815] In addition, when there is only one three-dimensional point as the two adjacent three-dimensional points when applying the Haar transform, the three-dimensional data encoding device can also use the value of the attribute information of the existing one three-dimensional point as the input value for the upper level.

[0816] In this way, the three-dimensional data encoding device hierarchically applies the Haar transform to the input attribute information to generate the high-frequency component and the low-frequency component of the attribute information, and performs encoding by using quantization and the like described later. Thereby, the encoding efficiency can be improved.

[0817] In the case where the attribute information is N-dimensional, the three-dimensional data encoding device may also independently apply the Haar transform for each dimension to calculate respective encoding coefficients. For example, in the case where the attribute information is color information (such as RGB or YUV), the three-dimensional data encoding device applies the Haar transform for each component to calculate respective encoding coefficients.

[0818] The three-dimens...

Claims

1. A three-dimensional data encoding method, wherein, calculate a plurality of coefficient values according to a plurality of attribute information of a plurality of three-dimensional points included in point cloud data, generate a plurality of quantization values by quantizing each of the plurality of coefficient values, generate a bitstream including the plurality of quantization values, the plurality of coefficient values belong to a certain layer among a plurality of layers, in the quantization, for each of the plurality of coefficient values, use the quantization parameter for the layer to which the coefficient value belongs for quantization, calculate the quantization parameter for the layer respectively for the plurality of layers, the bitstream includes first information representing a reference quantization parameter and a plurality of second information for calculating the plurality of quantization parameters for the plurality of layers respectively according to the reference quantization parameter.

2. The three-dimensional data encoding method according to claim 1, wherein, the plurality of second information respectively represent the difference between the reference quantization parameter and the quantization parameter for the layer.

3. The three-dimensional data encoding method according to claim 1 or 2, wherein, the bitstream further includes a first flag indicating whether the plurality of second information is included in the bitstream.

4. The three-dimensional data encoding method according to claim 1 or 2, wherein, the bitstream further includes third information representing the number of the plurality of second information included in the bitstream.

5. The three-dimensional data encoding method according to claim 1 or 2, wherein, the plurality of three-dimensional points are classified into a certain layer among the plurality of layers based on the position information of the plurality of three-dimensional points.

6. The three-dimensional data encoding method according to claim 1 or 2, wherein, the plurality of coefficient values are generated by dividing each of the plurality of attribute information into high-frequency components and low-frequency components and hierarchically classifying them into the plurality of layers.

7. A three-dimensional data decoding method, wherein, using (i) first information representing a reference quantization parameter and (ii) a plurality of second information for calculating the plurality of quantization parameters for the plurality of layers respectively according to the reference quantization parameter included in the bitstream, calculate the quantization parameters for the plurality of layers respectively for the plurality of layers, generate a plurality of coefficient values by inverse quantizing each of the plurality of quantization values included in the bitstream using the quantization parameter for the layer to which the quantization value belongs among the calculated quantization parameters for the plurality of layers, calculate a plurality of attribute information of a plurality of three-dimensional points included in point cloud data according to the plurality of coefficient values.

8. The three-dimensional data decoding method according to claim 7, wherein, the plurality of second information respectively represent the difference between the reference quantization parameter and the quantization parameter for the layer.

9. The three-dimensional data decoding method according to claim 7 or 8, wherein, the bitstream further includes a first flag indicating whether the plurality of second information is included in the bitstream.

10. The three-dimensional data decoding method according to claim 7 or 8, wherein, the bitstream further includes third information representing the number of the plurality of second information included in the bitstream.

11. The three-dimensional data decoding method according to claim 7 or 8, wherein, The plurality of three-dimensional points are classified into one of the plurality of hierarchies based on the position information of the plurality of three-dimensional points.

12. The three-dimensional data decoding method according to claim 7 or 8, wherein, the plurality of coefficient values are generated by dividing each of the plurality of attribute information into high-frequency components and low-frequency components and hierarchically classifying them into the plurality of hierarchies.

13. A three-dimensional data encoding device, wherein, comprises: a processor; and a memory, the processor uses the memory, calculates a plurality of coefficient values based on a plurality of attribute information of a plurality of three-dimensional points included in point cloud data, generates a plurality of quantization values by quantizing each of the plurality of coefficient values, generates a bitstream including the plurality of quantization values, the plurality of coefficient values belong to one of the plurality of hierarchies, in the quantization, for each of the plurality of coefficient values, quantization is performed using a quantization parameter for the hierarchy to which the coefficient value belongs, the quantization parameters for the hierarchies are calculated respectively for the plurality of hierarchies, the bitstream includes first information indicating a reference quantization parameter and a plurality of second information for calculating the plurality of quantization parameters for the plurality of hierarchies respectively based on the reference quantization parameter.

14. A three-dimensional data decoding device, wherein, comprises: a processor; and a memory, the processor uses the memory, uses (i) first information indicating a reference quantization parameter and (ii) a plurality of second information for calculating the plurality of quantization parameters for the plurality of hierarchies respectively based on the reference quantization parameter included in the bitstream, and calculates the quantization parameters for the plurality of hierarchies respectively for the plurality of hierarchies, generates a plurality of coefficient values by inverse quantizing each of the plurality of quantization values included in the bitstream using the quantization parameter for the hierarchy to which the quantization value belongs among the calculated plurality of quantization parameters for the hierarchies, calculates a plurality of attribute information of a plurality of three-dimensional points included in point cloud data based on the plurality of coefficient values.

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