Point cloud data encoding device, point cloud data encoding method, point cloud data decoding device, and point cloud data decoding method

Abstract

A decoding method, according to embodiments, may comprise the steps of: decoding geometry data of point cloud data in a bitstream; and decoding attribute data of the point cloud data. An encoding method, according to embodiments, may comprise the steps of: encoding geometry data of point cloud data; and encoding attribute data of the point cloud data.

Description

Point cloud data encoding device, point cloud data encoding method, point cloud data decoding device and point cloud data decoding method

[0001] The embodiments relate to a method and apparatus for processing point cloud content.

[0002] Point cloud content is content represented as a point cloud, which is a set of points belonging to a coordinate system that represents three-dimensional space. Point cloud content can represent three-dimensional media and is used to provide various services such as VR (Virtual Reality), AR (Augmented Reality), MR (Mixed Reality), and autonomous driving services. However, representing point cloud content requires tens of thousands to hundreds of thousands of point data points. Therefore, a method is required to efficiently process a vast amount of point data.

[0003] The embodiments provide an apparatus and a method for efficiently processing point cloud data. The embodiments provide a method and apparatus for processing point cloud data to address latency and encoding / decoding complexity.

[0004] However, the scope of rights of the embodiments is not limited to the technical problems described above, and may be extended to other technical problems that a person skilled in the art can infer based on the entire content described.

[0005] A decoding method according to the embodiments may include the step of decoding geometry data of point cloud data within a bitstream; and the step of decoding attribute data of point cloud data. An encoding method according to the embodiments may include the step of encoding geometry data of point cloud data; and the step of encoding attribute data of point cloud data.

[0006] The device and method according to the embodiments can process point cloud data with high efficiency.

[0007] The device and method according to the embodiments can provide a high-quality point cloud service.

[0008] The device and method according to the embodiments can provide point cloud content for providing general-purpose services such as VR services and autonomous driving services.

[0009] Drawings are included to further understand the embodiments, and the drawings illustrate the embodiments along with descriptions related to the embodiments. For a better understanding of the various embodiments described below, one must refer to the description of the embodiments below in relation to the following drawings, which include parts corresponding to similar reference numerals throughout the drawings.

[0010] FIG. 1 shows an example of a point cloud content provision system according to embodiments.

[0011] FIG. 2 is a block diagram illustrating a point cloud content provision operation according to embodiments.

[0012] FIG. 3 shows an example of a point cloud encoder according to embodiments.

[0013] FIG. 4 shows examples of octree and occupancy codes according to embodiments.

[0014] Figure 5 shows an example of a point configuration by LOD according to embodiments.

[0015] Figure 6 shows an example of a point configuration by LOD according to embodiments.

[0016] FIG. 7 shows an example of a point cloud decoder according to embodiments.

[0017] FIG. 8 is an example of a transmission device according to embodiments.

[0018] FIG. 9 is an example of a receiving device according to embodiments.

[0019] FIG. 10 shows an example of a structure that can be linked with a point cloud data transmission / reception method / device according to embodiments.

[0020] FIG. 11 shows an encoder according to embodiments.

[0021] FIG. 12 shows the encoding of attribute data according to embodiments.

[0022] FIG. 13 shows a decoder according to embodiments.

[0023] FIG. 14 illustrates the decoding of attribute data according to embodiments.

[0024] FIG. 15 shows a flowchart of the nearest point group determination unit according to the embodiments.

[0025] FIG. 16 shows a flowchart of the nearest point group determination unit according to the embodiments.

[0026] FIG. 17 shows a flowchart of the nearest point group determination unit according to the embodiments.

[0027] FIG. 18 shows a bitstream including geometry data, attribute data, and parameter information according to embodiments.

[0028] FIG. 19 shows a sequence parameter set (SPS) within a bitstream according to embodiments.

[0029] FIG. 20 shows an attribute parameter set (APS) in a bitstream according to embodiments.

[0030] FIG. 21 shows an APS in a bitstream according to embodiments.

[0031] FIG. 22 shows a Tile Parameter Set (TPS) within a bitstream according to embodiments.

[0032] FIG. 23 shows the TPS in the bitstream according to the embodiments.

[0033] FIG. 24 shows an attribute data header within a bitstream according to embodiments.

[0034] FIG. 25 shows an attribute data header within a bitstream according to embodiments.

[0035] FIG. 26 illustrates a encoding method according to embodiments.

[0036] FIG. 27 illustrates a decoding method according to embodiments.

[0037] Preferred embodiments of the embodiments are described in detail, and examples thereof are shown in the accompanying drawings. The following detailed description, with reference to the accompanying drawings, is intended to describe preferred embodiments of the embodiments rather than merely embodiments that may be implemented according to the embodiments. The following detailed description includes details to provide a thorough understanding of the embodiments. However, it is obvious to those skilled in the art that the embodiments may be practiced without these details.

[0038] Most terms used in the embodiments are selected from those commonly used in the field, but some terms are chosen at the applicant's discretion, and their meanings are described in detail in the following description as necessary. Accordingly, the embodiments should be understood based on the intended meaning of the terms, rather than their mere names or meanings.

[0039] FIG. 1 shows an example of a point cloud content provision system according to embodiments.

[0040] The point cloud content providing system illustrated in FIG. 1 may include a transmission device (10000) and a reception device (10004). The transmission device (10000) and the reception device (10004) can communicate via wired or wireless means to transmit and receive point cloud data.

[0041] A transmission device (10000) according to embodiments can acquire, process, and transmit point cloud video (or point cloud content). According to embodiments, the transmission device (10000) may include a fixed station, a base transceiver system (BTS), a network, an AI (Artificial Intelligence) device and / or system, a robot, an AR / VR / XR device and / or server, etc. Additionally, according to embodiments, the transmission device (10000) may include a device that communicates with a base station and / or other wireless devices using wireless access technology (e.g., 5G NR (New RAT), LTE (Long Term Evolution)), a robot, a vehicle, an AR / VR / XR device, a mobile device, a home appliance, an IoT (Internet of Things) device, an AI device / server, etc.

[0042] A transmission device (10000) according to embodiments includes a point cloud video acquisition unit (10001), a point cloud video encoder (10002), and / or a transmitter (or communication module), 10003.

[0043] A point cloud video acquisition unit (10001) according to the embodiments acquires a point cloud video through processing steps such as capture, synthesis, or generation. The point cloud video is a point cloud content represented as a point cloud, which is a set of points located in a three-dimensional space, and may be referred to as point cloud video data, etc. The point cloud video according to the embodiments may include one or more frames. A frame represents a still image / picture. Accordingly, the point cloud video may include a point cloud image / frame / picture and may be referred to as any one of a point cloud image, a frame, and a picture.

[0044] A point cloud video encoder (10002) according to the embodiments encodes the obtained point cloud video data. The point cloud video encoder (10002) can encode the point cloud video data based on point cloud compression coding. The point cloud compression coding according to the embodiments may include Geometry-based Point Cloud Compression (G-PCC) coding and / or Video-based Point Cloud Compression (V-PCC) coding or next-generation coding. Furthermore, the point cloud compression coding according to the embodiments is not limited to the embodiments described above. The point cloud video encoder (10002) can output a bitstream containing the encoded point cloud video data. The bitstream may include not only the encoded point cloud video data but also signaling information related to the encoding of the point cloud video data.

[0045] A transmitter (10003) according to the embodiments transmits a bitstream containing encoded point cloud video data. The bitstream according to the embodiments is encapsulated into a file or segment (e.g., a streaming segment) and transmitted through various networks such as a broadcast network and / or a broadband network. Although not illustrated in the drawings, the transmission device (10000) may include an encapsulation unit (or encapsulation module) that performs an encapsulation operation. Additionally, according to the embodiments, the encapsulation unit may be included in the transmitter (10003). According to the embodiments, the file or segment may be transmitted to a receiving device (10004) via a network or stored on a digital storage medium (e.g., USB, SD, CD, DVD, Blu-ray, HDD, SSD, etc.). The transmitter (10003) according to the embodiments can communicate wired or wirelessly with the receiving device (10004) (or receiver (10005)) via a network such as 4G, 5G, or 6G. Additionally, the transmitter (10003) can perform necessary data processing operations according to a network system (e.g., a communication network system such as 4G, 5G, 6G, etc.). Additionally, the transmission device (10000) can transmit encapsulated data according to an on-demand method.

[0046] A receiving device (10004) according to embodiments includes a receiver (10005), a point cloud video decoder (10006), and / or a renderer (10007). According to embodiments, the receiving device (10004) may include a device, robot, vehicle, AR / VR / XR device, mobile device, home appliance, IoT (Internet of Thing) device, AI device / server, etc., that communicates with a base station and / or other wireless device using wireless access technology (e.g., 5G NR (New RAT), LTE (Long Term Evolution)).

[0047] A receiver (10005) according to the embodiments receives a bitstream containing point cloud video data or a file / segment containing the bitstream from a network or a storage medium. The receiver (10005) can perform necessary data processing operations according to a network system (e.g., a communication network system such as 4G, 5G, 6G, etc.). The receiver (10005) according to the embodiments can output a bitstream by decapsulating the received file / segment. Additionally, according to the embodiments, the receiver (10005) may include a decapsulation unit (or decapsulation module) for performing a decapsulation operation. Additionally, the decapsulation unit may be implemented as an element (or component) separate from the receiver (10005).

[0048] A point cloud video decoder (10006) decodes a bitstream containing point cloud video data. The point cloud video decoder (10006) can decode the point cloud video data according to the way the point cloud video data is encoded (e.g., the reverse process of the operation of a point cloud video encoder (10002)). Accordingly, the point cloud video decoder (10006) can decode the point cloud video data by performing point cloud decompression coding, which is the reverse process of point cloud compression. Point cloud decompression coding includes G-PCC coding.

[0049] The renderer (10007) renders the decoded point cloud video data. The renderer (10007) can render not only the point cloud video data but also audio data to output point cloud content. According to embodiments, the renderer (10007) may include a display for displaying the point cloud content. According to embodiments, the display may not be included in the renderer (10007) but may be implemented as a separate device or component.

[0050] The arrows indicated by dotted lines in the drawing represent the transmission path of feedback information obtained from the receiving device (10004). The feedback information is information intended to reflect interaction with a user consuming point cloud content, and includes user information (e.g., head orientation information), viewport information, etc. In particular, if the point cloud content is content for a service requiring interaction with a user (e.g., autonomous driving service, etc.), the feedback information may be transmitted to the content transmitting side (e.g., the transmitting device (10000)) and / or the service provider. Depending on the embodiments, the feedback information may be used in the receiving device (10004) as well as the transmitting device (10000), or it may not be provided.

[0051] Head orientation information according to the embodiments is information regarding the user's head position, direction, angle, movement, etc. The receiving device (10004) according to the embodiments can calculate viewport information based on the head orientation information. Viewport information is information about the area of ​​the point cloud video that the user is looking at. The viewpoint refers to the point where the user is looking at the point cloud video, and may mean the exact center point of the viewport area. That is, the viewport is an area centered on the viewpoint, and the size and shape of the area can be determined by the Field Of View (FOV). Therefore, the receiving device (10004) can extract viewport information based on the vertical or horizontal FOV supported by the device in addition to the head orientation information. In addition, the receiving device (10004) performs gaze analysis, etc., to check the user's point cloud consumption method, the point cloud video area the user is looking at, the gaze time, etc. According to embodiments, the receiving device (10004) may transmit feedback information including gaze analysis results to the transmitting device (10000). According to embodiments, the feedback information may be obtained during the rendering and / or display process. According to embodiments, the feedback information may be obtained by one or more sensors included in the receiving device (10004). Also, according to embodiments, the feedback information may be obtained by the renderer (10007) or a separate external element (or device, component, etc.). The dotted line in FIG. 1 indicates the process of transmitting the feedback information obtained from the renderer (10007). The point cloud content providing system may process (encode / decode) point cloud data based on the feedback information. Accordingly, the point cloud video data decoder (10006) may perform a decoding operation based on the feedback information.Additionally, the receiving device (10004) can transmit feedback information to the transmitting device (10000). The transmitting device (10000) (or the point cloud video data encoder (10002)) can perform an encoding operation based on the feedback information. Thus, the point cloud content providing system can efficiently process necessary data (e.g., point cloud data corresponding to the user's head position) based on the feedback information without processing (encoding / decoding) all point cloud data, and provide point cloud content to the user.

[0052] According to embodiments, the transmission device (10000) may be referred to as an encoder, transmission device, transmitter, etc., and the receiving device (10004) may be referred to as a decoder, receiving device, receiver, etc.

[0053] Point cloud data processed in the point cloud content providing system of FIG. 1 according to embodiments (processed through a series of processes of acquisition / encoding / transmission / decoding / rendering) may be referred to as point cloud content data or point cloud video data. According to embodiments, point cloud content data may be used as a concept including metadata or signaling information related to point cloud data.

[0054] The elements of the point cloud content delivery system illustrated in FIG. 1 can be implemented using hardware, software, processors, and / or combinations thereof.

[0055] FIG. 2 is a block diagram illustrating a point cloud content provision operation according to embodiments.

[0056] The block diagram of FIG. 2 illustrates the operation of the point cloud content provision system described in FIG. 1. As described above, the point cloud content provision system can process point cloud data based on point cloud compression coding (e.g., G-PCC).

[0057] A point cloud content providing system according to the embodiments (e.g., a point cloud transmission device (10000) or a point cloud video acquisition unit (10001)) can acquire a point cloud video (20000). The point cloud video is represented as a point cloud belonging to a coordinate system representing a three-dimensional space. The point cloud video according to the embodiments may include a Ply (Polygon File format or the Stanford Triangle format) file. If the point cloud video has one or more frames, the acquired point cloud video may include one or more Ply files. The Ply file contains point cloud data such as the geometry and / or attributes of the points. The geometry includes the positions of the points. The position of each point may be represented by parameters (e.g., values ​​of the X-axis, Y-axis, and Z-axis, respectively) representing a three-dimensional coordinate system (e.g., a coordinate system consisting of XYZ axes). Attributes include attributes of points (e.g., texture information, color (YCbCr or RGB), reflectance (r), transparency, etc. of each point). A point has one or more attributes (or properties). For example, a point may have one attribute which is color, or two attributes which are color and reflectance. According to embodiments, geometry may be referred to as positions, geometry information, geometry data, etc., and attributes may be referred to as attributes, attribute information, attribute data, etc.In addition, a point cloud content provision system (e.g., a point cloud transmission device (10000) or a point cloud video acquisition unit (10001)) can obtain point cloud data from information related to the acquisition process of point cloud video (e.g., depth information, color information, etc.).

[0058] A point cloud content providing system according to embodiments (e.g., a transmission device (10000) or a point cloud video encoder (10002)) can encode point cloud data (20001). The point cloud content providing system can encode point cloud data based on point cloud compression coding. As described above, point cloud data may include geometry and attributes of points. Accordingly, the point cloud content providing system can output a geometry bitstream by performing geometry encoding to encode geometry. The point cloud content providing system can output an attribute bitstream by performing attribute encoding to encode attributes. According to embodiments, the point cloud content providing system can perform attribute encoding based on geometry encoding. The geometry bitstream and attribute bitstream according to embodiments can be multiplexed and output as a single bitstream. The bitstream according to the embodiments may further include signaling information related to geometry encoding and attribute encoding.

[0059] A point cloud content providing system according to embodiments (e.g., a transmission device (10000) or a transmitter (10003)) can transmit encoded point cloud data (20002). As described in FIG. 1, the encoded point cloud data can be represented as a geometry bitstream and an attribute bitstream. Additionally, the encoded point cloud data can be transmitted in the form of a bitstream along with signaling information related to the encoding of the point cloud data (e.g., signaling information related to geometry encoding and attribute encoding). Additionally, the point cloud content providing system can encapsulate the bitstream transmitting the encoded point cloud data and transmit it in the form of a file or segment.

[0060] A point cloud content providing system according to embodiments (e.g., a receiving device (10004) or a receiver (10005)) can receive a bitstream containing encoded point cloud data. Additionally, the point cloud content providing system (e.g., a receiving device (10004) or a receiver (10005)) can demultiplex the bitstream.

[0061] A point cloud content providing system (e.g., a receiving device (10004) or a point cloud video decoder (10005)) can decode encoded point cloud data (e.g., a geometry bitstream, an attribute bitstream) transmitted as a bitstream. A point cloud content providing system (e.g., a receiving device (10004) or a point cloud video decoder (10005)) can decode point cloud video data based on signaling information related to the encoding of point cloud video data included in the bitstream. A point cloud content providing system (e.g., a receiving device (10004) or a point cloud video decoder (10005)) can decode the geometry bitstream to restore the positions (geometry) of the points. A point cloud content providing system can decode the attribute bitstream based on the restored geometry to restore the attributes of the points. A point cloud content delivery system (e.g., a receiving device (10004) or a point cloud video decoder (10005)) can restore a point cloud video based on positions according to the restored geometry and decoded attributes.

[0062] A point cloud content providing system according to embodiments (e.g., a receiving device (10004) or a renderer (10007)) can render decoded point cloud data (20004). The point cloud content providing system (e.g., a receiving device (10004) or a renderer (10007)) can render geometry and attributes decoded through a decoding process according to various rendering methods. Points of the point cloud content may be rendered as vertices having a certain thickness, cubes having a specific minimum size with the vertex location as the center, or circles with the vertex location as the center, etc. All or part of the rendered point cloud content is provided to a user through a display (e.g., a VR / AR display, a general display, etc.).

[0063] A point cloud content providing system (e.g., a receiving device (10004)) according to the embodiments can obtain feedback information (20005). The point cloud content providing system can encode and / or decode point cloud data based on the feedback information. Since the feedback information and the operation of the point cloud content providing system according to the embodiments are the same as the feedback information and operation described in FIG. 1, a detailed description is omitted.

[0064] FIG. 3 shows an example of a point cloud encoder according to embodiments.

[0065] FIG. 3 shows an example of the point cloud video encoder (10002) of FIG. 1. The point cloud encoder reconstructs point cloud data (e.g., positions and / or attributes of points) and performs encoding operations to adjust the quality of point cloud content (e.g., lossless, lossy, near-lossless) according to network conditions or applications. If the total size of the point cloud content is large (e.g., point cloud content of 60 Gbps in the case of 30 fps), the point cloud content delivery system may not be able to stream the content in real time. Therefore, the point cloud content delivery system may reconstruct the point cloud content based on a maximum target bitrate to provide it according to the network environment.

[0066] As described in FIGS. 1 and 2, the point cloud encoder can perform geometry encoding and attribute encoding. Geometry encoding is performed before attribute encoding.

[0067] The point cloud encoder according to the embodiments comprises a coordinate system transformation unit (Transformation Coordinates, 30000), a quantization unit (Quantize and Remove Points (Voxelize), 30001), an octree analysis unit (Analyze Octree, 30002), a surface approximation analysis unit (Analyze Surface Approximation, 30003), an arithmetic encoder (Arithmetic Encode, 30004), a geometry reconstruction unit (Reconstruct Geometry, 30005), a color transformation unit (Transform Colors, 30006), an attribute transformation unit (Transfer Attributes, 30007), a RAHT transformation unit (30008), an LOD generation unit (Generated LOD, 30009), a lifting transformation unit (Lifting) (30010), and a coefficient quantization unit (Quantize Coefficients, 30011). Includes an and / or arithmetic encoder (Arithmetic Encode, 30012).

[0068] The coordinate system transformation unit (30000), quantization unit (30001), octree analysis unit (30002), surface approximation analysis unit (30003), arismetic encoder (30004), and geometry reconstruction unit (30005) can perform geometry encoding. Geometry encoding according to the embodiments may include octree geometry coding, direct coding, trisoup geometry encoding, and entropy encoding. Direct coding and trisoup geometry encoding are applied optionally or in combination. Additionally, geometry encoding is not limited to the above examples.

[0069] As illustrated in the drawings, the coordinate system conversion unit (30000) according to the embodiments receives positions and converts them into a coordinate system. For example, the positions can be converted into position information in a three-dimensional space (e.g., a three-dimensional space expressed in an XYZ coordinate system). The position information in the three-dimensional space according to the embodiments may be referred to as geometry information.

[0070] The quantization unit (30001) according to the embodiments quantizes the geometry. For example, the quantization unit (30001) can quantize points based on the minimum position values ​​of all points (e.g., minimum values ​​on each axis for the X-axis, Y-axis, and Z-axis). The quantization unit (30001) performs a quantization operation to find the nearest integer value by multiplying the difference between the minimum position value and the position value of each point by a preset quantization scale value and then performing rounding down or rounding up. Thus, one or more points may have the same quantized position (or position value). The quantization unit (30001) according to the embodiments performs voxelization based on the quantized positions to reconstruct the quantized points. Just as the minimum unit containing 2D image / video information is a pixel, the points of the point cloud content (or 3D point cloud video) according to the embodiments may be contained in one or more voxels. A voxel is a combination of volume and pixel, and refers to a three-dimensional cubic space that is generated when a three-dimensional space is divided into units (unit=1.0) based on axes representing the three-dimensional space (e.g., X-axis, Y-axis, Z-axis). The quantization unit (40001) can match groups of points in the three-dimensional space to voxels. According to embodiments, a single voxel may contain only one point. According to embodiments, a single voxel may contain one or more points. In addition, to represent a single voxel as a single point, the position of the center of the voxel can be set based on the positions of one or more points included in the voxel. In this case, the attributes of all positions included in the voxel can be combined and assigned to the voxel.

[0071] The octree analysis unit (30002) according to the embodiments performs octree geometry coding (or octree coding) to represent the voxels in an octree structure. The octree structure represents points matched to the voxels based on an octree structure.

[0072] The surface approximation analysis unit (30003) according to the embodiments can analyze and approximate an octree. The octree analysis and approximation according to the embodiments is a process of analyzing to voxelize an area containing multiple points in order to efficiently provide octree and voxelization.

[0073] An arithmetic encoder (30004) according to the embodiments entropy-encodes an octree and / or an approximated octree. For example, the encoding method includes an arithmetic encoding method. As a result of the encoding, a geometry bitstream is generated.

[0074] The color conversion unit (30006), attribute conversion unit (30007), RAHT conversion unit (30008), LOD generation unit (30009), lifting conversion unit (30010), coefficient quantization unit (30011) and / or arismetic encoder (30012) perform attribute encoding. As described above, a point may have one or more attributes. The attribute encoding according to the embodiments is applied equally to the attributes of a point. However, if a single attribute (e.g., color) includes one or more elements, independent attribute encoding is applied to each element. The attribute encoding according to the embodiments may include color conversion coding, attribute conversion coding, Region Adaptive Hierarchial Transform (RAHT) coding, prediction transformation (Interpolaration-based hierarchical nearest-neighbour prediction-Prediction Transform) coding, and lifting transformation (interpolation-based hierarchical nearest-neighbour prediction with an update / lifting step (Lifting Transform)) coding. Depending on the point cloud content, the above-described RAHT coding, prediction transformation coding, and lifting transformation coding may be used optionally, or a combination of one or more of the codings may be used. Furthermore, the attribute encoding according to the embodiments is not limited to the examples described above.

[0075] The color conversion unit (30006) according to the embodiments performs color conversion coding that converts color values ​​(or textures) included in attributes. For example, the color conversion unit (30006) can convert the format of color information (e.g., convert from RGB to YCbCr). The operation of the color conversion unit (30006) according to the embodiments may be applied optionally depending on the color values ​​included in attributes.

[0076] The geometry reconstruction unit (30005) according to the embodiments reconstructs (decompresses) an octree and / or an approximated octree. The geometry reconstruction unit (30005) reconstructs an octree / voxel based on the results of analyzing the distribution of points. The reconstructed octree / voxel may be referred to as the reconstructed geometry (or restored geometry).

[0077] The attribute transformation unit (30007) according to the embodiments performs attribute transformation that transforms attributes based on positions where geometry encoding has not been performed and / or reconstructed geometry. As described above, since attributes are dependent on geometry, the attribute transformation unit (30007) can transform attributes based on reconstructed geometry information. For example, the attribute transformation unit (30007) can transform the attributes of a point at a position based on the position value of a point included in a voxel. As described above, when the position of the center point of a voxel is set based on the positions of one or more points included in a voxel, the attribute transformation unit (30007) transforms the attributes of one or more points. When trisoop geometry encoding is performed, the attribute conversion unit (30007) can convert attributes based on the trisoop geometry encoding.

[0078] The attribute transformation unit (30007) can perform attribute transformation by calculating the average value of attributes or attribute values ​​(e.g., the color or reflectance of each point) of neighboring points within a specific location / radius from the position (or position value) of the center point of each voxel. The attribute transformation unit (30007) can apply a weight based on the distance from the center point to each point when calculating the average value. Thus, each voxel has a position and a calculated attribute (or attribute value).

[0079] The attribute conversion unit (30007) can search for neighboring points within a specific location / radius from the position of the center point of each voxel based on a KD tree or a Molton code. A KD tree is a binary search tree that supports a data structure capable of managing points based on their positions to enable rapid Nearest Neighbor Search (NNS). A Molton code is generated by representing the coordinate values ​​(e.g., (x, y, z)) representing the 3D positions of all points as bit values ​​and mixing the bits. For example, if the coordinate values ​​representing the position of a point are (5, 9, 1), the bit values ​​of the coordinate values ​​are (0101, 1001, 0001). When the bit values ​​are mixed according to the bit indices in the order of z, y, and x, it becomes 010001000111. When this value is represented in decimal, it becomes 1095. That is, the Molton code value of the point with coordinates (5, 9, 1) is 1095. The attribute transformation unit (30007) sorts the points based on the Molton code value and can perform shortest neighbor search (NNS) through a depth-first traversal process. After the attribute transformation operation, if shortest neighbor search (NNS) is required in other transformation processes for attribute coding, a KD tree or Molton code is utilized.

[0080] As shown in the drawing, the converted attributes are input to the RAHT conversion unit (30008) and / or LOD generation unit (30009).

[0081] The RAHT transformation unit (30008) according to the embodiments performs RAHT coding to predict attribute information based on reconstructed geometry information. For example, the RAHT transformation unit (30008) can predict attribute information of a node at an upper level of the octree based on attribute information associated with a node at a lower level of the octree.

[0082] The LOD generation unit (30009) according to the embodiments generates a Level of Detail (LOD) to perform predictive transformation coding. The LOD according to the embodiments represents the degree of detail of the point cloud content, and indicates that the smaller the LOD value, the lower the detail of the point cloud content, and the larger the LOD value, the higher the detail of the point cloud content. Points can be classified according to the LOD.

[0083] The lifting transformation unit (30010) according to the embodiments performs lifting transformation coding that transforms the attributes of the point cloud based on weights. As described above, the lifting transformation coding may be applied optionally.

[0084] The coefficient quantization unit (30011) according to the embodiments quantizes attribute-coded attributes based on coefficients.

[0085] An arismetic encoder (30012) according to the embodiments encodes quantized attributes based on arismetic coding.

[0086] The elements of the point cloud encoder of FIG. 3 may be implemented in hardware, software, firmware, or a combination thereof, comprising one or more processors or integrated circuits configured to communicate with one or more memories included in the point cloud providing device, although not illustrated in the drawing. One or more processors may perform at least one of the operations and / or functions of the elements of the point cloud encoder of FIG. 3 described above. Additionally, one or more processors may operate or execute a set of software programs and / or instructions for performing the operations and / or functions of the elements of the point cloud encoder of FIG. 3. One or more memories according to the embodiments may include high-speed random access memory and may include non-volatile memory (e.g., one or more magnetic disk storage devices, flash memory devices, or other non-volatile solid-state memory devices).

[0087] FIG. 4 shows examples of octree and occupancy codes according to embodiments.

[0088] As described in FIGS. 1 to 3, a point cloud content providing system (point cloud video encoder (10002)) or a point cloud encoder (e.g., an octree analysis unit (30002)) performs octree geometry coding based on an octree structure (or octree coding) to efficiently manage the area and / or position of a voxel.

[0089] The top of FIG. 4 shows an octree structure. The three-dimensional space of the point cloud content according to the embodiments is represented by the axes of the coordinate system (e.g., X-axis, Y-axis, Z-axis). The octree structure has two poles (0,0,0) and (2 d , 2 d , 2 d It is generated by recursively subdividing the bounding box (cubical axis-aligned bounding box) defined by ). 2d can be set to the value that constitutes the smallest bounding box enclosing all points of the point cloud content (or point cloud video). d represents the depth of the octree. The value of d is determined according to the following equation. In the equation below, (x int n , y int n , z int n ) represents the positions (or position values) of quantized points.

[0090] d=Ceil(Log2(Max(x int n ,y int n ,z int n ,n=1,… ,N)+1))

[0091] As illustrated in the middle of the top of Fig. 4, the entire three-dimensional space can be divided into eight spaces according to the division. Each divided space is represented as a cube having six faces. As illustrated in the right of the top of Fig. 4, each of the eight spaces is further divided based on the axes of the coordinate system (e.g., X-axis, Y-axis, Z-axis). Thus, each space is again divided into eight smaller spaces. The divided smaller spaces are also represented as cubes having six faces. This division method is applied until the leaf nodes of the octree become voxels.

[0092] The bottom of Fig. 4 shows the occupancy code of an octree. The occupancy code of an octree is generated to indicate whether each of the eight partitioned spaces resulting from the partitioning of a single space contains at least one point. Therefore, one occupancy code is represented by eight child nodes. Each child node represents the occupancy of the partitioned space, and the child node has a value of 1 bit. Thus, the occupancy code is represented as an 8-bit code. That is, if the space corresponding to the child node contains at least one point, the node has a value of 1. If the space corresponding to the child node does not contain a point (empty), the node has a value of 0. Since the occupancy code shown in Fig. 4 is 00100001, it indicates that the spaces corresponding to the 3rd and 8th child nodes among the eight child nodes each contain at least one point. As illustrated in the drawing, the 3rd child node and the 8th child node each have 8 child nodes, and each child node is represented by an 8-bit Occupancy code. The drawing indicates that the Occupancy code of the 3rd child node is 10000111 and the Occupancy code of the 8th child node is 01001111. A point cloud encoder according to the embodiments (e.g., an arismetic encoder (30004)) can entropy-encode the Occupancy code. Additionally, to increase compression efficiency, the point cloud encoder can intra- / inter-encode the Occupancy code. A receiving device according to the embodiments (e.g., a receiving device (10004) or a point cloud video decoder (10006)) reconstructs the octree based on the Occupancy code.

[0093] A point cloud encoder according to the embodiments (e.g., the point cloud encoder of FIG. 3, or the octree analysis unit (30002)) can perform voxelization and octree coding to store the positions of the points. However, since points in a three-dimensional space are not always evenly distributed, there may be specific areas where few points exist. Therefore, performing voxelization on the entire three-dimensional space is inefficient. For example, if there are almost no points in a specific area, there is no need to perform voxelization up to that area.

[0094] Accordingly, the point cloud encoder according to the embodiments can perform direct coding, which directly codes the positions of points included in a specific region (or nodes excluding leaf nodes of an octree) without performing voxelization on the aforementioned specific region. The coordinates of the points directly coded according to the embodiments are referred to as the Direct Coding Mode (DCM). Additionally, the point cloud encoder according to the embodiments can perform trisoup geometry encoding, which reconstructs the positions of points within a specific region (or node) based on voxels using a surface model. Trisoup geometry encoding is a geometry encoding that represents an object as a series of triangle meshes. Therefore, the point cloud decoder can generate a point cloud from the mesh surface. Direct coding and trisoup geometry encoding according to the embodiments may be performed optionally. In addition, direct coding and trisoop geometry encoding according to the embodiments can be performed in combination with octree geometry coding (or octree coding).

[0095] To perform direct coding, the option to use direct mode for applying direct coding must be enabled, the node to which direct coding is to be applied must not be a leaf node, and there must be points within a specific node that are below a threshold. In addition, the total number of points subject to direct coding must not exceed a preset threshold. If the above conditions are satisfied, the point cloud encoder (or arismetic encoder (30004)) according to the embodiments can entropy-code the positions (or position values) of the points.

[0096] A point cloud encoder according to the embodiments (e.g., a surface approximation analysis unit (30003)) can determine a specific level of an octree (where the level is smaller than the depth d of the octree) and, starting from that level, perform trisoop geometry encoding to reconstruct the position of points within a node region based on voxels using a surface model (trisoop mode). The point cloud encoder according to the embodiments can specify the level to which trisoop geometry encoding is applied. For example, if the specified level is equal to the depth of the octree, the point cloud encoder does not operate in trisoop mode. That is, the point cloud encoder according to the embodiments can operate in trisoop mode only when the specified level is smaller than the depth value of the octree. A three-dimensional cubic region of nodes at a specified level according to the embodiments is referred to as a block. A block may include one or more voxels. A block or a voxel may correspond to a brick. Within each block, geometry is represented as a surface. A surface according to the embodiments may intersect each edge of the block at most once.

[0097] Since one block has 12 edges, there are at least 12 intersection points within one block. Each intersection point is referred to as a vertex. A vertex along an edge is detected if there is at least one occupied voxel adjacent to that edge among all blocks sharing that edge. An occupied voxel according to the embodiments means a voxel containing a point. The position of a vertex detected along an edge is the average position along the edge of all voxels adjacent to that edge among all blocks sharing that edge.

[0098] When a vertex is detected, the point cloud encoder according to the embodiments can entropy-code the edge start point (x, y, z), edge direction vector (Δx, Δy, Δz), and vertex position value (relative position value within the edge). When trisoop geometry encoding is applied, the point cloud encoder according to the embodiments (e.g., geometry reconstruction unit (30005)) can generate restored geometry (reconstructed geometry) by performing triangle reconstruction, up-sampling, and voxelization processes.

[0099] The vertices located on the edges of the block determine the surface passing through the block. The surface according to the embodiments is a non-planar polygon. The triangle reconstruction process reconstructs the surface represented by triangles based on the edge start point, the edge direction vector, and the vertex position value. The triangle reconstruction process is as follows: ① calculate the centroid value of each vertex, ② subtract the centroid value from each vertex value, ③ square the result, and add all the result together.

[0100]

[0101] The minimum sum is calculated, and a projection process is performed along the axis where the minimum value is located. For example, if the x-element is at its minimum, each vertex is projected along the x-axis relative to the center of the block and onto the (y, z) plane. If the resulting value from projection onto the (y, z) plane is (ai, bi), the θ value is calculated using atan2(bi, ai), and the vertices are aligned based on this θ value. The table below shows the combinations of vertices to generate triangles depending on the number of vertices. The vertices are aligned in order from 1 to n. The table below indicates that for four vertices, two triangles can be formed based on the combination of vertices. The first triangle is composed of the 1st, 2nd, and 3rd vertices among the aligned vertices, and the second triangle can be composed of the 3rd, 4th, and 1st vertices among the aligned vertices.

[0102] Table 2-1. Triangles formed from vertices ordered 1,… ,n

[0103] n triangles

[0104] 3 (1,2,3)

[0105] 4 (1,2,3), (3,4,1)

[0106] 5 (1,2,3), (3,4,5), (5,1,3)

[0107] 6 (1,2,3), (3,4,5), (5,6,1), (1,3,5)

[0108] 7 (1,2,3), (3,4,5), (5,6,7), (7,1,3), (3,5,7)

[0109] 8 (1,2,3), (3,4,5), (5,6,7), (7,8,1), (1,3,5), (5,7,1)

[0110] 9 (1,2,3), (3,4,5), (5,6,7), (7,8,9), (9,1,3), (3,5,7), (7,9,3)

[0111] 10 (1,2,3), (3,4,5), (5,6,7), (7,8,9), (9,10,1), (1,3,5), (5,7,9), (9,1,5)

[0112] 11 (1,2,3), (3,4,5), (5,6,7), (7,8,9), (9,10,11), (11,1,3), (3,5,7), (7,9,11), (11,3,7)

[0113] 12 (1,2,3), (3,4,5), (5,6,7), (7,8,9), (9,10,11), (11,12,1), (1,3,5), (5,7,9), (9,11,1), (1,5,9)

[0114] The upsampling process is performed to voxelize by adding intermediate points along the edges of the triangle. Additional points are generated based on the upsampling factor value and the width of the block. The additional points are referred to as refined vertices. A point cloud encoder according to the embodiments can voxelize the refined vertices. Additionally, the point cloud encoder can perform attribute encoding based on the voxelized positions (or position values).

[0115] Figure 5 shows an example of a point configuration by LOD according to embodiments.

[0116] As described in FIGS. 1 to 4, the encoded geometry is reconstructed (decompressed) before attribute encoding is performed. When direct coding is applied, the geometry reconstruction operation may include changing the arrangement of the direct-coded points (e.g., placing the direct-coded points at the front of the point cloud data). When trisoop geometry encoding is applied, the geometry reconstruction process involves triangle reconstruction, upsampling, and voxelization. Since attributes depend on geometry, attribute encoding is performed based on the reconstructed geometry.

[0117] A point cloud encoder (e.g., an LOD generation unit (30009)) can reorganize points by LOD. The drawing shows point cloud content corresponding to the LOD. The left side of the drawing shows the original point cloud content. The second figure from the left of the drawing shows the distribution of points of the lowest LOD, and the rightmost figure of the drawing shows the distribution of points of the highest LOD. That is, the points of the lowest LOD are sparsely distributed, while the points of the highest LOD are densely distributed. In other words, according to the direction of the arrow indicated at the bottom of the drawing, as the LOD increases, the spacing (or distance) between points becomes shorter.

[0118] Figure 6 shows an example of a point configuration by LOD according to embodiments.

[0119] As described in FIGS. 1 to 5, a point cloud content providing system or a point cloud encoder (e.g., a point cloud video encoder (10002), the point cloud encoder of FIG. 3, or an LOD generation unit (30009)) can generate an LOD. The LOD is generated by reorganizing points into a set of refinement levels according to a set LOD distance value (or a set of Euclidean distances). The LOD generation process is performed in a point cloud decoder as well as a point cloud encoder.

[0120] The top of Fig. 6 shows examples of points (P0 to P9) of point cloud content distributed in three-dimensional space. The Original Order in Fig. 6 represents the order of points P0 to P9 prior to LOD generation. The LOD-based Order in Fig. 6 represents the order of points following LOD generation. Points are rearranged by LOD. Additionally, higher LODs include points belonging to lower LODs. As illustrated in Fig. 6, LOD0 includes P0, P5, P4, and P2. LOD1 includes the points of LOD0 and P1, P6, and P3. LOD2 includes the points of LOD0, the points of LOD1, and P9, P8, and P7.

[0121] As described in FIG. 3, the point cloud encoder according to the embodiments can perform predictive transform coding, lifting transform coding, and RAHT transform coding selectively or in combination.

[0122] The point cloud encoder according to the embodiments can generate predictors for points and perform predictive transformation coding to set the predicted attribute (or predicted attribute value) of each point. That is, N predictors can be generated for N points. The predictor according to the embodiments can calculate a weight (=1 / distance) value based on the LOD value of each point, indexing information for neighboring points within a set distance per LOD, and the distance value to the neighboring points.

[0123] According to the embodiments, the predicted attribute (or attribute value) is set as the average value of the values ​​obtained by multiplying the attributes (or attribute values, e.g., color, reflectance, etc.) of neighboring points set in the predictor of each point by a weight (or weight value) calculated based on the distance to each neighboring point. The point cloud encoder according to the embodiments (e.g., coefficient quantization unit (30011)) can quantize and inverse quantize the residual values ​​(which may be referred to as residual attributes, residual attribute values, attribute prediction residual values, etc.) obtained by subtracting the predicted attribute (attribute value) from the attribute (attribute value) of each point. The quantization process is as shown in the following table.

[0124] Attribute prediction residuals quantization pseudo codeint PCCQuantization(int value, int quantStep) {if( value >=0) {return floor(value / quantStep + 1.0 / 3.0);} else {return -floor(-value / quantStep + 1.0 / 3.0);}}

[0125] Attribute prediction residuals inverse quantization pseudo codeint PCCInverseQuantization(int value, int quantStep) {if( quantStep ==0) {return value;} else {return value * quantStep;}}

[0126] A point cloud encoder according to the embodiments (e.g., an arismetic encoder (30012)) can entropy-code the quantized and inversely quantized residual values ​​as described above when there are neighboring points in the predictor of each point. A point cloud encoder according to the embodiments (e.g., an arismetic encoder (30012)) can entropy-code the attributes of the corresponding point without performing the process described above when there are no neighboring points in the predictor of each point.

[0127] A point cloud encoder according to the embodiments (e.g., a lifting transformation unit (30010)) can perform lifting transformation coding by generating a predictor for each point, setting the LOD calculated in the predictor, registering neighboring points, and setting weights based on the distance to neighboring points. The lifting transformation coding according to the embodiments is similar to the prediction transformation coding described above, but differs in that weights are cumulatively applied to attribute values. The process of cumulatively applying weights to attribute values ​​according to the embodiments is as follows.

[0128] 1) Create an array QW (QuantizationWift) to store the weight values ​​of each point. The initial value of all elements in QW is 1.0. Add the value obtained by multiplying the current point's predictor weight by the QW value of the predictor index of the neighboring node registered in the predictor.

[0129] 2) Lift prediction process: To calculate the predicted attribute value, the value obtained by multiplying the point's attribute value by a weight is subtracted from the existing attribute value.

[0130] 3) Create temporary arrays named updateweight and update, and initialize the temporary arrays to 0.

[0131] 4) For all predictors, the calculated weight is additionally multiplied by the weight stored in the QW corresponding to the predictor index, and the resulting weight is accumulated in the update weight array with the neighbor node index. In the update array, the value obtained by multiplying the attribute value of the neighbor node index by the calculated weight is accumulated.

[0132] 5) Lift update process: For all predictors, the attribute value of the update array is divided by the weight value of the update weight array at the predictor index, and the original attribute value is added back to the divided value.

[0133] 6) For all predictors, the predicted attribute value is calculated by additionally multiplying the attribute value updated through the lift update process by the weight (stored in QW) updated through the lift prediction process. A point cloud encoder according to the embodiments (e.g., coefficient quantizer (30011)) quantizes the predicted attribute value. Additionally, a point cloud encoder (e.g., arismetic encoder (30012)) entropies the quantized attribute value.

[0134] A point cloud encoder according to the embodiments (e.g., a RAHT transform unit (30008)) can perform RAHT transform coding to predict attributes of upper-level nodes using attributes associated with nodes at lower levels of the octree. RAHT transform coding is an example of attribute intra-coding through octree backward scanning. A point cloud encoder according to the embodiments scans from a voxel to the entire region and repeats the merging process up to the root node, merging the voxels into larger blocks at each step. The merging process according to the embodiments is performed only on occupied nodes. The merging process is not performed on empty nodes, and the merging process is performed on the node immediately above the empty node.

[0135] The following equation represents the RAHT transformation matrix. is level Represents the average attribute value of the voxels in. Is and It can be calculated from. and The weights of class am.

[0136]

[0137] is a low-pass value used in the merging process at the next higher level. are high-pass coefficients, and the high-pass coefficients at each step are quantized and entropy-coded (e.g., encoding of an arismetic encoder (300012)). The weights are It is calculated as. The root node is the last class It is generated as follows through.

[0138]

[0139] The gDC value is also quantized and entropy-coded, just like the high-pass coefficient.

[0140] FIG. 7 shows an example of a point cloud decoder according to embodiments.

[0141] The point cloud decoder illustrated in FIG. 7 is an example of a point cloud decoder and can perform a decoding operation, which is the reverse process of the encoding operation of the point cloud encoder described in FIG. 1 to 6.

[0142] As described in Fig. 1, the point cloud decoder can perform geometry decoding and attribute decoding. Geometry decoding is performed before attribute decoding.

[0143] A point cloud decoder according to the embodiments comprises an arithmetic decoder (7000), a synthesize octree (7001), a synthesize surface approximation (7002), a reconstruct geometry (7003), an inverse transform coordinates (7004), an arithmetic decoder (7005), an inverse quantize (7006), a RAHT transform (7007), an LOD generater (7008), an inverse lifting (7009), and / or an inverse transform colors (7010).

[0144] An arismetic decoder (7000), an octree synthesis unit (7001), a surface offset synthesis unit (7002), a geometry reconstruction unit (7003), and a coordinate system inverse transformation unit (7004) can perform geometry decoding. Geometry decoding according to the embodiments may include direct coding and trisoup geometry decoding. Direct coding and trisoup geometry decoding are applied optionally. Additionally, geometry decoding is not limited to the above examples and is performed as the reverse process of geometry encoding described in FIGS. 1 through 6.

[0145] The arismetic decoder (7000) according to the embodiments decodes the received geometry bitstream based on arismetic coding. The operation of the arismetic decoder (7000) corresponds to the reverse process of the arismetic encoder (30004).

[0146] The octree synthesis unit (7001) according to the embodiments can generate an octree by obtaining an Occupancy code from a decoded geometry bitstream (or information regarding the geometry obtained as a result of decoding). A specific description of the Occupancy code is as described in FIGS. 1 to 6.

[0147] The surface off-relation synthesis unit (7002) according to the embodiments can synthesize a surface based on the decoded geometry and / or the generated octree when trisoop geometry encoding is applied.

[0148] The geometry reconstruction unit (7003) according to the embodiments can regenerate geometry based on a surface and / or decoded geometry. As described in FIGS. 1 through 6, direct coding and trisoop geometry encoding are applied optionally. Accordingly, the geometry reconstruction unit (7003) directly retrieves and adds position information of points to which direct coding has been applied. In addition, when trisoop geometry encoding is applied, the geometry reconstruction unit (7003) can restore geometry by performing reconstruction operations of the geometry reconstruction unit (30005), such as triangle reconstruction, up-sampling, and voxelization operations. Specific details are omitted as they are the same as those described in FIG. 4. The restored geometry may include a point cloud picture or frame that does not contain attributes.

[0149] The coordinate system inverse transformation unit (7004) according to the embodiments can obtain the positions of the points by transforming the coordinate system based on the restored geometry.

[0150] The arismetic decoder (7005), inverse quantization unit (7006), RAHT transformation unit (7007), LOD generation unit (7008), inverse lifting unit (7009), and / or color inverse transformation unit (7010) can perform attribute decoding as described in FIG. 10. Attribute decoding according to the embodiments may include Region Adaptive Hierarchial Transform (RAHT) decoding, Interpolaration-based hierarchical nearest-neighbour prediction-Prediction Transform) decoding, and interpolation-based hierarchical nearest-neighbour prediction with an update / lifting step (Lifting Transform) decoding. The three decodings described above may be used optionally, or a combination of one or more decodings may be used. Furthermore, attribute decoding according to the embodiments is not limited to the examples described above.

[0151] The arismetic decoder (7005) according to the embodiments decodes the attribute bitstream into arismetic coding.

[0152] The inverse quantization unit (7006) according to the embodiments inverse quantizes information about the decoded attribute bitstream or the attribute obtained as a result of decoding and outputs the inverse quantized attributes (or attribute values). Inverse quantization may be optionally applied based on the attribute encoding of the point cloud encoder.

[0153] According to embodiments, the RAHT transformation unit (7007), LOD generation unit (7008), and / or inverse lifting unit (7009) can process the reconstructed geometry and inverse quantized attributes. As described above, the RAHT transformation unit (7007), LOD generation unit (7008), and / or inverse lifting unit (7009) can optionally perform a corresponding decoding operation according to the encoding of the point cloud encoder.

[0154] The color inverse conversion unit (7010) according to the embodiments performs inverse conversion coding to inversely convert the color value (or texture) included in the decoded attributes. The operation of the color inverse conversion unit (7010) may be selectively performed based on the operation of the color conversion unit (30006) of the point cloud encoder.

[0155] The elements of the point cloud decoder of FIG. 7 may be implemented in hardware, software, firmware, or a combination thereof, comprising one or more processors or integrated circuits configured to communicate with one or more memories included in the point cloud providing device, although not illustrated in the drawing. One or more processors may perform at least one of the operations and / or functions of the elements of the point cloud decoder of FIG. 7 described above. Additionally, one or more processors may operate or execute a set of software programs and / or instructions for performing the operations and / or functions of the elements of the point cloud decoder of FIG. 7.

[0156] FIG. 8 is an example of a transmission device according to embodiments.

[0157] The transmission device illustrated in FIG. 8 is an example of the transmission device (10000) of FIG. 1 (or the point cloud encoder of FIG. 3). The transmission device illustrated in FIG. 8 can perform at least one of the same or similar operations and methods as the operations and encoding methods of the point cloud encoder described in FIG. 1 to 6. A transmission device according to embodiments may include a data input unit (8000), a quantization processing unit (8001), a voxelization processing unit (8002), an octree occupancy code generation unit (8003), a surface model processing unit (8004), an intra / inter coding processing unit (8005), an arithmetic coder (8006), a metadata processing unit (8007), a color conversion processing unit (8008), an attribute conversion processing unit (or attribute conversion processing unit) (8009), a prediction / lifting / RAHT conversion processing unit (8010), an arithmetic coder (8011) and / or a transmission processing unit (8012).

[0158] The data input unit (8000) according to the embodiments receives or acquires point cloud data. The data input unit (8000) may perform an operation and / or acquisition method identical or similar to the operation and / or acquisition method of the point cloud video acquisition unit (10001) (or the acquisition process (20000) described in FIG. 2).

[0159] The data input unit (8000), quantization processing unit (8001), voxelization processing unit (8002), octree occupancy code generation unit (8003), surface model processing unit (8004), intra / inter coding processing unit (8005), and arithmetic coder (8006) perform geometry encoding. Since the geometry encoding according to the embodiments is identical or similar to the geometry encoding described in FIGS. 1 to 6, a detailed description is omitted.

[0160] The quantization processing unit (8001) according to the embodiments quantizes geometry (e.g., location values ​​of points, or position values). The operation and / or quantization of the quantization processing unit (8001) is the same or similar to the operation and / or quantization of the quantization unit (30001) described in FIG. 3. The specific description is the same as that described in FIG. 1 through 6.

[0161] The voxelization processing unit (8002) according to the embodiments voxelizes the position values ​​of the quantized points. The voxelization processing unit (80002) may perform the same or similar operation and / or process as the operation and / or voxelization process of the quantization unit (30001) described in FIG. 3. The specific description is the same as that described in FIG. 1 to 6.

[0162] The octree occupancy code generation unit (8003) according to the embodiments performs octree coding on the positions of voxelized points based on an octree structure. The octree occupancy code generation unit (8003) can generate an occupancy code. The octree occupancy code generation unit (8003) can perform operations and / or methods identical or similar to the operations and / or methods of the point cloud encoder (or octree analysis unit (30002)) described in FIGS. 3 and 4. The specific description is the same as that described in FIGS. 1 through 6.

[0163] The surface model processing unit (8004) according to the embodiments can perform trisup geometry encoding that reconstructs the positions of points within a specific region (or node) based on a voxel based on a surface model. The surface model processing unit (8004) can perform operations and / or methods identical or similar to the operations and / or methods of the point cloud encoder (e.g., surface approximation analysis unit (30003)) described in FIG. 3. The specific description is the same as that described in FIG. 1 through 6.

[0164] According to the embodiments, the intra / inter coding processing unit (8005) can intra / inter code point cloud data. The intra / inter coding processing unit (8005) can perform coding identical or similar to the intra / inter coding described in FIG. 7. The specific description is the same as that described in FIG. 7. According to the embodiments, the intra / inter coding processing unit (8005) may be included in an arismetic coder (8006).

[0165] An arismetic coder (8006) according to the embodiments entropy-encodes an octree and / or approximated octree of point cloud data. For example, the encoding method includes an arismetic encoding method. The arismetic coder (8006) performs the same or similar operation and / or method as the arismetic encoder (30004).

[0166] A metadata processing unit (8007) according to the embodiments processes metadata regarding point cloud data, such as setting values, and provides it to necessary processing processes such as geometry encoding and / or attribute encoding. Additionally, a metadata processing unit (8007) according to the embodiments may generate and / or process signaling information related to geometry encoding and / or attribute encoding. The signaling information according to the embodiments may be encoded separately from geometry encoding and / or attribute encoding. Additionally, the signaling information according to the embodiments may be interleaved.

[0167] The color conversion processing unit (8008), attribute conversion processing unit (8009), prediction / lifting / RAHT conversion processing unit (8010), and arithmetic coder (8011) perform attribute encoding. Since the attribute encoding according to the embodiments is identical or similar to the attribute encoding described in FIGS. 1 to 6, a detailed description is omitted.

[0168] The color conversion processing unit (8008) according to the embodiments performs color conversion coding that converts color values ​​included in attributes. The color conversion processing unit (8008) may perform color conversion coding based on reconstructed geometry. The description of the reconstructed geometry is the same as that described in FIGS. 1 through 6. In addition, it performs the same or similar operation and / or method as the operation and / or method of the color conversion unit (30006) described in FIG. 3. A detailed description is omitted.

[0169] The attribute transformation processing unit (8009) according to the embodiments performs attribute transformation that transforms attributes based on positions where geometry encoding has not been performed and / or reconstructed geometry. The attribute transformation processing unit (8009) performs operations and / or methods identical or similar to the operations and / or methods of the attribute transformation unit (30007) described in FIG. 3. A detailed description is omitted. The prediction / lifting / RAHT transformation processing unit (8010) according to the embodiments may code the transformed attributes by RAHT coding, prediction transformation coding, and lifting transformation coding, or a combination thereof. The prediction / lifting / RAHT transformation processing unit (8010) performs at least one of operations identical or similar to the operations of the RAHT transformation unit (30008), LOD generation unit (30009), and lifting transformation unit (30010) described in FIG. 3. In addition, the descriptions of predictive transformation coding, lifting transformation coding, and RAHT transformation coding are the same as those described in Figures 1 to 6, so a detailed description is omitted.

[0170] The arismetic coder (8011) according to the embodiments can encode coded attributes based on arismetic coding. The arismetic coder (8011) performs the same or similar operation and / or method as the operation and / or method of the arismetic encoder (300012).

[0171] A transmission processing unit (8012) according to embodiments may transmit each bitstream containing encoded geometry and / or encoded attributes and metadata information, or may transmit the encoded geometry and / or encoded attributes and metadata information by configuring them into a single bitstream. When the encoded geometry and / or encoded attributes and metadata information according to embodiments is configured into a single bitstream, the bitstream may include one or more sub-bitstreams. The bitstream according to embodiments may include signaling information and slice data, including SPS (Sequence Parameter Set) for sequence-level signaling, GPS (Geometry Parameter Set) for signaling of geometry information coding, APS (Attribute Parameter Set) for signaling of attribute information coding, and TPS (Tile Parameter Set) for tile-level signaling. The slice data may include information about one or more slices. One slice according to embodiments is one geometry bitstream (Geom0 0 ) and one or more attribute bitstreams (Attr0 0 , Attr1 0 It may include ).

[0172] A slice refers to a series of syntax elements representing all or part of a coded point cloud frame.

[0173] According to the embodiments, the TPS may include information regarding each tile (e.g., coordinate value information of a bounding box and height / size information, etc.) for one or more tiles. The geometry bitstream may include a header and a payload. The header of the geometry bitstream according to the embodiments may include identification information of a parameter set included in the GPS (geom_parameter_set_id), a tile identifier (geom_tile_id), a slice identifier (geom_slice_id), and information regarding data included in the payload, etc. As described above, the metadata processing unit (8007) according to the embodiments may generate and / or process signaling information and transmit it to the transmission processing unit (8012). According to the embodiments, the elements performing geometry encoding and the elements performing attribute encoding may share data / information with each other as indicated by the dotted lines. The transmission processing unit (8012) according to the embodiments may perform an operation and / or transmission method identical or similar to the operation and / or transmission method of the transmitter (10003). A detailed explanation is omitted as it is the same as that described in FIGS. 1 and 2.

[0174] FIG. 9 is an example of a receiving device according to embodiments.

[0175] The receiving device illustrated in FIG. 9 is an example of the receiving device (10004) of FIG. 1 (or the point cloud decoder of FIG. 10 and FIG. 11). The receiving device illustrated in FIG. 9 can perform at least one of the same or similar operations and methods as the operations and decoding methods of the point cloud decoder described in FIG. 1 to FIG. 11.

[0176] A receiving device according to the embodiments may include a receiving unit (9000), a receiving processing unit (9001), an arithmetic decoder (9002), an occupancy code-based octree reconstruction processing unit (9003), a surface model processing unit (triangle reconstruction, up-sampling, voxelization) (9004), an inverse quantization processing unit (9005), a metadata parser (9006), an arithmetic decoder (9007), an inverse quantization processing unit (9008), a prediction / lifting / RAHT inverse transformation processing unit (9009), a color inverse transformation processing unit (9010), and / or a renderer (9011). Each component of the decoding according to the embodiments may perform the inverse process of the components of the encoding according to the embodiments.

[0177] A receiver (9000) according to the embodiments receives point cloud data. The receiver (9000) may perform an operation and / or a receiving method identical or similar to the operation and / or receiving method of the receiver (10005) of FIG. 1. A detailed description is omitted.

[0178] A receiving processing unit (9001) according to the embodiments can obtain a geometry bitstream and / or an attribute bitstream from the received data. The receiving processing unit (9001) may be included in the receiving unit (9000).

[0179] The arismetic decoder (9002), the Occupancy code-based octree reconstruction processing unit (9003), the surface model processing unit (9004), and the inverse quantization processing unit (9005) can perform geometry decoding. Since the geometry decoding according to the embodiments is identical or similar to the geometry decoding described in FIGS. 1 to 10, a detailed description is omitted.

[0180] The arismetic decoder (9002) according to the embodiments can decode a geometry bitstream based on arismetic coding. The arismetic decoder (9002) performs the same or similar operation and / or coding as the operation and / or coding of the arismetic decoder (7000).

[0181] According to the embodiments, the Occupancy code-based octree reconstruction processing unit (9003) can reconstruct an octree by obtaining an Occupancy code from a decoded geometry bitstream (or information regarding geometry obtained as a result of decoding). The Occupancy code-based octree reconstruction processing unit (9003) performs the same or similar operations and / or methods as the octree synthesis unit (7001) and / or octree generation method. According to the embodiments, the surface model processing unit (9004) can perform trisup geometry decoding and related geometry reconstruction (e.g., triangle reconstruction, up-sampling, voxelization) based on the surface model method when trisup geometry encoding is applied. The surface model processing unit (9004) performs the same or similar operations as the surface offset synthesis unit (7002) and / or geometry reconstruction unit (7003).

[0182] The inverse quantization processing unit (9005) according to the embodiments can inverse quantize the decoded geometry.

[0183] A metadata parser (9006) according to the embodiments can parse metadata included in the received point cloud data, such as setting values, etc. The metadata parser (9006) can pass the metadata to geometry decoding and / or attribute decoding. A specific description of the metadata is omitted as it is the same as described in FIG. 8.

[0184] The arismetic decoder (9007), inverse quantization processing unit (9008), prediction / lifting / RAHT inverse transformation processing unit (9009), and color inverse transformation processing unit (9010) perform attribute decoding. Since attribute decoding is identical or similar to the attribute decoding described in FIGS. 1 to 10, a detailed description is omitted.

[0185] The arismetic decoder (9007) according to the embodiments can decode an attribute bitstream into arismetic coding. The arismetic decoder (9007) can perform decoding of the attribute bitstream based on reconstructed geometry. The arismetic decoder (9007) performs the same or similar operation and / or coding as the operation and / or coding of the arismetic decoder (7005).

[0186] The inverse quantization processing unit (9008) according to the embodiments can inverse quantize the decoded attribute bitstream. The inverse quantization processing unit (9008) performs the same or similar operation and / or method as the operation and / or inverse quantization method of the inverse quantization unit (7006).

[0187] According to the embodiments, the prediction / lifting / RAHT inverse transformation processing unit (9009) can process the reconstructed geometry and inverse quantized attributes. The prediction / lifting / RAHT inverse transformation processing unit (9009) performs at least one of the same or similar operations and / or decodings as the operations and / or decodings of the RAHT transformation unit (7007), LOD generation unit (7008), and / or inverse lifting unit (7009). According to the embodiments, the color inverse transformation processing unit (9010) performs inverse transformation coding to inversely transform the color values ​​(or textures) included in the decoded attributes. The color inverse transformation processing unit (9010) performs the same or similar operations and / or inverse transformation coding as the operations and / or inverse transformation coding of the color inverse transformation unit (7010). A renderer (9011) according to the embodiments can render point cloud data.

[0188] FIG. 10 shows an example of a structure that can be linked with a point cloud data transmission / reception method / device according to embodiments.

[0189] The structure of FIG. 10 represents a configuration in which at least one of a server (1060), a robot (1010), an autonomous vehicle (1020), an XR device (1030), a smartphone (1040), a home appliance (1050) and / or an HMD (1070) is connected to a cloud network (1010). The robot (1010), the autonomous vehicle (1020), the XR device (1030), the smartphone (1040), or the home appliance (1050) are referred to as devices. Additionally, the XR device (1030) may correspond to a point cloud data (PCC) device according to the embodiments or may be linked with a PCC device.

[0190] The cloud network (1000) may refer to a network that constitutes part of the cloud computing infrastructure or exists within the cloud computing infrastructure. Here, the cloud network (1000) may be configured using a 3G network, a 4G or LTE (Long Term Evolution) network, or a 5G network, etc.

[0191] The server (1060) is connected to at least one of a robot (1010), an autonomous vehicle (1020), an XR device (1030), a smartphone (1040), a home appliance (1050) and / or an HMD (1070) via a cloud network (1000) and can assist in at least some of the processing of the connected devices (1010 to 1070).

[0192] The HMD (Head-Mount Display) (1070) represents one of the types in which an XR device and / or PCC device according to the embodiments may be implemented. A device of the HMD type according to the embodiments includes a communication unit, a control unit, a memory unit, an I / O unit, a sensor unit, and a power supply unit, etc.

[0193] Hereinafter, various embodiments of the device (1010 to 1050) to which the above-described technology is applied are described. Here, the device (1010 to 1050) illustrated in FIG. 10 may be linked / coupled with a point cloud data transmission / reception device according to the above-described embodiments.

[0194] <PCC+XR>

[0195] The XR / PCC device (1030) may be implemented as a Head-Mount Display (HMD), a Head-Up Display (HUD) equipped in a vehicle, a television, a mobile phone, a smartphone, a computer, a wearable device, a home appliance, digital signage, a vehicle, a stationary robot, or a mobile robot by applying PCC and / or XR (AR+VR) technology.

[0196] The XR / PCC device (1030) can obtain information about surrounding space or real objects by analyzing 3D point cloud data or image data obtained through various sensors or from an external device to generate position data and attribute data for 3D points, and can render and output an XR object to be output. For example, the XR / PCC device (1030) can output an XR object containing additional information about a recognized object by associating it with the recognized object.

[0197] <PCC+XR+모바일폰>

[0198] The XR / PCC device (1030) can be implemented as a mobile phone (1040) or the like by applying PCC technology.

[0199] The mobile phone (1040) can decode and display point cloud content based on PCC technology.

[0200] <PCC+자율주행+XR>

[0201] The autonomous vehicle (1020) can be implemented as a mobile robot, vehicle, unmanned aerial vehicle, etc. by applying PCC technology and XR technology.

[0202] An autonomous vehicle (1020) equipped with XR / PCC technology may refer to an autonomous vehicle equipped with means for providing XR images, or an autonomous vehicle that is the subject of control / interaction within the XR images. In particular, the autonomous vehicle (1020) that is the subject of control / interaction within the XR images is distinguished from the XR device (1030) and can be interconnected with it.

[0203] An autonomous vehicle (1020) equipped with means for providing XR / PCC images can acquire sensor information from sensors including cameras and output XR / PCC images generated based on the acquired sensor information. For example, the autonomous vehicle (1020) can provide an XR / PCC object corresponding to a real object or an object in the screen to the occupant by providing an XR / PCC object by outputting an XR / PCC image with a HUD.

[0204] At this time, when the XR / PCC object is displayed on the HUD, at least a portion of the XR / PCC object may be displayed so as to overlap with the actual object to which the occupant's gaze is directed. On the other hand, when the XR / PCC object is displayed on a display provided inside the autonomous vehicle, at least a portion of the XR / PCC object may be displayed so as to overlap with an object on the screen. For example, the autonomous vehicle (1220) may display XR / PCC objects corresponding to objects such as lanes, other vehicles, traffic lights, traffic signs, motorcycles, pedestrians, buildings, etc.

[0205] VR (Virtual Reality) technology, AR (Augmented Reality) technology, MR (Mixed Reality) technology and / or PCC (Point Cloud Compression) technology according to the embodiments can be applied to various devices.

[0206] In other words, VR technology is a display technology that provides real-world objects or backgrounds solely as CG images. On the other hand, AR technology refers to a technology that displays virtual CG images alongside images of real objects. Furthermore, MR technology is similar to the aforementioned AR technology in that it mixes and combines virtual objects with the real world. However, it is distinguished from AR technology in that while AR technology maintains a clear distinction between real-world objects and virtual objects created from CG images, using virtual objects to complement real-world objects, MR technology regards virtual objects as having the same nature as real-world objects. To give a more specific example, the aforementioned MR technology is applied in hologram services.

[0207] However, recently, rather than clearly distinguishing between VR, AR, and MR technologies, they are also referred to as XR (extended Reality) technology. Therefore, embodiments of the present invention are applicable to all VR, AR, MR, and XR technologies. These technologies may utilize encoding / decoding based on PCC, V-PCC, and G-PCC technologies.

[0208] The PCC method / device according to the embodiments can be applied to a vehicle providing autonomous driving services.

[0209] Vehicles providing autonomous driving services are connected to PCC devices to enable wired / wireless communication.

[0210] When a point cloud data (PCC) transceiver according to the embodiments is connected to a vehicle for wired or wireless communication, it can receive and process content data related to AR / VR / PCC services that can be provided along with an autonomous driving service, and transmit it to the vehicle. Additionally, when the point cloud data transceiver is mounted on a vehicle, the point cloud transceiver can receive and process content data related to AR / VR / PCC services according to a user input signal received through a user interface device and provide it to the user. A vehicle or a user interface device according to the embodiments can receive a user input signal. The user input signal according to the embodiments may include a signal indicating an autonomous driving service.

[0211] Predictor search

[0212] General process

[0213] The points used to predict refinement points for each detail level must be determined by exploration. The result of this process is to represent the point predictors in the state variables PredCnt, PredPtIdx, PredPtRef, and PredWeight. When attr_coding_type is 2, exploration should not be performed for refinement points of the coarsest detail level.

[0214] maxLvl = LodCnt - (attr_coding_type == 2)

[0215] for (Lvl = 0; Lvl < maxLvl; Lvl++)

[0216] for (RfmtIdx = 0; RfmtIdx < LodRfmtPtCnt[Lvl]; RfmtIdx++) {

[0217] … / * find predictors (10.6.6.3) of the current point * /

[0218] }

[0219] Minimum reference detail level for inter-level predictor searches

[0220] The variable MinInterRefLvl identifies the finest detail level to be used as a reference for prediction between detail levels. When lod_scalability_enabled is 1, this must be the finest detail level with fewer refinement points than the total number of refinement points associated with all finer detail levels.

[0221] MinInterRefLvl = 1

[0222] if (lod_scalability_enabled) {

[0223] for (lvl = 1; lvl < LodCnt - 1; lvl++) {

[0224] if (LodRfmtPtCnt[lvl] < slice_num_points_minus1 - LodPtCnt[lvl])

[0225] break

[0226] MinInterRefLvl++

[0227] }

[0228] }

[0229] Predictor search for a single refinement point

[0230] For a refinement point at index RfmtIdx in detail level Lvl, a search must be performed to find the nearest neighbor points from the candidate neighbor set. The search process is specified by the following variables:

[0231] Variable PtIdx: This is the AttrPos index of the refinement point.

[0232] Variable RefLvl: The reference detail level used for predictor search between levels.

[0233] PtIdx = LodRfmtPtIdx[RfmtIdx]

[0234] RefLvl = Max(Lvl + 1, MinInterRefLvl)

[0235] Inter-detail-level search must be performed before any intra-level search. Excluding the coarsest detail level, the following inter-detail-level searches should be performed:

[0236] Initial search

[0237] If fewer than 3 predictors are found (PredCnt[PtIdx] < 3), extended search

[0238] When Lvl is greater than or equal to pred_intra_min_lod, an intra-detail-level search should be performed.

[0239] When slice_attr_inter_prediction is equal to 1, inter-frame search must be performed after inter-level search and within-level search.

[0240] The following inter-frame search must be performed:

[0241] Initial inter-frame search

[0242] If attr_inter_extended_search_enabled is equal to 1 and fewer than 3 predictor candidates are found during the initial inter-frame search, Extended inter-frame search

[0243] After the search is complete, weights for each predictor must be calculated, and during this process, the set of predictors is pruned and reordered. When pred_blending_enabled is 1, the predictor weights must be blended.

[0244] Inclusion of a candidate point in the predictor set (InsertPredictor)

[0245] This subsection defines the InsertPredictor(candPtIdx, candRef) function, which conditionally inserts candidate points into the predictor set of the current refinement points. Each candidate must be tested against the predictor set of the refinement points to determine whether to insert and where to insert them.

[0246] The parameter candPtIdx is the AttrPos index or RefAttrPos index of the candidate point. The parameter candRef specifies whether the candidate point was found in the reference slice. When prediction_with_distribution_enabled is 1, the parameter id2 is the additional predictor index of the candidate point and is initialized to 3 at the start of coding for the current refinement point.

[0247] A candidate point must be inserted into the point's neighbor set only once. If candPresent is 1, the candidate is not inserted into the predictor set.

[0248] candPresent = 0

[0249] for (i = 0; i < PredCnt[PtIdx]; i++)

[0250] candPresent |= PredPtIdx[PtIdx][i] == candPtIdx

[0251] && (slice_attr_inter_prediction

[0252] PredPtRef[PtIdx][i] == candRef

[0253] : 1)

[0254] Otherwise (if the candidate does not already exist), the following must be used to determine whether to include the predictor set.

[0255] Spatial distance between the candidate and the refinement point.

[0256] The candidate's relative spatial location with respect to the current point.

[0257] The distance should be calculated using a biased L1 norm weighted by PredBias.

[0258] dist = BiasedNorm1(PtIdx, candPtIdx, 0, candRef)

[0259] Points must be inserted into the predictor set along with elements sorted according to the biased L1 distance to the refine point. When prediction_with_distribution_enabled is 1 and the size of the predictor set is less than 6, a point is inserted if the distance between the point and the current point is equal to the distance between the third predictor in the predictor set and the current point. Points with the same distance are sorted according to the insertion order, with the member that entered first placed before the member that entered later.

[0260] i = 0

[0261] insert = 0

[0262] for (; i < min(3, PredCnt[PtIdx]); i++){

[0263] if (dist < BiasedNorm1(PtIdx, PredPtIdx[PtIdx][i], 0, PredPtRef[PtIdx][i])){

[0264] insert = 1

[0265] break

[0266] }

[0267] }

[0268]

[0269] if(insert){

[0270] if(prediction_with_distribution_enabled && PredCnt[PtIdx] > 2){

[0271] PredPtIdx[PtIdx][id2] = PredPtIdx[PtIdx][2]

[0272] PredPtRef[PtIdx][id2] = slice_attr_inter_prediction ? PredPtRef[PtIdx][2] : 0

[0273] id2++

[0274] }

[0275] for (j = min(2, PredCnt[PtIdx]); j > i; j--){

[0276] PredPtIdx[PtIdx][j] = PredPtIdx[PtIdx][j - 1]

[0277] if (slice_attr_inter_prediction)

[0278] PredPtRef[PtIdx][j] = PredPtRef[PtIdx][j - 1]

[0279] }

[0280] PredPtIdx[PtIdx][i] = candPtIdx

[0281] PredPtRef[PtIdx][i] = slice_attr_inter_prediction candRef : 0

[0282] PredCnt[PtIdx]++

[0283] }

[0284] else if(prediction_with_distribution_enabled

[0285] && dist == BiasedNorm1(PtIdx, PredPtIdx[PtIdx][2], 0, PredPtRef[PtIdx][2])

[0286] && PredCnt[PtIdx] < 6){

[0287] PredPtIdx[PtIdx][id2] = candPtIdx

[0288] PredPtRef[PtIdx][id2] = slice_attr_inter_prediction ? candRef : 0

[0289] PredCnt[PtIdx]++

[0290] id2++

[0291] }

[0292]

[0293] if(prediction_with_distribution_enabled && id2 == 6)

[0294] id2 = 3

[0295] PredCnt[PtIdx] = min((prediction_with_distribution_enabled ? 6 : 3) , PredCnt[PtIdx])

[0296] The size of the predictor set should be limited to 3 elements (when prediction_with_distribution_enabled is 0) or 6 elements (when prediction_with_distribution_enabled is 1) by discarding the furthest predictor if necessary.

[0297] Initial inter-level predictor search

[0298] Initial inter-level search should be performed by spatially partitioning the reference detail level into a cubic block lattice of size 2BlkSizeLog2. Only blocks adjacent to blocks containing refinement points that are within the availability window should be searched.

[0299] BlkSizeLog2 := lod_initial_dist_log2 + lod_dist_log2_offset + Lvl + 1

[0300] Block location (bs, bt, bv) identifies the block containing the refinement point.

[0301] bs := AttrPos[PtIdx][0] >> BlkSizeLog2

[0302] bt := AttrPos[PtIdx][1] >> BlkSizeLog2

[0303] bv := AttrPos[PtIdx][2] >> BlkSizeLog2

[0304] The availability window must be a 128 * 128 * 128 block volume identified by (bs >> 7, bt >> 7, bv >> 7).

[0305] Searches must be performed on the search blocks in the specified order. Within each search block, points must be searched in ascending order of indices within the reference detail level.

[0306] for (si = 0; si < 27; si++) {

[0307] ss = bs + searchBlkOffsets[si][0]

[0308] st = bt + searchBlkOffsets[si][1]

[0309] sv = bv + searchBlkOffsets[si][2]

[0310] unavailable = (ss ^ bs) >> 7 || (st ^ bt) >> 7 || (sv ^ bv) >> 7

[0311] if (unavailable)

[0312] continue

[0313] for (i = 0; i < LodPtCnt[RefLvl]; i++) {

[0314] candPtIdx = LodPtIdx[RefLvl][i]

[0315] cs = AttrPos[candPtIdx][0]

[0316] ct = AttrPos[candPtIdx][1]

[0317] cv = AttrPos[candPtIdx][2]

[0318] inSblk = cs ≥ (ss << BlkSizeLog2) && cs < (ss + 1 << BlkSizeLog2)

[0319] inSblk &= ct ≥ (st << BlkSizeLog2) && ct < (st + 1 << BlkSizeLog2)

[0320] inSblk &= cv ≥ (sv << BlkSizeLog2) && cv < (sv + 1 << BlkSizeLog2)

[0321] if (inSblk)

[0322] InsertPredictor(candPtIdx, 0)

[0323] }

[0324] }

[0325] For each search block, indices i for which inSblk is true are consecutive.

[0326] The table below shows the relative search block coordinates (search block coordinates, searchBlkOffsets[i][k]) for ( bs, bt, bv ).

[0327]

[0328] Extended inter-level search

[0329] Search between extension levels evaluates predictor candidates across the index span of the reference detail level.

[0330] This range must be centered on the center index of the reference detail level. This must be the following index:

[0331] If at least one predictor is found for the current point: the first predictor; or

[0332] if (PredCnt[PtIdx])

[0333] for (centre = 0; center < LodPtCnt[RefLvl] - 1; center++)

[0334] if (LodPtIdx[RefLvl][centre] == PredPtIdx[PtIdx][0])

[0335] break

[0336] Otherwise (no predictor found): The first point with Morton-coded attribute coordinates greater than the current point.

[0337] if (PredCnt[PtIdx] == 0) {

[0338] mortonCurPt = Morton[AttrPos[PtIdx]]

[0339] for (centre = 0; center < LodPtCnt[RefLvl] - 1; center++) {

[0340] mortonCentre = Morton[AttrPos[LodPtIdx[RefLvl][centre]]]

[0341] if (mortonCurPt < mortonCentre)

[0342] break

[0343] }

[0344] }

[0345] The search range for searching between expansion levels is specified by the variable interLodSearchRange.

[0346] interLodSearchRange = slice_inter_lod_search_range

[0347] Extended search must be performed in order for each of the following index offsets i: 0, +1, -1, +2, -2, +3 … interLodSearchRange, and -(3 … interLodSearchRange).

[0348] Predictor candidates must be evaluated for each valid search index centre + i that is within the range specified by interLodSearchRange and does not exceed the boundaries of the reference detail level.

[0349] if (Abs(i) ≤ interLodSearchRange)

[0350] if (centre + i ≥ 0 && center + i < LodPtCnt[RefLvl])

[0351] InsertPredictor(LodPtIdx[RefLvl][centre + i], 0)

[0352] Intra-level search

[0353] In-level search evaluates predictor candidates across a range of indices in the refinement list of the current detail level. In-level predictor candidates must precede the refinement point in the refinement list.

[0354] The search range for in-level search is specified by the variable intraLodSearchRange.

[0355] intraLodSearchRange = slice_intra_lod_search_range

[0356] Predictor candidates must be evaluated for each valid search index offset -i (i = 1 … intraLodSearchRange) from the refine point, and this must not exceed the boundaries of the refine list.

[0357] for (i = 1; i ≤ Min(RfmtIdx, intraLodSearchRange); i++)

[0358] InsertPredictor(LodRfmtPtIdx[Lvl][RfmtIdx - i], 0)

[0359] Initial inter-frame predictor search

[0360] Initial inter-frame search should be performed by spatially partitioning the reference slice into a grid of 2BlkSizeLog2 cubic blocks. Only blocks adjacent to a block containing a refine point that is within the availability window should be searched.

[0361] BlkSizeLog2 := lod_initial_dist_log2 + lod_dist_log2_offset

[0362] Block location (bs, bt, bv) identifies the block containing the refinement point.

[0363] bs := AttrPos[PtIdx][0] >> BlkSizeLog2

[0364] bt := AttrPos[PtIdx][1] >> BlkSizeLog2

[0365] bv := AttrPos[PtIdx][2] >> BlkSizeLog2

[0366] The availability window must be an 8 * 8 * 8 block volume identified by (bs >> 3, bt >> 3, bv >> 3).

[0367] Searches must be performed on the search blocks in the specified order. Within each search block, points must be searched in ascending order of their indices within the reference slice.

[0368] ss = bs + searchBlkOffsets[0][0]

[0369] st = bt + searchBlkOffsets[0][1]

[0370] sv = bv + searchBlkOffsets[0][2]

[0371] unavailable = (ss ^ bs) >> 3 || (st ^ bt) >> 3 || (sv ^ bv) >> 3

[0372] if (unavailable)

[0373] continue

[0374] for (i = 0; i < RefLodPtCnt; i++) {

[0375] candPtIdx = RefLodPtIdx[i]

[0376] cs = RefAttrPos[candPtIdx][0]

[0377] ct = RefAttrPos [candPtIdx][1]

[0378] cv = RefAttrPos[candPtIdx][2]

[0379] inSblk = cs ≥ (ss << BlkSizeLog2) && cs < (ss + 1 << BlkSizeLog2)

[0380] inSblk &= ct ≥ (st << BlkSizeLog2) && ct < (st + 1 << BlkSizeLog2)

[0381] inSblk &= cv ≥ (sv << BlkSizeLog2) && cv < (sv + 1 << BlkSizeLog2)

[0382] if (inSblk)

[0383] InsertPredictor(candPtIdx, 1)

[0384] }

[0385] Note: For each search block, indices i for which inSblk is true are consecutive.

[0386] Extended inter-frame search

[0387] When attr_inter_extended_search_enabled is 1, the following inter-extended frame search should be performed.

[0388] Cross-frame search evaluates predictor candidates across the index range at the finest detail level of the reference slice.

[0389] This range must be centered at the centerRef index of the finest detail level of the reference slice. This must be the index of the first point with a Morton code property coordinate greater than or equal to the current point.

[0390] mortonCurPt = Morton[AttrPos[PtIdx]]

[0391] for (centreRef = 0; centreRef < RefLodPtCnt - 1; centreRef++) {

[0392] mortonCentre = Morton[RefAttrPos[RefLodPtIdx[centreRef]]]

[0393] if (mortonCurPt <= mortonCentre)

[0394] break

[0395] }

[0396] The search range for inter-frame search is specified by the variable interFrameSearchRange.

[0397] interFrameSearchRange := (slice_biprediction && slice_attr_inter_prediction &&

[0398] slice_attr_inter_prediction2) attr_inter_prediction_search_range >> 1:

[0399] attr_inter_prediction_search_range

[0400] Inter-frame search must be performed in order for each of the following index offsets i: 0, +1, -1, +2, -2, +3 … interFrameSearchRange, and -(3 … interFrameSearchRange).

[0401] Predictor candidates must be evaluated for each valid search index centreRef + i that is within the range specified by interFrameSearchRange and does not exceed the boundaries of the finest detail level of the reference slice.

[0402] if (Abs(i) ≤ interFrameSearchRange)

[0403] if (centreRef + i ≥ 0 && centerRef + i < RefLodPtCnt)

[0404] InsertPredictor(RefLodPtIdx[centreRef + i], 1)

[0405] The encoding method and apparatus according to the embodiments may include and perform the transmission device (10000) of FIG. 1, acquisition (20000) to transmission (20002) of FIG. 2, encoder of FIG. 3 and FIG. 8, each device of FIG. 10, encoders of FIG. 11 to FIG. 12, encoding of FIG. 15 to FIG. 17, bitstream and parameter generation of FIG. 18 to FIG. 25, method of FIG. 26, etc.

[0406] The decoding method and apparatus according to the embodiments may include and perform the receiving device (10004) of FIG. 1, receiving (20002) to rendering (20004) of FIG. 2, decoders of FIG. 7 and FIG. 9, each device of FIG. 10, decoders of FIG. 13 to FIG. 14, decoding of FIG. 15 to FIG. 17, bitstream and parameter acquisition of FIG. 18 to FIG. 25, method of FIG. 27, etc.

[0407] The method according to the embodiments may include an encoding / decoding method, and the device may include an encoding / decoding device. The method and device according to the embodiments may include and perform an inter-frame nearest neighbor search method for point cloud compression.

[0408] The embodiments include an inter-frame nearest neighbor search method to increase the compression efficiency of attributes of Geometry-based Point Cloud Compression (G-PCC) of a point cloud frame. The embodiments include an inter-frame nearest neighbor search method and a signaling method. The method and apparatus according to the embodiments may include and perform the aforementioned predictor search process.

[0409] The encoding device according to the embodiments may be referred to as an encoder or an encoder, and the decoding device may be referred to as a decoder or a decoder.

[0410] A point cloud consists of a set of points, and each point can have geometry information and attribute information. Geometry information is 3D position (XYZ) information, and attribute information is color (RGB, YUV, etc.) or / and reflection values.

[0411] The G-PCC encoding process can divide the point cloud into tiles according to region and divide each tile into slices for parallel processing. It can be composed of a process of compressing geometry on a slice-by-slice basis and compressing attribute information based on reconstructed geometry (decoded geometry) with location information changed through compression.

[0412] The G-PCC decoding process can be composed of receiving a geometry bitstream and an attribute bitstream in the form of encoded slice units, decoding the geometry, and decoding attribute information based on the geometry reconstructed through the decoding process.

[0413] Octree-based, predictive tree-based, or trisoup-based compression techniques can be used for geometry information compression. Predicting transform-based, lifting transform-based, or RAHT transform-based compression techniques can be used for attribute information compression. In predicting transform and lifting transform techniques, points can be divided and grouped into Levels of Detail (hereinafter referred to as LOD).

[0414] Every point can have a single predictor. A predictor can have multiple neighbor points. These neighbor points may be points belonging to the same LOD level as the current point, points belonging to a higher LOD level than the current point, or points belonging to a reference frame that is different from the frame to which the current point belongs. In other words, a single predictor can select and register multiple points from the same LOD level, different LOD levels, or reference frames. Distance can be used as one method to determine registration. That is, since the point closest to the current point may have attribute values ​​similar to the current point, N nearest neighbors are searched based on distance and registered in the predictor.

[0415] Attributes can be predicted using the nearest neighbor points registered in the predictor via the method described above. The average of the weighted values ​​obtained by multiplying the attributes of the registered neighbor points can be used as the predicted result, or a specific point can be used as the predicted result. The method to be used can be selected by calculating the compressed result value in advance and choosing the method that generates the smallest stream.

[0416] The residuals of the attribute values ​​of the point and the attribute values ​​predicted by the point's predictor can be encoded together with the method selected by the predictor and signaled to the receiver.

[0417] In the decoder, the process described above is performed, and subsequently, the transmitted prediction method is decoded to predict the attribute value according to that method. The attribute value can be restored by decoding the transmitted residual value and adding the predicted value.

[0418] The embodiments aim to improve encoding efficiency for attribute compression by finding nearest neighbors through a search method of narrow range of inter-frame neighbor points in nearest neighbor search and using them for prediction.

[0419] In conventional point cloud compression, the search range for neighbor points within a frame and the search range for neighbor points between frames were used identically. However, since the distribution of points within a frame and the distribution of points between frames may differ, and in particular, points within a frame may have different distributions depending on the LOD level, the search between frames may be performed over an unnecessarily wide range, resulting in high encoding / decoding complexity. Furthermore, if accurate motion prediction and compensation are not performed, an unnecessarily wide search range may register neighbor points that adversely affect encoding / decoding to the predictor. Therefore, to solve these problems, the present invention aims to achieve improved encoding / decoding speed and enhanced attribute encoding efficiency by performing a narrow-range search for neighbor points between frames. The method and apparatus according to the embodiments may include and perform a cross-attribute weight generation process.

[0420] Neighbor Point Exploration

[0421] Neighbor point groups can be determined using the distance and distribution between the current point and surrounding points. In this case, points that are close to each other can be selected based on distance. Additionally, the distribution can be determined to include neighbor points from different directions within the group to prevent the selected number of neighbor points from being clustered in one direction. Furthermore, in addition to points in the current frame, points in the reference frame—which is an already restored frame—can also be targeted for neighbor point search. Therefore, a neighbor point group may include points from both the current frame and the reference frame, and each neighbor point may have a flag or information indicating whether it belongs to the current frame or the reference frame.

[0422] Here, since the computational complexity of the point cloud encoder and decoder would be too high if they calculated the distance between each point and all other points, they can perform a hierarchical neighbor point search centered on the current point.

[0423] Here, hierarchical neighbor point search can be divided into a first search and a second search. In hierarchical neighbor point search, the first search may be performed, followed by the second search. Alternatively, only the first search or only the second search may be performed.

[0424] Here, the first search may be a search method that spatially explores the space surrounding the current point. And the second search may be a method that sequentially searches the surroundings in terms of point order.

[0425] The first search and the second search can be used not only for searching neighbor points within a screen but also for searching neighbor points between screens. That is, hierarchical neighbor point search may include one or more of the following: a first search for neighbor points of different LODs within a screen, a second search for neighbor points of different LODs within a screen, a first search for neighbor points of the same LOD within a screen, a second search for neighbor points of the same LOD within a screen, a first search for neighbor points of the same LOD within a screen, a first search for neighbor points between screens, and a second search for neighbor points between screens.

[0426] In addition, if one or more searches are performed, specific searches can be omitted by checking conditions in the middle.

[0427] The neighbor point search according to the embodiments may be referred to as a predictor search. The first search may be referred to as an initial search, and the second search as an extended search. The first search for neighbor points of different LODs within the screen may be referred to as an initial inter-level search, and the second search for neighbor points of different LODs within the screen may be referred to as an extended inter-level search. Additionally, the first search for neighbor points of the same LOD within the screen may be referred to as an initial inter-level search, and the second search for neighbor points of the same LOD within the screen may be referred to as an extended inter-level search; the intra-level search may include the initial inter-level search or the extended inter-level search. Furthermore, the first search for neighbor points between screens may be referred to as an initial inter-frame search, and the second search for neighbor points between screens may be referred to as an extended inter-frame search.

[0428] To predict the attribute value of the current point, neighbor point groups can be established using the distance and distribution between the current point and surrounding points. Points close to the current point can be selected as neighbor point groups, and neighbor points in different directions can be established as neighbor point groups. Additionally, not only points included in the current frame but also points in the reference frame (the restored frame) can be established as neighbor point groups, and each neighbor point may include flag information indicating whether it belongs to the current frame or the reference frame.

[0429] Neighbor point search may include a first search that spatially searches the area around the current point, and a second search that sequentially searches the area around the point in order. Additionally, neighbor point search may include an in-screen other LOD search that searches for neighbor points included in a different LOD of the current frame, an in-screen same LOD search that searches for neighbor points included in the same LOD of the current frame, and an inter-screen search that searches for neighbor points included in a reference frame other than the current frame. Furthermore, the first search may include an in-screen other LOD first search, an in-screen same LOD first search, and / or an inter-screen first search. Additionally, the second search may include an in-screen other LOD second search, an in-screen same LOD second search, and / or an inter-screen second search.

[0430] Neighbor point search can be performed hierarchically in order. A second search may be performed after the first search. Additionally, it may be performed in the order of searching for different LODs within the screen, searching for the same LOD within the screen, and searching between screens. Furthermore, neighbor point search may include, in order, at least one of the following: a first search for different LODs within the screen, a second search for different LODs within the screen, a first search for the same LOD within the screen, a second search for the same LOD within the screen, a first search between screens, or a second search between screens. Additionally, if certain conditions are satisfied, one or more searches may be omitted.

[0431] First search method

[0432] The first search is a method of searching the area surrounding the current point in space. Here, "area" may refer to the spatial search range. That is, for points within the search range, the spatial distance from the current point is calculated, and they can be sequentially registered in the predictor as neighbor points. Neighbor points can be registered in the predictor in order of shortest distance from each other.

[0433] At this time, the predictor can register up to the maximum number of neighbors (maxNeighCount). That is, after the maximum number is registered, the neighbor points that are furthest away from the current neighbor point, including the current neighbor point, can be removed from the predictor based on their distances.

[0434] In this case, the predictor can use a specific value for maxNeighCount. For example, maxNeighCount can be 3 or 6. 3 and 6 can be agreed-upon values ​​between the encoder and the decoder. 3 and 6 can be controlled via a flag (prediction_with_distribution_enabled) that considers point distribution characteristics, and such flag can be transmitted from the encoder to the decoder via the bitstream.

[0435] maxNeighCount = prediction_with_distribution_enabled ? 6:3

[0436] As another embodiment, the predictor determines the maximum number to be registered in the predictor based on information on the maximum number of neighbor points (pred_set_size_minus1), and neighbor points in order greater than the maximum number may be removed from the neighbor point list. pred_set_size_minus1 may be information transmitted from the encoder to the decoder via a bitstream.

[0437] maxNeighCount = pred_set_size_minus1+1

[0438] As another embodiment, the predictor can determine the maximum number to register to the predictor based on information on the maximum number of neighboring points (pred_set_size_minus1) and a flag (prediction_with_distribution_enabled) that considers point distribution characteristics.

[0439] maxNeighCount = prediction_with_distribution_enabled ? 2*(pred_set_size_minus1+1) : (pred_set_size_minus1+1)

[0440] Here, the distance between the current point and the neighbor point can be the sum of the squares of the current point location vector and the neighbor point location vector. Or the sum of the absolute values ​​can be used.

[0441] The search range of the first search can be expressed in node units. For example, a search can be performed over K x K x K nodes centered on the node containing the current point. If K is 3, the search is performed over a 3D cube area of ​​3 x 3 x 3 centered on the current node, and the total number of nodes can be 27. The value of K can also be 1. Alternatively, K can be set to 1 for cross-screen neighbor point search and to 3 for intra-screen neighbor point search.

[0442] In this case, the size of a single node can be represented by S x S x S voxels. That is, the search range expressed in voxel units can be a KS x KS x KS voxel space.

[0443] According to the embodiments, S can be calculated as an offset value representing the node size.

[0444] S = 1 << (offset)

[0445] According to embodiments, S can be calculated based on dist2, a parameter that reflects the distribution characteristics of the point cloud. In one embodiment, S can be calculated as 2 to the power of (dist2+offset). For example, if the dist2 value is 0 and the offset value is 1, the size of one node can be 2 x 2 x 2 voxels.

[0446] S = 1 << (dist2+offset)

[0447] According to the embodiments, S can be calculated based on dist2 and the LOD level. In one embodiment, S can be calculated using dist2, an offset, and an LOD index (lod_index). The reason for considering the LOD index here is that the distribution characteristics differ due to subsampling during the process of constructing the LOD structure through subsampling. Here, lod_index may be the value obtained by differing the current LOD level from the total number of LODs. This is because nearest neighbor search starts from the details.

[0448] S = 1 << (dist2+offset+lod_index)

[0449] According to the embodiments, S may use different values ​​depending on the dist2 value. If the dist2 value is 0, S may be calculated as the first offset, offset0. Conversely, if dist2 is not 0, S may be calculated as dist2, the second offset, and the LOD index.

[0450] if (dist2 == 0),

[0451] S = 1< <offset0

[0452] Else

[0453] S = 1 << (dist2+offset1+lod_index)

[0454] According to the embodiments, S may use different values ​​depending on the dist2 value and whether inter-frame prediction is used. If the dist2 value is 0 and inter-frame prediction is used, S may be calculated as the first offset, offset0. Conversely, if dist2 is not 0 or inter-frame prediction is not used, S may be calculated as dist2, the second offset, and the LOD index.

[0455] if (dist2 == 0 && isInter==1),

[0456] S = 1< <offset0

[0457] Else

[0458] S = 1 << (dist2+offset1+lod_index)

[0459] According to embodiments, S may be calculated as different values ​​depending on the narrow_search_mode_flag. Different values ​​may be used depending on the dist2 value and whether inter-frame prediction is used. If the narrow_search_mode_enabled_flag determined at the upper level is 1, the dist2 value is 0, and inter-frame prediction is used, the narrow_search_mode_flag may be determined as 1. Subsequently, if narrow_search_mode_flag is 1, S may be calculated as the first offset, offset0. Conversely, if narrow_search_mode_flag is 0, S may be calculated as dist2, the second offset, and the LOD index.

[0460] narrow_search_mode_flag = narrow_search_mode_enabled_flag && (dist2 == 0 && isInter==1)

[0461] if (narrow_search_mode_flag),

[0462] S = 1< <offset0

[0463] Else

[0464] S = 1 << (dist2+offset1+lod_index)

[0465] Here, S can be calculated as a separate value in in-screen neighbor point search and inter-screen neighbor point search, and in the embodiments described above, S can be interpreted as the size of the node used in in-screen neighbor point search or inter-screen neighbor point search.

[0466] In addition, for narrow range search mode, K can be set to 1 instead of 3.

[0467] The first navigation may include other LOD first navigations within the screen and / or first navigations between screens.

[0468] First search refers to a method of searching the surroundings of a current point, which may mean a method of performing a search for neighboring points within a spatial search range established around the current point. In the case of first search for other LODs within a screen, the search range may be set for other LODs within the same frame or slice, and in the case of first search between screens, the search range may be set for a reference frame or reference slice.

[0469] The search range of the first search can be expressed in node units or block units. The size of a single node can be expressed as SxSxS voxels.

[0470] According to embodiments, S can be set based on at least one of an offset value, a distance parameter dist2 reflecting the distribution characteristics of the point cloud, or an LOD level (lod_index) as follows.

[0471] S = 1 << (offset);

[0472] S = 1 << (dist2+offset); or

[0473] S = 1 << (dist2+offset+lod_index)

[0474] According to embodiments, S may be set to a first value in screen-in-neighbor point search and to a second value in screen-to-neighbor point search, and the first value and the second value may be different from each other.

[0475] The search range can be set to a size of KxKxK nodes centered on a node or block containing the current point. For example, if K is 3, the search range can be set to a 3x3x3 area of ​​a 3D cube centered on the current node, and the total number of nodes included in the search range can be 27.

[0476] And K can be set differently for each type of search. For example, K can be set to 1 for cross-screen neighbor point search and K can be set to 3 for intra-screen neighbor point search.

[0477] Second search method

[0478] The second search may be a method of searching the surroundings in order. The list of neighboring points may be sorted in Molton code order or standard order. Therefore, a search can be performed on the points before and after the current point within the list based on the order of the current point. Here, the sequential search range can be determined based on the value transmitted from the encoder to the decoder, and searches within the same LOD and searches between different LODs may have different search ranges.

[0479] According to the embodiments, in the case of a narrow-range search mode, a separate search range may be provided. The narrow-range search mode may also be included in the bitstream and transmitted from the encoder to the decoder.

[0480] The second search may include a second search for a different LOD within the screen, a second search for the same LOD within the screen, and / or a second search between screens.

[0481] The second search may be a method of searching the surroundings according to the index order. The neighboring point list may be sorted by Morton code or standard order, and the second search may perform a search on the preceding and succeeding points based on the current point's order according to the index order of the sorted points.

[0482] Method to skip the second search

[0483] The second search may be omitted depending on the currently registered neighbor points.

[0484] For example, if three neighbor points are registered in the first search, the second search can be omitted. Since three neighbor points have already been registered through a narrow range search, the second search is not necessary, and subsequent processes can be omitted to accelerate the encoder and decoder. The above omission process can be used only in the search for neighbor points within the screen.

[0485] According to embodiments, whether to omit the second search may be determined based on the received maximum neighbor point count information (pred_set_size_minus1). For example, if the neighbor points registered in the predictor of the current point are equal to or greater than “pred_set_size_minus1+1”, the second search may be omitted.

[0486] According to the embodiments, it may be omitted depending on whether a narrow area search mode is used. For example, if narrow_search_mode_flag is 1, the second search may be omitted.

[0487] According to the embodiments, if the narrow_search_mode_flag is 1 in the inter-screen neighbor point search, the second search of the inter-screen neighbor point search may be omitted.

[0488] The second search may be omitted. According to embodiments, the second search may be omitted based on the number of points registered by the first search. For example, if three neighbor points are registered by the first search, the second search may be omitted. According to embodiments, the second search may be omitted based on signaled maximum neighbor point count information (pred_set_size_minus1). For example, the second search may be omitted if the number of points registered in the neighbor point group of the current point is greater than or equal to pred_set_size_minus1+1. According to embodiments, the second search may be omitted based on narrow area search mode information (narrow_search_mode_flag). The narrow area search mode information (narrow_search_mode_flag) may be used as flag information indicating whether the second search is performed. For example, if narrow_search_mode_flag is 1, the second search may be omitted.

[0489] FIG. 11 shows an encoder according to embodiments.

[0490] Other encoding methods and devices in embodiments (transmitting device (10000) in FIG. 1, acquisition (20000) to transmission (20002) in FIG. 2, encoder in FIG. 3, encoder in FIG. 8, each device in FIG. 10, encoder in FIG. 11 to FIG. 12, encoding in FIG. 15 to FIG. 17, bitstream and parameter generation in FIG. 18 to FIG. 25, method in FIG. 26) may be configured as in FIG. 11.

[0491] A PCC data encoder is a configuration for performing encoding of PCC data and may include a configuration having the function of FIG. 11. Each component may correspond to hardware, software, a processor, and / or a combination thereof. PCC data may be fed into the encoder as input, encoded, and output as a geometry information bitstream and an attribute information bitstream.

[0492] The data input section can read and configure input data (e.g., ply, configuration file, etc.). The coordinate system transformation section can support coordinate system changes, such as changing the xyz axes or transforming from an xyz orthogonal coordinate system to a spherical coordinate system.

[0493] The geometry information conversion quantization processing unit can adjust the scale by multiplying the geometry position x, y, and z values ​​of the point cloud points by the scale (scale = geometry quantization value) according to the scale setting. The spatial partitioning unit can divide the content into tiles or slices for region-by-region access or parallel processing. The geometry encoding unit can generate a geometry information bitstream by encoding the spatially partitioned geometry information.

[0494] The color conversion processing unit can support attribute type conversion, such as changing RGB colors to YUV. The color recalculation unit can predict an attribute value suitable for the changed location when the geometry is scaled and the location information value is changed.

[0495] The attribute information encoding unit can generate an attribute information bitstream by receiving the color-reset original attribute information, restored geometry information, and reference frame as input and performing encoding. The reference frame generation unit can store the restored geometry and restored attribute information in a reference frame buffer and transmit reference frame data from the reference frame to another module.

[0496] FIG. 11 is a block diagram illustrating the functions of a point cloud data encoding device. A data input unit according to embodiments receives input data including geometry data, attribute data, and parameters, and can perform loading and setting in a form available for use in subsequent modules. A coordinate system conversion unit according to embodiments can convert the coordinate representation of the input geometry data (e.g., changing the xyz axes, converting from a Cartesian coordinate system to a spherical coordinate system). A geometry information conversion quantization processing unit according to embodiments can adjust the position values ​​of the geometry data by enlarging or shrinking them by multiplying the position information (x, y, z) of a point by a scale (geometry quantization value). A spatial partitioning unit according to embodiments can divide the original point cloud data into tiles or slices for region-specific access or parallel processing, and can output the divided original attribute data to provide for attribute processing. The geometry information encoding unit according to the embodiments can encode spatially partitioned geometry information to generate a geometry information bitstream, or output restored geometry information to provide for attribute processing.

[0497] The divided attribute original data according to the embodiments may be provided to the color recoloring unit after undergoing color conversion (e.g., converting RGB colors to YUV) in the color conversion processing unit. The color recoloring unit may make the attribute data spatially aligned with the geometry data by predicting or correcting attribute values ​​to correspond to the changed location information when a scale is applied to the geometry data and the location information is scaled or converted.

[0498] The attribute information encoding unit can encode color-restored attribute information, restored geometry information, and reference frames as inputs and generate an attribute bitstream.

[0499] The reference frame buffer stores restored attribute information and restored geometry information, and the reference frame generation unit generates a reference frame using the information stored in the reference frame buffer and provides it to the geometry information encoding unit and the attribute information encoding unit, thereby improving encoding efficiency based on temporal / spatial prediction.

[0500] FIG. 12 shows the encoding of attribute data according to embodiments.

[0501] FIG. 12 may represent a functional flowchart or block diagram of the attribute information encoding unit of FIG. 11. Each component may correspond to hardware, software, a processor, and / or a combination thereof.

[0502] The LOD generation unit can receive segmented point cloud data as input, sample points, and generate point groups by LOD. The generated point groups by LOD can be transmitted to the nearest point group determination unit.

[0503] The nearest point group determination unit may utilize points included in a point group of a higher LOD than the current LOD to determine the nearest point group for points within the current LOD's point group, using the received LOD-specific point groups. In this case, inclusion in the nearest point group can be determined based on the distance between the current point and a point included in the higher LOD point group. The determined nearest point group can be transmitted to the weight calculation unit.

[0504] The weight calculation unit can calculate weights used for lifting transformation or prediction transformation using points within the received nearest point group.

[0505] The lifting transform unit can generate transform coefficients by performing a frequency transform on attribute information through a prediction and update process for each LOD level using the generated LOD. The generated transform coefficients can be quantized and transmitted to the attribute information entropy encoder. Additionally, it can output the restored attribute information by internally performing an inverse transform.

[0506] Predictive transformation can be performed to predict the current point using neighbor points determined during the LOD generation process. The difference between the predicted attribute information and the original attribute information can be referred to as the transformation coefficient, which can be quantized and transmitted to the attribute information entropy encoding unit. Additionally, attribute information can be restored and output through inverse quantization and inverse predictive transformation.

[0507] Region Adaptive Hierarchical Transform (RAHT) can be performed when there are no LOD parameters. RAHT can receive segmented point cloud data as input, perform RAHT to transform it into the frequency domain, and generate transformation coefficients. Subsequently, the transformation coefficients can be quantized and transmitted to the attribute information entropy encoding unit.

[0508] Referring to FIG. 12, an attribute information encoding unit according to embodiments can receive segmented point cloud data and output an attribute information bitstream. The encoding process can be branched into an LOD-based lifting transformation / predictive transformation or a RHAT transformation depending on the presence or absence of LOD parameters (LOD params).

[0509] If LOD parameters exist, the LOD generation unit can generate point groups by LOD by sampling points using segmented point cloud data. The nearest point group determination unit can then use the LOD-specific point groups to establish the nearest point groups for points belonging to the current LOD's point group. In this case, inclusion in the nearest point group can be determined based on the distance to points included in the point group of a higher-level LOD than the current LOD. The weight calculation unit can calculate weights used for lifting transformation or predictive transformation based on the nearest point groups. The lifting transformation unit can generate transformation coefficients for attribute data through prediction and update processes for each LOD level. The generated transformation coefficients can be quantized and transmitted to the attribute information entropy encoding unit, and the encoded attribute information can be output as an attribute information bitstream. The predictive transformation unit can perform a predictive transformation to predict the current point using the nearest point groups. The difference between the predicted attribute information and the original attribute information can be generated as a transformation coefficient, quantized, and transmitted to the attribute information entropy encoding unit.

[0510] If LOD parameters do not exist, Region Adaptive Hierarchical Transform (RAHT) can be performed. RAHT can be performed using segmented point cloud data to transform it into the frequency domain and generate transform coefficients. The transform coefficients can be quantized and transmitted to the attribute information entropy encoding unit.

[0511] FIG. 13 shows a decoder according to embodiments.

[0512] The decoding method and apparatus according to the embodiments (receiving device (10004) of FIG. 1, receiving (20002) to rendering (20004) of FIG. 2, decoder of FIG. 7 and FIG. 9, each device of FIG. 10, decoder of FIG. 13 to FIG. 14, decoding of FIG. 15 to FIG. 17, bitstream and parameter acquisition of FIG. 18 to FIG. 25, method of FIG. 27) may be configured as in FIG. 13.

[0513] FIG. 13 is a block diagram of a PCC data decoder. Each component may correspond to hardware, software, a processor, and / or a combination thereof. The decoder can receive a geometry information bitstream and an attribute information bitstream as input, decode them, and output restored PCC data.

[0514] The geometry information decoding unit can receive a geometry information bitstream as input, decode it, and restore the geometry information. The coordinate system inverse transformation unit can restore the changed xyz axes or inversely transform the transformed coordinate system into an xyz orthogonal coordinate system.

[0515] The geometry information conversion inverse quantization processing unit can restore geometry position values ​​by restoring the signaled scale (scale = geometry quantization value) and applying it to the geometry position information (x, y, z values) of the restored point.

[0516] The attribute residual information entropy decoding can entropy decode the attribute bitstream. The attribute information decoding unit can receive the attribute information bitstream as input, decode it, and restore the attribute information. The color inverse conversion processing unit can restore the converted attributes to RGB colors. The reference frame generation unit can store the restored geometry and restored attribute information in the reference frame buffer and transmit the reference frame data from the reference frame to another module.

[0517] FIG. 13 illustrates a block diagram of a PCC data decoder according to embodiments, and shows a configuration in which geometry information bitstream and attribute information bitstream are each decoded to generate restored geometry information and restored attribute information, and restored PCC data is output using the same.

[0518] The decoder may be configured to receive a geometry information bitstream and an attribute information bitstream as inputs and decode them to output restored PCC data. To this end, it may include a geometry information decoding unit, a coordinate system inverse transformation unit, a geometry information transformation inverse quantization processing unit, an attribute residual information entropy decoding unit, an attribute information decoding unit, a color inverse transformation processing unit, and a reference frame generation unit.

[0519] The geometry information decoding unit can receive a geometry information bitstream and decode it to generate restored geometry information. The coordinate system inverse transformation unit can output inversely transformed geometry information by performing coordinate system inverse transformations on the decoded geometry information, such as restoring the changed xyz axes or inversely transforming the transformed spherical coordinate system into an xyz Cartesian coordinate system.

[0520] The attribute information decoding unit can generate restored attribute information by receiving an attribute information bitstream and performing decoding, and the color inverse conversion processing unit can output color-inverse converted attribute information by performing inverse conversion of the restored attribute information to RGB colors, etc.

[0521] The reference frame buffer can store restored geometry information and restored attribute information, and the reference frame generation unit can generate a reference frame based on the information stored in the reference frame buffer and provide it to the geometry information decoding unit and the attribute information decoding unit. Accordingly, the attribute information decoding unit can use the reference frame to restore attribute information, and the restored attribute information can be stored back in the reference frame buffer.

[0522] FIG. 14 illustrates the decoding of attribute data according to embodiments.

[0523] FIG. 14 illustrates a functional flowchart or block diagram of the attribute information decoding unit of FIG. 13. Each component may correspond to hardware, software, a processor, and / or a combination thereof.

[0524] The attribute information decoding unit may be configured to receive an attribute information bitstream and generate restored attribute information by performing entropy decoding, lifting inverse transform, predictive inverse transform, or inverse RAHT. The attribute information entropy decoding unit may receive an attribute information bitstream and decode it to restore transform coefficients.

[0525] The LOD (Level of Detail) generation unit can receive segmented point cloud data as input, sample points, and generate point groups by LOD. The generated point groups by LOD can be transmitted to the nearest point group determination unit. In this case, the LOD generation unit can be executed when LOD parameters are available.

[0526] The nearest point group determination unit may utilize points included in a point group of a higher LOD than the current LOD to determine the nearest point group for points within the current LOD's point group, using the received LOD-specific point groups. In this case, inclusion in the nearest point group can be determined based on the distance between the current point and a point included in the higher LOD point group. The determined nearest point group can be transmitted to the weight calculation unit. The weight calculation unit can calculate weights used for lifting transformation or prediction transformation using the points within the received nearest point group.

[0527] Lifting inverse transform can restore attribute information by inverse transforming the input transformation coefficients and performing frequency inverse transform through the generated LOD and the update and prediction processes for each LOD level. Predictive inverse transform can restore attributes by using neighbor points determined during the LOD generation process to sum the current point's predicted value with the value obtained by inverse transforming the input transformation coefficients. Inverse RAHT can restore attribute information by receiving the restored geometry information and transformation coefficients as input and performing the inverse process of RAHT.

[0528] FIG. 14 is a block diagram illustrating attribute information decoding according to embodiments. Referring to FIG. 14, the attribute information decoding unit can take an attribute information bitstream as input and output restored attribute information. The attribute information entropy decoding unit can decode the attribute information bitstream to restore the transformation coefficients. Subsequently, the decoding process can be branched into a lifting inverse transform / predictive inverse transform process or an inverse RAHT process based on the presence or absence of LOD parameters (LOD params).

[0529] If LOD parameters exist, the LOD generation unit can generate an LOD using restored geometry information and / or attribute reference frames, and generate point groups for each LOD. The nearest point group determination unit can determine the nearest point group for the points of the current LOD based on the point groups for each LOD. In this case, whether a point is included in the nearest point group can be determined based on the distance to a point included in the point group of a higher-level LOD than the current LOD. The weight calculation unit can calculate weights used for lifting transformation or predictive transformation based on the points within the nearest point group. Furthermore, the lifting inverse transformation can restore attribute information by inversely transforming the input transformation coefficients and performing frequency inverse transformation through the update and prediction processes for the generated LOD and LOD levels. Additionally, the predictive inverse transformation can obtain a predicted value of the current point using neighbor points determined during the LOD generation process, and restore attributes using the sum of the predicted value and the value obtained by inversely transforming the input transformation coefficients.

[0530] If LOD parameters do not exist, Inverse RAHT can take the restored geometry information and transformation coefficients as inputs, perform the inverse RAHT process, and output the restored attribute information.

[0531] FIG. 15 shows a flowchart of the nearest point group determination unit according to the embodiments.

[0532] FIG. 15 shows a flowchart according to embodiments of the nearest point group determination unit of FIG. 12 or FIG. 14.

[0533] The nearest point group determination unit can receive a point group as input and output the nearest point group. First, a first search for neighbor points of another LOD within the screen may be performed. Subsequently, a second search for neighbor points of another LOD within the screen may be omitted based on the number of neighbor points registered in the predictor. If the number of neighbor points registered in the predictor is less than 3, a second search for neighbor points of another LOD within the screen may be performed additionally.

[0534] Subsequently, the second search for neighboring points within the same LOD on the screen may be omitted based on pred_intra_min_lod, which represents the LOD index at which a second search for neighboring points within the same LOD on the screen can be performed. If the LOD index (lod_index) is greater than or equal to pred_intra_min_lod, the second search for neighboring points may be performed.

[0535] Subsequently, based on information regarding whether cross-screen prediction is performed, the first and second cross-screen neighbor point searches may be omitted. If the current slice is a slice that can use cross-screen prediction, the first cross-screen neighbor point search may be performed, and subsequently, the second cross-screen neighbor point search may be performed.

[0536] In the embodiments, other screens may be referred to as frames, other LODs may refer to the reference LOD of the current LOD and may be referred to as inter-LOD or inter-level, and the same LOD may refer to the current LOD and may be referred to as intra-LOD or intra-level. Additionally, inter-frame prediction may be referred to as inter-frame prediction.

[0537] Referring to FIG. 15, the nearest point group determination unit receives point groups by LOD and can output the nearest point group.

[0538] First, a first search for neighbor points between levels within a frame can be performed based on point groups.

[0539] And, based on the number of discovered points, it may be determined whether to perform a second search for neighbor points between levels within the frame. For example, if the number of discovered neighbor points is less than 3, a second search for neighbor points between levels within the frame may be performed. And, if the number of discovered neighbor points is greater than or equal to 3, a second search for neighbor points between levels within the frame may be omitted.

[0540] Additionally, whether to perform a second search for neighbor points within the frame's LOD can be determined based on information representing the LOD index and the intra-predicted minimum LOD value (pred_intra_min_lod). For example, if the current LOD index (lod_index) is greater than or equal to pred_intra_min_lod, a second search for neighbor points within the frame's LOD may be performed. Furthermore, if the current LOD index (lod_index) is smaller than pred_intra_min_lod, the second search for neighbor points within the frame's LOD may be omitted.

[0541] Then, it is determined whether the current slice can perform inter-frame prediction, and if inter-frame prediction is unsuitable, the first inter-frame neighbor point search and the second inter-frame neighbor point search may be omitted. If the current slice can perform inter-frame prediction, the first inter-frame neighbor point search and the second inter-frame neighbor point search may be performed.

[0542] FIG. 16 shows a flowchart of the nearest point group determination unit according to the embodiments.

[0543] FIG. 16 shows a flowchart according to embodiments of the nearest point group determination unit of FIG. 12 or FIG. 14.

[0544] The nearest point group determination unit can receive a point group as input and output the nearest point group.

[0545] First, a first search of other LOD neighbor points within the screen can be performed.

[0546] Subsequently, based on the number of neighbor points registered in the predictor, a second search for other LOD neighbor points within the screen may be omitted.

[0547] If the number of neighbor points registered in the predictor is less than (pred_set_size_minus1+1), a second search for other LOD neighbor points within the screen may be performed.

[0548] Subsequently, the second search for neighboring points with the same LOD within the screen may be omitted based on pred_intra_min_lod, which represents the LOD index at which a second search for neighboring points with the same LOD within the screen can be performed.

[0549] If the LOD index (lod_index) is greater than or equal to pred_intra_min_lod, a second search for neighboring points can be performed.

[0550] Subsequently, based on information regarding whether to perform a prediction between screens, the first search for neighbor points between screens and the second search for neighbor points between screens may be omitted.

[0551] If the current slice is a slice that can use cross-screen prediction, a first search for cross-screen neighbor points can be performed.

[0552] Subsequently, the second search for inter-screen neighbor points may be omitted based on the number of inter-screen neighbor points registered in the predictor.

[0553] If the number of inter-screen neighbor points registered in the predictor is less than (pred_set_size_minus1+1), a second search for inter-screen neighbor points may be performed additionally.

[0554] In the embodiments, other screens may be referred to as frames, other LODs may refer to the reference LOD of the current LOD and may be referred to as inter-LOD or inter-level, and the same LOD may refer to the current LOD and may be referred to as intra-LOD or intra-level. Additionally, inter-frame prediction may be referred to as inter-frame prediction.

[0555] Referring to FIG. 16, the nearest point group determining unit according to the embodiments can receive a point group as input and output a nearest point group.

[0556] First, a first search for neighbor points between levels within a frame can be performed based on point groups.

[0557] Additionally, whether to perform a second search for neighbor points between levels within a frame can be determined based on information (pred_set_size_minus1+1) indicating the predictor set size. For example, if the number of discovered neighbor points is less than (pred_set_size_minus1+1), a second search for neighbor points between levels within a frame may be performed. Furthermore, if the number of discovered neighbor points is greater than or equal to (pred_set_size_minus1+1), a second search for neighbor points between levels within a frame may be omitted.

[0558] Additionally, whether to perform a second search for neighbor points within the frame's LOD can be determined based on information representing the LOD index and the intra-predicted minimum LOD value (pred_intra_min_lod). For example, if the current LOD index (lod_index) is greater than or equal to pred_intra_min_lod, a second search for neighbor points within the frame's LOD may be performed. Furthermore, if the current LOD index (lod_index) is smaller than pred_intra_min_lod, the second search for neighbor points within the frame's LOD may be omitted.

[0559] Then, it is determined whether the current slice can perform inter-frame prediction, and if inter-frame prediction is unsuitable, the first inter-frame neighbor point search and the second inter-frame neighbor point search may be omitted. If the current slice can perform inter-frame prediction, the first inter-frame neighbor point search may be performed.

[0560] In addition, whether to perform a second search for inter-frame neighbor points can be determined based on information (pred_set_size_minus1+1) indicating the predictor set size. For example, if the number of discovered points is less than pred_set_size_minus1+1, a second search for inter-frame neighbor points may be performed, and if the number of discovered points is equal to or greater than pred_set_size_minus1+1, a second search for inter-frame neighbor points may be omitted.

[0561] FIG. 17 shows a flowchart of the nearest point group determination unit according to the embodiments.

[0562] FIG. 17 shows a flowchart according to embodiments of the nearest point group determination unit of FIG. 12 or FIG. 14.

[0563] The nearest point group determination unit can receive a point group as input and output the nearest point group.

[0564] First, a first search of other LOD neighbor points within the screen can be performed.

[0565] Subsequently, based on the number of neighbor points registered in the predictor, a second search for other LOD neighbor points within the screen may be omitted.

[0566] If the number of neighbor points registered in the predictor is less than (pred_set_size_minus1+1), a second search for other LOD neighbor points within the screen may be performed.

[0567] Subsequently, the second search for neighboring points with the same LOD within the screen may be omitted based on pred_intra_min_lod, which represents the LOD index at which a second search for neighboring points with the same LOD within the screen can be performed.

[0568] If the LOD index (lod_index) is greater than or equal to pred_intra_min_lod, a second search for neighboring points can be performed.

[0569] Subsequently, based on information regarding whether to perform a prediction between screens, the first search for neighbor points between screens and the second search for neighbor points between screens may be omitted.

[0570] If the current slice is a slice that can use cross-screen prediction, a first search for cross-screen neighbor points can be performed.

[0571] Subsequently, the second search for inter-screen neighbor points may be omitted based on the number of inter-screen neighbor points registered in the predictor and narrow_search_mode_flag.

[0572] If the number of inter-screen neighbor points registered in the predictor is less than (pred_set_size_minus1+1) and narrow_search_mode_flag is 0, a second inter-screen neighbor point search may be performed additionally.

[0573] In the embodiments, other screens may be referred to as frames, other LODs may refer to the reference LOD of the current LOD and may be referred to as inter-LOD or inter-level, and the same LOD may refer to the current LOD and may be referred to as intra-LOD or intra-level. Additionally, inter-frame prediction may be referred to as inter-frame prediction.

[0574] Referring to FIG. 17, the nearest point group determining unit according to the embodiments can receive a point group as input and output a nearest point group.

[0575] First, a first search for neighbor points between levels within a frame can be performed based on point groups.

[0576] Additionally, whether to perform a second search for neighbor points between levels within a frame can be determined based on information (pred_set_size_minus1+1) indicating the predictor set size. For example, if the number of discovered neighbor points is less than (pred_set_size_minus1+1), a second search for neighbor points between levels within a frame may be performed. Furthermore, if the number of discovered neighbor points is greater than or equal to (pred_set_size_minus1+1), a second search for neighbor points between levels within a frame may be omitted.

[0577] Additionally, whether to perform a second search for neighbor points within the frame's LOD can be determined based on information (pred_intra_min_lod) indicating the intra-predicted minimum LOD value. For example, if the current LOD index (lod_index) is greater than or equal to pred_intra_min_lod, a second search for neighbor points within the frame's LOD may be performed. Furthermore, if the current LOD index (lod_index) is smaller than pred_intra_min_lod, the second search for neighbor points within the frame's LOD may be omitted.

[0578] Then, it is determined whether the current slice can perform inter-frame prediction, and if inter-frame prediction is unsuitable, the first inter-frame neighbor point search and the second inter-frame neighbor point search may be omitted. If the current slice can perform inter-frame prediction, the first inter-frame neighbor point search may be performed.

[0579] And, whether to perform a second search for neighboring points between frames may be determined based on information indicating the predictor set size (pred_set_size_minus1+1) and / or narrow search mode information (narrow_search_mode_flag). For example, if the number of searched points is less than pred_set_size_minus1+1 and narrow_search_mode_flag is 0, a second search for neighboring points between frames may be performed, and if the number of searched points is equal to or greater than pred_set_size_minus1+1 and narrow_search_mode_flag is 1, a second search for neighboring points between frames may be omitted.

[0580] Alternatively, whether to perform a second search for inter-frame neighbor points may be determined based on the narrow search mode information (narrow_search_mode_flag). The narrow search mode information (narrow_search_mode_flag) may be a flag indicating whether to perform a second search for inter-frame neighbor points. For example, if the narrow search mode information indicates a first value, a second search for inter-frame neighbor points may be performed, and if the narrow search mode information indicates a second value, a second search for inter-frame neighbor points may be omitted. In this case, the narrow search mode information may be information indicating whether to perform a second search for inter-frame neighbor points, and may be referred to by various terms within the same meaning. For example, the narrow search mode information may be replaced by the wide search mode information, and whether to perform a second search for inter-frame neighbor points may be determined based on the first or second value of the wide search mode information.

[0581] FIG. 18 shows a bitstream including geometry data, attribute data, and parameter information according to embodiments.

[0582] The encoding method and apparatus according to the embodiments (transmitting device (10000) of FIG. 1, acquisition (20000) to transmission (20002) of FIG. 2, encoder of FIG. 3, encoder of FIG. 8, each device of FIG. 10, encoder of FIG. 11 to FIG. 12, encoding of FIG. 15 to FIG. 17, bitstream and parameter generation of FIG. 18 to FIG. 25, method of FIG. 26) can generate a bitstream including encoded geometry data, attribute data, and parameter information as in FIG. 18.

[0583] Decoding method and apparatus according to embodiments (Fig. 1 receiving device (10004), Fig. 2 receiving (20002) to rendering (20004), Fig. 7, Fig. 9 decoder, Fig. 10 each device, Fig. 13 to Fig. 14 decoder, Fig. 15 to Fig. 17 decoding, Fig. 18 to Fig. 25 bitstream and parameter acquisition, Fig. 27 method) can acquire parameter information and / or signaling information from a bitstream as in Fig. 18, decode geometry data within the bitstream based on the acquired information, and decode attribute data.

[0584] In order to carry out the embodiments, relevant information may be signaled. The signaling information according to the embodiments may be used at a transmitting end or a receiving end, etc.

[0585] The encoded point cloud configuration is as follows. A point cloud data encoder that performs geometry encoding and / or attribute encoding processes can generate an encoded point cloud (or a bitstream containing a point cloud) as follows. Additionally, signaling information regarding point cloud data can be generated and processed by a metadata processing unit of a point cloud data transmitting device and included in the point cloud as follows.

[0586] The abbreviations used in the embodiments have the following meanings, and each abbreviation may be referred to by other terms within the scope of equivalent meaning. SPS (Sequence Parameter Set) means sequence parameter set, GPS (Geometry Parameter Set) means geometry parameter set, APS (Attribute Parameter Set) means attribute parameter set, and TPS (Tile Parameter Set) means tile parameter set. Additionally, Geom (Geometry bitstream) is a geometry bitstream and may include a geometry slice header and geometry slice data, and may have a configuration such as “geometry slice header + [geometry PU header + geometry PU data] | geometry slice data”. Attr (Attribute bitstream) is an attribute bitstream and may include an attribute data unit header and attribute data unit data, and may have a configuration such as “attribute data unit header + [attribute PU header + attribute PU data] | attribute data unit data”.

[0587] Option information related to lifting weight calculation can be added to and signaled in SPS and APS. Option information related to lifting weight calculation can be added to and signaled in TPS or the Attribute header for each Slice.

[0588] Tile or slice functionality is provided to allow point clouds to be processed by dividing them into regions. When dividing by region, different options for generating neighbor point sets can be set for each region, offering a choice between low complexity with slightly lower reliability of results, or conversely, high complexity with high reliability. These settings can be configured differently depending on the receiver's processing capacity.

[0589] Therefore, if the point cloud is divided into tiles, different options can be applied to each tile. Alternatively, if the point cloud is divided into slices, different options can be applied to each slice.

[0590] Referring to FIG. 18, the bitstream may include a sequence parameter set (SPS), a geometry parameter set (GPS), one or more attribute parameter sets (APS0, APS1), and an alc tile parameter set (TPS), and may include a plurality of slices (Slice 0 to Slice n). Each slice may include a geometry bitstream (Geom) and one or more attribute bitstreams (Attr0, Attr1). The geometry bitstream may include a geometry slice header and geometry slice data, and the geometry slice data may include a geometry PU header and geometry PU data. Additionally, the attribute bitstream may include an attribute slice header and attribute slice data, and the attribute slice data may include an attribute PU header and attribute PU data.

[0591] FIG. 19 shows a sequence parameter set (SPS) within a bitstream according to embodiments.

[0592] Figure 19 shows the SPS syntax within the bitstream of Figure 18.

[0593] Option information related to lifting weight updates can be added to the Sequence Parameter Set (SPS) and signaled. The narrow search mode enable (sps_attr_narrow_search_mode_enabled) signaling information can be added to the SPS to efficiently signal for inter-predictive compression support. The names of the signaling information can be understood within the scope of the meaning and function of the signaling information, and may be referred to by other names within the scope of equivalent meaning.

[0594] The profile indicator (profile_idc) indicates the profile that the bitstream adheres to, as specified in Annex A. The bitstream must not contain a profile_idc value other than that specified in Annex A. Other values ​​for profile_idc are reserved by ISO / IEC for future use.

[0595] If profile_compatibility_flags is 1, it indicates that the bitstream complies with the profile where profile_idc is j.

[0596] The number of attribute sets (sps_num_attribute_sets) indicates the number of coded attributes included in the bitstream. The value of sps_num_attribute_sets is between 0 and 63.

[0597] The attribute dimension (attribute_dimension[ i ]) represents the number of components of the i-th attribute.

[0598] The attribute instance ID (attribute_instance_id[ i ]) represents the instance ID of the i-th attribute.

[0599] Narrow search mode enable (sps_attr_narrow_search_mode_enabled) indicates whether to apply narrow search mode to the sequence.

[0600] FIG. 20 shows an attribute parameter set (APS) in a bitstream according to embodiments.

[0601] Figure 20 shows the APS syntax within the bitstream of Figure 18.

[0602] During the attribute information encoding / decoding process, option information related to narrow search can be added to the Attribute Parameter Set (APS) and signaled. By adding the narrow search mode enable (aps_attr_narrow_search_mode_enabled) signaling information to the APS, it can be efficiently signaled for inter-prediction. The names of the signaling information can be understood within the scope of their meaning and function, and may be referred to by other names within the scope of equivalent meaning.

[0603] The attribute parameter set ID (aps_attr_parameter_set_id) provides an identifier for the APS so that it can be referenced by other syntax elements.

[0604] The sequence parameter set ID (aps_seq_parameter_set_id) indicates the active SPS by sps_seq_parameter_set_id.

[0605] The attribute coding type (attr_coding_type) indicates the attribute coding method. Valid values ​​are 0 to 3. 0=RAHT, 1=Predicting Transform, 2=Lifting Transform, 3=Raw attribute data. Other values ​​are reserved by ISO / IEC for future use. Decoders complying with this version of this document must ignore (remove from bitstream and discard) attribute data units coded with the reserved values ​​of attr_coding_type.

[0606] Narrow search mode enable (aps_attr_narrow_search_mode_enabled) indicates whether to apply narrow search mode to the frame.

[0607] FIG. 21 shows an APS in a bitstream according to embodiments.

[0608] Figure 21 shows the APS syntax within the bitstream of Figure 18.

[0609] During the encoding / decoding process of attribute information, option information related to narrow range search can be added to the Attribute Parameter Set (APS) and signaled.

[0610] The attribute parameter set ID (aps_attr_parameter_set_id) provides an identifier for the APS so that it can be referenced by other syntax elements.

[0611] The sequence parameter set ID (aps_seq_parameter_set_id) indicates the active SPS by sps_seq_parameter_set_id.

[0612] The attribute coding type (attr_coding_type) indicates the attribute coding method. Valid values ​​are 0 to 3. 0=RAHT, 1=Predicting Transform, 2=Lifting Transform, 3=Raw attribute data. Other values ​​are reserved by ISO / IEC for future use. Decoders complying with this version of this document must ignore (remove from bitstream and discard) attribute data units coded with the reserved values ​​of attr_coding_type.

[0613] Narrow search mode enable (aps_attr_narrow_search_mode_enabled) indicates whether to apply narrow search mode to the frame.

[0614] FIG. 22 shows a Tile Parameter Set (TPS) within a bitstream according to embodiments.

[0615] Figure 22 shows the TPS syntax within the bitstream of Figure 18.

[0616] Narrow search-related option information can be added to the Tile Parameter Set (TPS) and signaled. By adding the narrow search mode enable (aps_attr_narrow_search_mode_enabled) signaling information to the TPS, it can be efficiently signaled for reference frame buffer support. The names of the signaling information can be understood within the scope of the meaning and function of the signaling information, and may be referred to by other terms within the same meaning.

[0617] The number of tiles (num_tiles) represents the number of tiles signaled for the bitstream. If this value does not exist, num_tiles is assumed to be 0.

[0618] The tile bounding box offset x[i] (tile_bounding_box_offset_x[ i ]) represents the x-offset of the i-th tile in the Cartesian coordinate system. If this value does not exist, the value of tile_bounding_box_offset_x[0] is estimated to be sps_bounding_box_offset_x.

[0619] The tile bounding box offset y[i] (tile_bounding_box_offset_y[ i ]) represents the y-offset of the i-th tile in the Cartesian coordinate system. If this value does not exist, the value of tile_bounding_box_offset_y[0] is estimated to be sps_bounding_box_offset_y.

[0620] Narrow search mode enable[i](tile_attr_narrow_search_mode_enabled[i]) indicates whether to apply narrow search mode to the i-th tile.

[0621] FIG. 23 shows the TPS in the bitstream according to the embodiments.

[0622] Figure 23 shows the TPS syntax within the bitstream of Figure 18.

[0623] Narrow search mode-enabled option information can be added to the Tile Parameter Set (TPS) and signaled.

[0624] The number of tiles (num_tiles) represents the number of tiles signaled for the bitstream. If this value does not exist, num_tiles is assumed to be 0.

[0625] The tile bounding box offset x[i] (tile_bounding_box_offset_x[ i ]) represents the x-offset of the i-th tile in the Cartesian coordinate system. If this value does not exist, the value of tile_bounding_box_offset_x[0] is estimated to be sps_bounding_box_offset_x.

[0626] The tile bounding box offset y[i] (tile_bounding_box_offset_y[ i ]) represents the y-offset of the i-th tile in the Cartesian coordinate system. If this value does not exist, the value of tile_bounding_box_offset_y[0] is estimated to be sps_bounding_box_offset_y.

[0627] Narrow search mode enable[i](tile_attr_narrow_search_mode_enabled[i]) indicates whether to apply narrow search mode to the i-th tile.

[0628] FIG. 24 shows an attribute data header within a bitstream according to embodiments.

[0629] FIG. 24 shows the header syntax of an attribute data unit (or slice) within the bitstream of FIG. 18.

[0630] Option information related to narrow search can be added to the Attribute Slice Header and signaled. By adding the narrow search mode enable (abh_attr_narrow_search_mode_enabled) signaling information to the Attribute Slice Header syntax, it can be efficiently signaled to support attribute compression. The names of the signaling information can be understood within the scope of their meaning and function, and may be referred to by other terms within the same meaning.

[0631] The attribute parameter set ID (abh_attr_parameter_set_id) represents the aps_attr_parameter_set_id value of the active APS.

[0632] The SPS attribute index (abh_attr_sps_attr_idx) represents the set of attributes set on the active SPS. The value of abh_attr_sps_attr_idx must be between 0 and the number of attribute sets of the active SPS (sps_num_attribute_sets).

[0633] The geometry slice ID (abh_attr_geom_slice_id) represents the slice ID (gsh_slice_id) value of the active geometry slice header.

[0634] Narrow search mode enable (abh_attr_narrow_search_mode_enabled) indicates whether to apply narrow search mode to the slice.

[0635] FIG. 25 shows an attribute data header within a bitstream according to embodiments.

[0636] FIG. 25 shows the header syntax of an attribute data unit (or slice) within the bitstream of FIG. 18.

[0637] Option information related to narrow search can be added to the Attribute Slice Header and signaled. The signaling information for Narrow Search Mode Enable (abh_attr_narrow_search_mode_enabled) can be efficiently signaled to support attribute compression by being added to the Attribute Slice Header syntax. The names of the signaling information can be understood within the scope of their meaning and function, and may be referred to by other terms within the same meaning.

[0638] The attribute parameter set ID (abh_attr_parameter_set_id) represents the aps_attr_parameter_set_id value of the active APS.

[0639] The SPS attribute index (abh_attr_sps_attr_idx) represents the set of attributes set on the active SPS. The value of abh_attr_sps_attr_idx must be between 0 and the number of attribute sets of the active SPS (sps_num_attribute_sets).

[0640] The geometry slice ID (abh_attr_geom_slice_id) represents the slice ID (gsh_slice_id) value of the active geometry slice header.

[0641] Narrow search mode enable (abh_attr_narrow_search_mode_enabled) indicates whether to apply narrow search mode to the slice.

[0642] FIG. 26 illustrates a encoding method according to embodiments.

[0643] The encoding method of Fig. 26 can follow the inverse process of the decoding method of Fig. 27.

[0644] The method according to the embodiments may include the step of encoding geometry data of point cloud data (S2610) / or the step of encoding attribute data of point cloud data (S2620).

[0645] The method according to the embodiments may include the transmitting device (10000) of FIG. 1, the acquisition (20000) to transmission (20002) of FIG. 2, the encoder of FIG. 3 and FIG. 8, the respective device of FIG. 10, the encoders of FIG. 11 to FIG. 12, the encoding of FIG. 15 to FIG. 17, the generation of bitstreams and parameters of FIG. 18 to FIG. 25, and the operation described in the method of FIG. 26.

[0646] The step of encoding attribute data may include a step of generating a Level of Detail (LoD) by sampling points of point cloud data, a step of searching for the nearest neighbor points of a point based on the LOD, and a step of predicting the attribute value of a point based on the nearest neighbor points.

[0647] Referring to the first search method for neighboring LOD points within the screens of FIGS. 15-17, the step of searching for nearest neighboring points may include dividing a frame containing point cloud data into spatial regions and initial searching for a predictor for said point within the reference LOD. The regions may be derived based on parameters (dist2) regarding the distribution of said point cloud data, an offset, and the LOD for said point.

[0648] Referring to the second search method for neighboring LOD points in the screens of FIGS. 15-17, the step of searching for nearest neighboring points may further include the step of searching for predictors for points based on the order of points within the reference LOD within the frame containing point cloud data, based on the number of initially searched predictors.

[0649] Referring to the second search method for neighboring points with the same LOD within the screens of FIGS. 15-17, the step of searching for nearest neighboring points may further include the step of searching for a predictor for a point based on the order of points within the LOD for the point within a frame containing point cloud data, based on the LOD for the point and intra-min LOD information (pred_intra_min_lod).

[0650] Referring to the first search method for neighbor points between the frames of FIGS. 15-17, the step of searching for nearest neighbor points may further include the step of initially searching for a predictor for points by dividing a reference frame for a frame containing point cloud data into spatial regions. The regions may be derived based on parameters (dist2) and offsets regarding the distribution of the point cloud data.

[0651] Referring to the second search method for neighbor points between frames of FIGS. 15-17, the step of searching for nearest neighbor points further includes the step of searching for a predictor for a point based on the order of points in a reference frame based on a flag in the bitstream, and the flag may include a value related to whether the step of searching for a predictor for a point based on the order of points in the reference frame is performed.

[0652] FIG. 27 illustrates a decoding method according to embodiments.

[0653] The decoding method of Fig. 27 can follow the inverse process of the encoding method of Fig. 26.

[0654] The method according to the embodiments may include the step of decoding geometry data of point cloud data within a bitstream (S2710); and / or the step of decoding attribute data of point cloud data (S2720); etc.

[0655] The method according to the embodiments may include the receiving device (10004) of FIG. 1, the receiving (20002) to rendering (20004) of FIG. 2, the decoders of FIG. 7 and 9, the respective devices of FIG. 10, the decoders of FIG. 13 to 14, the decoding of FIG. 15 to 17, the acquisition of bitstreams and parameters of FIG. 18 to 25, and the operation described in the method of FIG. 27.

[0656] The step of decoding attribute data may include a step of generating a Level of Detail (LoD) by sampling points of point cloud data, a step of searching for the nearest neighbor points of a point based on the LOD, and a step of predicting the attribute value of a point based on the nearest neighbor points.

[0657] Referring to the first search method for neighboring LOD points within the screens of FIGS. 15-17, the step of searching for nearest neighboring points may include dividing a frame containing point cloud data into spatial regions and initial searching for a predictor for said point within the reference LOD. The regions may be derived based on parameters (dist2) regarding the distribution of said point cloud data, an offset, and the LOD for said point. The reference LOD may be a different LOD from the current LOD.

[0658] Referring to the second search method for neighboring LOD points in the screens of FIGS. 15-17, the step of searching for nearest neighboring points may further include the step of searching for predictors for points based on the order of points within the reference LOD within the frame containing point cloud data, based on the number of initially searched predictors.

[0659] Referring to the second search method for neighboring points with the same LOD within the screens of FIGS. 15-17, the step of searching for nearest neighboring points may further include the step of searching for a predictor for a point based on the order of points within the LOD for the point within a frame containing point cloud data, based on the LOD for the point and intra-min LOD information (pred_intra_min_lod).

[0660] Referring to the first search method for neighbor points between the frames of FIGS. 15-17, the step of searching for nearest neighbor points may further include the step of initially searching for a predictor for points by dividing a reference frame for a frame containing point cloud data into spatial regions. The regions may be derived based on parameters (dist2) and offsets regarding the distribution of the point cloud data.

[0661] Referring to the second search method for neighbor points between frames of FIGS. 15-17, the step of searching for nearest neighbor points further includes the step of searching for a predictor for a point based on the order of points in a reference frame based on a flag in the bitstream, and the flag may include a value related to whether the step of searching for a predictor for a point based on the order of points in the reference frame is performed.

[0662] FIG. 26 The encoding method may be performed by an encoding device comprising a memory and at least one processor connected to the memory, wherein the at least one processor is configured to encode geometry data of point cloud data and attribute data of point cloud data.

[0663] The PCC encoding method, PCC decoding method, and signaling method of the above-described embodiments can provide the following effects.

[0664] By performing nearest neighbor search used in lifting transformations while considering the distribution characteristics of point clouds when encoding and decoding attribute values, faster encoders and decoders can be provided. Additionally, improved prediction performance can be provided through accurate inter-frame prediction, and this improved prediction performance can lead to a reduction in bitstream size.

[0665] Accordingly, the transmitting method / device according to the embodiments can efficiently compress point cloud data and transmit the data, and by transmitting signaling information for this purpose, the receiving method / device according to the embodiments can also efficiently decode / restore the point cloud data.

[0666] The operation of the transmitting and receiving device according to the above-described embodiments can be explained in combination with the following point cloud compression processing process.

[0667] The embodiments have been described in terms of methods and / or devices, and the description of the methods and the description of the devices may be applied complementarily.

[0668] Although the drawings have been described separately for the convenience of explanation, it is also possible to design a new embodiment by combining the embodiments described in each drawing. Furthermore, designing a computer-readable recording medium containing a program for executing the previously described embodiments, as required by a person skilled in the art, falls within the scope of the embodiments. The apparatus and method according to the embodiments are not limited to the configuration and method of the embodiments described above; rather, the embodiments may be configured by selectively combining all or part of each embodiment to allow for various modifications. Although preferred embodiments have been illustrated and described, the embodiments are not limited to the specific embodiments described above. It is not only possible for a person skilled in the art to make various modifications without departing from the essence of the embodiments claimed in the claims, but such modifications should not be understood individually from the technical concept or perspective of the embodiments.

[0669] Various components of the device of the embodiments may be implemented by hardware, software, firmware, or a combination thereof. Various components of the embodiments may be implemented as a single chip, for example, a single hardware circuit. Depending on the embodiments, the components according to the embodiments may each be implemented as separate chips. Depending on the embodiments, at least one of the components of the device according to the embodiments may be composed of one or more processors capable of executing one or more programs, and one or more programs may include instructions for performing or executing any one or more of the operations / methods according to the embodiments. Executable instructions for performing the methods / operations of the device according to the embodiments may be stored in non-transient CRMs or other computer program products configured to be executed by one or more processors, or may be stored in transient CRMs or other computer program products configured to be executed by one or more processors. Additionally, memory according to the embodiments may be used as a concept that includes not only volatile memory (e.g., RAM, etc.) but also non-volatile memory, flash memory, PROM, etc. In addition, it may also include implementation in the form of carrier waves, such as transmission over the Internet. Furthermore, processor-readable recording media are distributed across networked computer systems, allowing processor-readable code to be stored and executed in a distributed manner.

[0670] In this document, “ / ” and “,” are interpreted as “and / or.” For example, “A / B” is interpreted as “A and / or B,” and “A, B” is interpreted as “A and / or B.” Additionally, “A / B / C” means “at least one of A, B and / or C.” Also, “A, B, C” means “at least one of A, B and / or C.” Additionally, in this document, “or” is interpreted as “and / or.” For example, “A or B” may mean 1) “A” alone, 2) “B” alone, or 3) “A and B.” In other words, “or” in this document may mean “additionally or alternatively.”

[0671] Terms such as "first," "second," etc., may be used to describe various components of the embodiments. However, the interpretation of the various components according to the embodiments should not be limited by these terms. These terms are merely used to distinguish one component from another. For example, the first user input signal may be referred to as the second user input signal. Similarly, the second user input signal may be referred to as the first user input signal. The use of these terms should be interpreted as not departing from the scope of the various embodiments. Although the first user input signal and the second user input signal are both user input signals, they do not imply the same user input signals unless clearly indicated in the context.

[0672] The terms used to describe the embodiments are intended for the purpose of describing specific embodiments and are not intended to limit the embodiments. As used in the description of the embodiments and in the claims, the singular is intended to include the plural unless explicitly indicated in the context. Expressions of and / or are used to mean including all possible combinations between the terms. Expressions of include describe the presence of features, numbers, steps, elements, and / or components and do not imply the exclusion of additional features, numbers, steps, elements, and / or components. Conditional expressions such as "if" or "when" used to describe the embodiments are not limited to being optional. It is intended to be interpreted as "when a specific condition is satisfied," "when a related action is performed in response to a specific condition," or "when a related definition is interpreted."

[0673] Additionally, operations according to the embodiments described herein may be performed by a transmitting and receiving device including memory and / or a processor, depending on the embodiments. The memory may store programs for processing / controlling operations according to the embodiments, and the processor may control various operations described in this document. The processor may be referred to as a controller, etc. Operations in the embodiments may be performed by firmware, software, and / or a combination thereof, and the firmware, software, and / or a combination thereof may be stored in the processor or in memory.

[0674] Meanwhile, the operation according to the embodiments described above may be performed by a transmitting device and / or a receiving device according to the embodiments. The transmitting and receiving device may include a transmitting and receiving unit for transmitting and receiving media data, a memory for storing instructions (program code, algorithm, flowchart and / or data) for a process according to the embodiments, and a processor for controlling the operations of the transmitting and receiving devices.

[0675] The processor may be referred to as a controller, etc., and may correspond, for example, to hardware, software, and / or a combination thereof. The operation according to the embodiments described above may be performed by the processor. Additionally, the processor may be implemented as an encoder / decoder, etc., for the operation of the embodiments described above.

[0676]

[0677] As described above, the relevant details have been explained in the best mode for carrying out the embodiments.

[0678]

[0679] As described above, the embodiments may be applied wholly or partially to point cloud data transmission and reception devices and systems.

[0680] Those skilled in the art may make various changes or modifications to the embodiments within the scope of the embodiments.

[0681] The embodiments may include modifications / variations, and such modifications / variations do not exceed the scope of the claims and their equivalents.

Claims

A step of decoding geometry data of point cloud data within a bitstream; and A step of decoding attribute data of the above point cloud data; comprising Decryption method. In paragraph 1, The step of decoding the above attribute data is, A step of generating Level of Detail (LoD) by sampling points of the above point cloud data; A step of searching for the nearest neighbor points of a point based on the LOD above; and A method comprising the step of predicting the attribute value of the point based on the nearest neighbor points. Decryption method. In paragraph 2, The step of searching for the above nearest neighbor points is, A method comprising the step of dividing a frame containing the above point cloud data into spatial regions and initial searching for a predictor for the point within an LOD different from the LOD for the point. Decryption method. In paragraph 3, The above region is derived based on parameters (dist2), offset, and LOD for the point regarding the distribution of the point cloud data, Decryption method. In paragraph 4, The step of searching for the above nearest neighbor points is, The method further comprises the step of searching for a predictor for the point based on the number of initially searched predictors, within a frame containing the point cloud data, based on the order of points in the LOD for the point and other LODs. Decryption method. In paragraph 5, The step of searching for the above nearest neighbor points is, The method further comprises the step of searching for a predictor for the point based on the order of points within the LOD for the point within a frame containing the point cloud data, based on LOD and intra-minimum LOD information (pred_intra_min_lod) for the point. Decryption method. In paragraph 1, The step of decoding the above attribute data is, A step of generating Level of Detail (LoD) by sampling points of the above point cloud data; A step of searching for the nearest neighbor points of a point based on the LOD above; and The method includes the step of predicting the attribute value of the point based on the nearest neighbor points. The step of searching for the above nearest neighbor points is, The method includes the step of dividing a reference frame for a frame containing the above point cloud data into spatial regions and initial searching for a predictor for the point. The above region is derived based on parameters (dist2) and offsets regarding the distribution of the point cloud data, Decryption method. In Paragraph 7, The step of searching for the above nearest neighbor points is, The method further includes the step of searching for a predictor for the point based on the order of points within the reference frame based on a flag within the bitstream, and The above flag includes a value related to whether the step of searching for a predictor for the point is performed based on the order of points within the above reference frame, Decryption method. Memory; and At least one processor connected to the memory; comprising, wherein the at least one processor: Decoding geometry data of point cloud data within a bitstream; and Decoding attribute data of the above point cloud data; configured to do so, Decoding device. A step of encoding the geometry data of the point cloud data; and A step of encoding attribute data of the above point cloud data; comprising Encoding method. In Paragraph 10, The step of encoding the above attribute data is, A step of generating Level of Detail (LoD) by sampling points of the above point cloud data; A step of searching for the nearest neighbor points of a point based on the LOD above; and A method comprising the step of predicting the attribute value of the point based on the nearest neighbor points. Encoding method. In Paragraph 11, The step of searching for the above nearest neighbor points is, A method comprising the step of dividing a frame containing the above point cloud data into spatial regions and initial searching for a predictor for the point within an LOD different from the LOD for the point. Encoding method. Memory; and At least one processor connected to the memory; comprising, wherein the at least one processor: Encoding the geometry data of the point cloud data; and Configured to encode the attribute data of the above point cloud data; Encoding device. A computer-readable storage medium for storing a bitstream generated by the method according to paragraph 10. Step of acquiring a bitstream for point cloud data, The bitstream is generated based on the step of encoding geometry data of the point cloud data; and the step of encoding attribute data of the point cloud data; and A method comprising the step of transmitting data including the bitstream above.

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