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

Abstract

A 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. A method according to embodiments may comprise the steps of: decoding geometry data of point cloud data within a bitstream; and decoding 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] To achieve the above-described purpose and other advantages, the 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 the point cloud data.

[0006] A 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 the point cloud data.

[0007] The device and method according to the embodiments can process point cloud data with high efficiency.

[0008] The device and method according to the embodiments can provide a high-quality point cloud service.

[0009] 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.

[0010] 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.

[0011] FIG. 1 shows an example of a point cloud content provision system according to embodiments.

[0012] FIG. 2 is a block diagram illustrating a point cloud content provision operation according to embodiments.

[0013] FIG. 3 shows an example of a point cloud encoder according to embodiments.

[0014] FIG. 4 shows examples of octree and occupancy codes according to embodiments.

[0015] Figure 5 shows an example of a point configuration by LOD according to embodiments.

[0016] Figure 6 shows an example of a point configuration by LOD according to embodiments.

[0017] FIG. 7 shows an example of a point cloud decoder according to embodiments.

[0018] FIG. 8 is an example of a transmission device according to embodiments.

[0019] FIG. 9 is an example of a receiving device according to embodiments.

[0020] FIG. 10 shows an example of a structure that can be linked with a point cloud data transmission / reception method / device according to embodiments.

[0021] FIG. 11 shows an initial search according to the embodiments.

[0022] FIG. 12 shows a Morton code in a two-dimensional plane according to embodiments.

[0023] FIG. 13 shows an extended search according to embodiments.

[0024] FIG. 14 shows a block diagram of a PCC data encoder according to embodiments.

[0025] FIG. 15 shows a block diagram of an attribute information encoding unit according to embodiments.

[0026] FIG. 16 shows a block diagram of a PCC data decoder according to embodiments.

[0027] FIG. 17 shows a block diagram of an attribute information decoding unit according to embodiments.

[0028] FIG. 18 shows a block diagram of an LoD generation unit according to embodiments.

[0029] FIG. 19 shows a block diagram of a nearest neighbor point search unit according to embodiments.

[0030] FIG. 20 shows a flowchart of intra-neighbor point search and inter-neighbor point search according to embodiments.

[0031] FIG. 21 shows a bitstream structure according to embodiments.

[0032] FIG. 22 shows the attribute parameter set syntax according to the embodiments.

[0033] FIG. 23 shows the attribute data unit header syntax according to the embodiments.

[0034] FIG. 24 illustrates an encoding method according to embodiments.

[0035] FIG. 25 illustrates a decoding method according to embodiments.

[0036] 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.

[0037] 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.

[0038] FIG. 1 shows an example of a point cloud content provision system according to embodiments.

[0039] 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.

[0040] 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 Artificial Intelligence (AI) 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 Internet of Things (IoT) device, an AI device / server, etc.

[0041] 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.

[0042] 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.

[0043] 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.

[0044] 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.

[0045] 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)).

[0046] 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).

[0047] 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.

[0048] 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.

[0049] 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 transmission side (e.g., transmission 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 transmission device (10000), or it may not be provided.

[0050] 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.

[0051] 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.

[0052] 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.

[0053] The elements of the point cloud content delivery system illustrated in FIG. 1 can be implemented in hardware, software, processors, and / or combinations thereof.

[0054] FIG. 2 is a block diagram illustrating a point cloud content provision operation according to embodiments.

[0055] 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).

[0056] 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.).

[0057] 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.

[0058] 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.

[0059] 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.

[0060] 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.

[0061] 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.).

[0062] 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.

[0063] FIG. 3 shows an example of a point cloud encoder according to embodiments.

[0064] 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.

[0065] 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.

[0066] 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).

[0067] 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.

[0068] 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.

[0069] 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.

[0070] 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.

[0071] 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.

[0072] 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.

[0073] 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 Hierarchical 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.

[0074] 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.

[0075] 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).

[0076] 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.

[0077] 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).

[0078] 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.

[0079] As shown in the drawing, the converted attributes are input to the RAHT conversion unit (30008) and / or LOD generation unit (30009).

[0080] 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.

[0081] 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.

[0082] 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.

[0083] The coefficient quantization unit (30011) according to the embodiments quantizes attribute-coded attributes based on coefficients.

[0084] An arismetic encoder (30012) according to the embodiments encodes quantized attributes based on arismetic coding.

[0085] 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).

[0086] FIG. 4 shows examples of octree and occupancy codes according to embodiments.

[0087] 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.

[0088] 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.

[0089]

[0090] 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.

[0091] 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.

[0092] 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.

[0093] 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).

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] 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.

[0099]

[0100] 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 value obtained from projecting onto the (y, z) plane is (ai, bi), the θ value is calculated using atan2(bi, ai), and the vertices are aligned based on the 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 depending on the combination of vertices. The first triangle can be formed from the 1st, 2nd, and 3rd vertices among the aligned vertices, and the second triangle can be formed from the 3rd, 4th, and 1st vertices among the aligned vertices.

[0101] Table 2-1. Triangles formed from vertices ordered 1,… ,n

[0102] n triangles

[0103] 3 (1,2,3)

[0104] 4 (1,2,3), (3,4,1)

[0105] 5 (1,2,3), (3,4,5), (5,1,3)

[0106] 6 (1,2,3), (3,4,5), (5,6,1), (1,3,5)

[0107] 7 (1,2,3), (3,4,5), (5,6,7), (7,1,3), (3,5,7)

[0108] 8 (1,2,3), (3,4,5), (5,6,7), (7,8,1), (1,3,5), (5,7,1)

[0109] 9 (1,2,3), (3,4,5), (5,6,7), (7,8,9), (9,1,3), (3,5,7), (7,9,3)

[0110] 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)

[0111] 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)

[0112] 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)

[0113] 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).

[0114] Figure 5 shows an example of a point configuration by LOD according to embodiments.

[0115] 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 processes. Since attributes depend on geometry, attribute encoding is performed based on the reconstructed geometry.

[0116] 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.

[0117] Figure 6 shows an example of a point configuration by LOD according to embodiments.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] graph. Attribute prediction residuals quantization pseudo code

[0124] int PCCQuantization(int value, int quantStep) {

[0125] if( value >=0) {

[0126] return floor(value / quantStep + 1.0 / 3.0);

[0127] } else {

[0128] return -floor(-value / quantStep + 1.0 / 3.0);

[0129] }

[0130] }

[0131] graph. Attribute prediction residuals inverse quantization pseudo code

[0132] int PCCInverseQuantization(int value, int quantStep) {

[0133] if( quantStep ==0) {

[0134] return value;

[0135] } else {

[0136] return value * quantStep;

[0137] }

[0138] }

[0139] 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.

[0140] 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 the 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.

[0141] 1) Create an array QW (QuantizationWight) 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.

[0142] 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.

[0143] 3) Create temporary arrays named updateweight and update, and initialize the temporary arrays to 0.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] The following equation represents the RAHT transformation matrix. g l x, y, z represents the average attribute value of the voxels at level l. g l x, y, z can be calculated from gl+1 2x, y, z and gl+1 2x+1, y, z. The weights of gl 2x, y, z and gl 2x+1, y, z are w1=wl 2x, y, z and w2=wl 2x+1, y, z.

[0149]

[0150] gl-1 x, y, z are low-pass values ​​used in the merging process at the next higher level. hl-1 x, y, z 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 calculated as wl-1 x, y, z = wl 2x, y, z + wl 2x + 1, y, z. The root node is the last g 1 0, 0, 0 and g 1 0, 0, 1 It is generated as follows through.

[0151]

[0152] The gDC value is also quantized and entropy-coded, just like the high-pass coefficient.

[0153] FIG. 7 shows an example of a point cloud decoder according to embodiments.

[0154] 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.

[0155] As described in Fig. 1, the point cloud decoder can perform geometry decoding and attribute decoding. Geometry decoding is performed before attribute decoding.

[0156] 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).

[0157] An arismetic decoder (7000), an octree composite unit (7001), a surface offset composite 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.

[0158] 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).

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] The arismetic decoder (7005) according to the embodiments decodes the attribute bitstream into arismetic coding.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] FIG. 8 is an example of a transmission device according to embodiments.

[0170] 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).

[0171] 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).

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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).

[0178] 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).

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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).

[0184] 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 ).

[0185] A slice refers to a series of syntax elements representing all or part of a coded point cloud frame.

[0186] According to the embodiments, the TPS may include information regarding each tile (e.g., information on the coordinate values ​​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.

[0187] FIG. 9 is an example of a receiving device according to embodiments.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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).

[0192] 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.

[0193] 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).

[0194] 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).

[0195] The inverse quantization processing unit (9005) according to the embodiments can inverse quantize the decoded geometry.

[0196] 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.

[0197] 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.

[0198] 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).

[0199] 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).

[0200] 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.

[0201] FIG. 10 shows an example of a structure that can be linked with a point cloud data transmission / reception method / device according to embodiments.

[0202] 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.

[0203] 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.

[0204] 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).

[0205] 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.

[0206] 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.

[0207] <PCC+XR>

[0208] 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.

[0209] 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.

[0210] <PCC+XR+모바일폰>

[0211] The XR / PCC device (1030) can be implemented as a mobile phone (1040) or the like by applying PCC technology.

[0212] The mobile phone (1040) can decode and display point cloud content based on PCC technology.

[0213] <PCC+자율주행+XR>

[0214] The autonomous vehicle (1020) can be implemented as a mobile robot, vehicle, unmanned aerial vehicle, etc. by applying PCC technology and XR technology.

[0215] 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.

[0216] 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.

[0217] 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.

[0218] 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.

[0219] 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.

[0220] 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.

[0221] The PCC method / device according to the embodiments can be applied to a vehicle providing autonomous driving services.

[0222] Vehicles providing autonomous driving services are connected to PCC devices to enable wired / wireless communication.

[0223] 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.

[0224] A point cloud transmission method / device (or encoding method and device) according to embodiments may include, and can be performed, a transmission device (10000) of FIG. 1, a point cloud video encoder (10002), a transmitter (10003), an acquisition-encoding-transmission (20000-20001-20002) of FIG. 2, an initial search of FIG. 11, a Morton code of FIG. 12, and an encoding based on an extended search of FIG. 13, a PCC data encoder of FIG. 14, an attribute information encoding unit of FIG. 15, a LoD generation unit of FIG. 18, a nearest neighbor point search unit of FIG. 19, an intra-neighbor point search and inter-neighbor point search flowchart of FIG. 20, bitstream and parameter information (syntax element) generation of FIG. 21 to 23, an encoding method of FIG. 24, etc.

[0225] A point cloud receiving method / device (or decoding method and device) according to embodiments may include and perform the receiving device (10004) of FIG. 1, a receiver (10005), a point cloud video decoder (10006), the transmission-decoding-rendering (20002-20003-20004) of FIG. 2, the decoder of FIG. 7, the receiving device of FIG. 9, the device of FIG. 10, the decoding based on the initial search of FIG. 11, the Morton code of FIG. 12, and the extended search of FIG. 13, the PCC data decoder of FIG. 16, the attribute information decoding unit of FIG. 17, the LoD generation unit of FIG. 18, the nearest neighbor point search unit of FIG. 19, the intra-neighbor point search and inter-neighbor point search flowchart of FIG. 20, the acquisition of bitstream and parameter information (syntax element) of FIG. 21 to 23, the decoding method of FIG. 25, etc.

[0226] The point cloud data transmission / reception method / device (or encoding / decoding method / device) according to the embodiments may be referred to simply as the method / device according to the embodiments.

[0227] According to the embodiments, geometry data, geometry information, location information, position information, etc., constituting the point cloud data are interpreted as having the same meaning. Attribute data, attribute information, attribute information, etc., constituting the point cloud data are interpreted as having the same meaning. In addition, occupancy trees, octrees, etc., used when decoding / encoding geometry data are interpreted as having the same meaning.

[0228] The embodiments relate to a method for improving attribute encoding / decoding speed in geometry-based point cloud compression (G-PCC) for 3D point cloud data compression, and may include or perform a condition-based nearest neighbor search method to reduce the time required for the encoding / decoding process of attribute information when generating a level of detail (hereinafter referred to as LoD) during the G-PCC attribute encoding and decoding process.

[0229] The contents of this specification may be understood based on G-PCC standard specification documents, including ISO / IEC 23090-38, which were disclosed at the time of the priority date or filing date of this application.

[0230] The embodiments may include or perform a method for increasing the compression speed of geometry-based point cloud compression (G-PCC) for 3D point cloud data compression.

[0231] The encoding and encoder (or encoding unit) according to the embodiments may be interpreted as having the same meaning as encoding and encoder, respectively. The decoding and decoder (or decoder) according to the embodiments may be interpreted as having the same meaning as decoding and decoder, respectively.

[0232] A point cloud can be composed of a set of points, and each point may have geometry information and attribute information. Geometry information is 3D position (XYZ) information, and attribute information may include color (RGB, YUV, etc.) or / and reflectance values.

[0233] The G-PCC encoding process may include the step of dividing a point cloud into tiles according to region and dividing each tile into slices for parallel processing. Specifically, a point cloud frame is a set of points for a single point in time within point cloud data (or a point cloud sequence) and can be divided into one or more slices. Additionally, a set of slices having the same slice tag can be defined as a tile, and a tile can represent a specific region of a point cloud frame.

[0234] 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.

[0235] The G-PCC decoding process may consist of receiving a geometry bitstream and an attribute bitstream in the form of encoded slice units to decode the geometry, and decoding attribute information based on the geometry reconstructed through the decoding process.

[0236] Specifically, according to the embodiments, attribute information can be encoded / decoded by performing processes such as generating LoDs and searching for nearest neighbor points on a slice-by-slice basis within a point cloud frame. The following description of searching for neighbor points and encoding / decoding geometry information and attribute information on a slice-by-slice basis represents embodiments in which the point cloud frame is composed of a single slice, but the embodiments according to the present invention are not limited thereto, and when the point cloud frame is composed of multiple slices, the above process can be performed on a slice-by-slice basis.

[0237] Octree-based, predictive tree-based, or trisoup-based compression techniques can be used to compress geometry information.

[0238] For attribute information compression, predicting transform-based, lifting transform-based, or RAHT transform-based compression techniques can be used.

[0239] According to the embodiments, a method for improving encoding / decoding speed can be included or performed by generating the Level of Detail (LoD) of a lifting transformation used for compressing attribute information of point cloud content captured by a LiDAR RGB-D camera or LiDAR equipment through a suitable nearest neighbor search order.

[0240] According to the embodiments, the nearest neighbor points or closest neighbor points of a specific point may be referred to as points or predictors within a predictor set of the specific point, and through the nearest neighbor search process according to the embodiments, nearest neighbor point candidates or predictor candidates are searched, and after the search process is completed, the nearest neighbor points or predictor set of the specific point may be obtained from the nearest neighbor point candidates or predictor candidates.

[0241] According to the embodiments, there can be three main methods for generating Levels of Detail (LoDs). First, there is a distance-based method that classifies LoDs based on the distance between points. Second, there is a sampling-based method that sorts points using Morton codes and classifies every X-th point as a sub-LoD that can be a candidate for the neighbor set. Third, there is a method that constructs an octree for the points and classifies points close to the center as sub-LoDs by selecting them based on the node levels of the octree.

[0242] Points sampled using one of the three methods described earlier search for neighboring points within their respective Lines of Dungeon to generate a predictor, continuing the search until the three nearest neighbor points are found. There are two search methods for finding nearest neighbor points. The first is the initial search, which defines a 3D area surrounding each point and searches only for points existing within that area. The second is the extended search, which performs the search based on the order of the points rather than the 3D space.

[0243] When compressing a multi-frame point cloud, initial search and extended search can be applied to both intra-search and inter-search. Inter-search differs from intra-search in that neighbor point search is performed in the reference frame rather than the current frame. Neighbor point search can be performed in the order of intra initial search, intra extended search, inter initial search, and inter extended search.

[0244] According to the conventional G-PCC standard technology, unlike the intra-extended search, which is performed only when three or more points are not found as nearest neighbors in the intra-initial search, the inter-extended search was always performed regardless of the number of neighbor points previously found. The advantage of the initial search is that it reduces search time by quickly searching nearby points within a narrow search range and thereby reducing the process of moving on to the extended search, but this effect is not fully realized in inter-prediction. Furthermore, performing the intra-extended search only when fewer than three neighbor points are found in the intra-initial search may not always be the optimal condition.

[0245] Accordingly, the embodiments can increase compression speed by compensating for the shortcomings of the nearest neighbor point search process in point cloud compression requiring fast and accurate encoding / decoding. Specifically, the embodiments can support a method for accelerating the encoding / decoding process through a technique that sets the execution conditions for intra / inter initial search and extended search according to content characteristics.

[0246] Modifications and combinations between the embodiments disclosed in this specification are possible, and terms used in this specification may be understood based on their intended meanings within the scope of their widespread use in the relevant field (e.g., G-PCC).

[0247] The nearest neighbor point search method according to the embodiments can be performed or applied in a PCC encoder / decoder.

[0248] Initial Search

[0249] FIG. 11 shows an initial search according to the embodiments.

[0250] Referring to FIG. 11, the initial search is a method of searching in three-dimensional space by setting an area containing the point (gray area in FIG. 11) based on the point seeking the nearest neighbor point, and setting an area of ​​the same size as that area in all directions. When the area of ​​the size to which the point belongs is referred to as a mini-cube, the intra initial search searches a total of 27 mini-cubes, including the mini-cube to which the point in the current frame belongs, and the area for these 27 mini-cubes can be referred to as the intra initial search range. In the case of the inter initial search, a single central mini-cube is searched in the reference frame, and this can be referred to as the inter initial search range. The mini-cube according to the embodiments may correspond to terms referring to three-dimensional space areas, such as blocks.

[0251] Specifically, according to the embodiments, a point cloud frame may be divided into one or more slices, and points within each slice may generate Levels of Detail (LoDs) based on Morton code order. An initial search may be performed for a specific point at a specific LoD level, and the initial search may be performed for points belonging to adjacent detail levels (e.g., points belonging to a level coarser than the specific LoD level), and for points within the same detail level as the specific point. Additionally, in the case of a multi-frame point cloud, an initial search may also be performed within a reference slice of the reference frame for the current point cloud frame.

[0252] Referring to FIG. 11, specifically, an initial search (intra-initial search) within the current point cloud frame can be performed based on blocks that spatially portion the current point cloud frame or the current slice, and the size of the blocks can be determined according to the LoD level to which the current point to be searched belongs. Based on the location of the block containing the current point, the initial search can set a total of 27 blocks adjacent in the forward direction, including the block, as search targets.

[0253] Referring to FIG. 11, the initial search (inter-initial search) within the reference frame for the current point cloud frame can be performed based on blocks that spatially divide the reference frame or reference slice, and similarly, a total of 27 blocks adjacent in all directions, including the block, can be set as search targets based on the location of the block containing the coordinates corresponding to the current point within the reference frame or reference slice.

[0254] According to the embodiments, when performing an initial search, it can be determined that the nearest neighbor point among the neighbor points searched based on distance. For example, when the coordinates of the current point are (x, y, z) and the coordinates of the neighbor point searched are (x', y', z'), the distance D between the two points can be calculated using the following mathematical formula 1.

[0255]

[0256] According to the embodiments, the point with the smallest distance value calculated by the above formula, that is, the closest point, can be called the nearest neighbor point. In the intra initial search, three nearest neighbor points within the intra initial search area are found and used as predictors, and an intra extended search can be performed only when three or more neighbor points are not found.

[0257] Extended Search

[0258] FIG. 12 shows a Morton code in a two-dimensional plane according to embodiments, and FIG. 13 shows an extended search according to embodiments.

[0259] Referring to FIGS. 12 and 13, extended search is a method of performing a search using the order of Morton codes of points rather than three-dimensional space.

[0260] Morton code is one of the space-filling curves as illustrated in Fig. 12. Generally, in attribute compression of a G-PCC encoder / decoder, Morton codes are assigned using the 3D coordinates of each point, and attribute encoding / decoding can be performed by aligning the points in the order of the Morton codes. Specifically, in a G-PCC encoder / decoder, points are reordered based on the Morton codes for the reconstructed geometry, and Levels of Detail (LoDs) structures can be generated by subsampling points according to a certain standard from the finest level to the coarsest level.

[0261] The extended search range can be determined through the signaled value, and as shown in the example of FIG. 13, if the extended search range is 2s based on the current point with Morton code j, it is possible to search from the point with Morton code js to the point with Morton code j+s. Here, j is the Morton code value assigned to the current point, and s is a parameter corresponding to half the size of the search range, which can be transmitted through signaling.

[0262] Points within the range become candidates for neighbor points, and the distance between a candidate neighbor point and the current point can be calculated using the mathematical formula 1 above. Even if the nearest neighbor point has already been selected in the preceding initial search, if the distance of the neighbor point found through the extended search is closer, the nearest neighbor point to be used as a predictor for the current point is updated to the point with the closer distance.

[0263] According to embodiments, the extended search may include an intra-extended search performed on points within the current frame and an inter-extended search performed on points within the reference frame. Specifically, the intra-extended search may be performed based on the index of a point within the finest level of the LoDs of the current slice, and the inter-extended search may be performed based on the index of a point within the finest level of the LoDs of the reference slice.

[0264] The inter initial search and inter extended search according to the embodiments may be referred to as the inter frame initial search and inter frame extended search, respectively.

[0265] Conditional search method

[0266] Conditional intra initial search method

[0267] According to the embodiments, a method for determining whether to perform an intra initial search based on specific conditions can be efficient.

[0268] According to the embodiments, a flag (e.g., enable_intra_initial_search) regarding whether to perform an intra initial search may be signaled, and if the value of the flag is 0, an intra initial search may not be performed. If the intra initial search has content characteristics that do not provide a substantial search time reduction effect, the search may be omitted to efficiently decode attribute information.

[0269] According to the embodiments, a threshold number of nearest neighbor candidates within an intra initial search range (e.g., intra_initial_NN_candidates_threshold1) is signaled, so that an intra initial search may not be performed if the number of candidates within the intra initial search range is less than the threshold number. In this case, unnecessary search operations can be reduced by omitting the initial search in sparse regions where the intra initial search range does not contain sufficient candidates.

[0270] Alternatively, the reference number of nearest neighbor point candidates within the intra initial search area may be set to a preset value without signaling, so that intra initial search is not performed if the number of candidates is less than the reference number. According to the embodiments, the reference number may be 3 or 6.

[0271] According to the embodiments, based on the density of points, intra-initial search may not be performed for areas below a specific density threshold (e.g., intra_density_threshold). In low-density areas, since there may be few neighboring point candidates within the intra-initial search area, it may be efficient to omit such search. According to the embodiments, density may be measured based on the average distance between points. Alternatively, density may be measured based on the number of points within a specific area.

[0272] Conditional intra-extended search method

[0273] According to the embodiments, a method for determining whether to perform an intra-extended search based on specific conditions can be efficient.

[0274] According to the embodiments, a flag (e.g., enable_intra_extended_search) regarding whether to perform intra-extended search is signaled, and intra-extended search is performed only when the value of the flag is 1. When the flag is 0, the nearest neighbor point search can be terminated based only on the results of the intra-initial search, which can be utilized to reduce search time in content where there is a low need to perform intra-extended search.

[0275] According to the embodiments, an intra extended search can be performed when the distance between the current point and the farthest neighbor point among the nearest neighbor points selected in the intra initial search is greater than a specific threshold (e.g., farthest_chosenNN_distance_threshold1). This ensures the quality of neighbor points above a certain standard by performing an additional extended search only when the quality of neighbor points discovered in the intra initial search is insufficient, that is, when the distance between the current point and neighbor points is greater than the threshold.

[0276] According to the embodiments, an intra-initial search can be performed only when the number of candidates is less than the threshold number by signaling a threshold number of nearest neighbor point candidates within the intra-initial search area (e.g., intra_initial_NN_candidates_threshold2). Alternatively, an intra-initial search can be performed only when the number of candidates is less than the threshold number by pre-setting the threshold number. According to the embodiments, the threshold number may be 3 or 6.

[0277] Conditional Inter-Initial Search Method

[0278] According to the embodiments, a method for determining whether to perform an inter initial search based on specific conditions may be efficient.

[0279] According to the embodiments, a flag (e.g., enable_inter_initial_search) regarding whether to perform an inter initial search may be signaled, and if the value of the flag is 0, the inter initial search may not be performed. In this way, the search may be omitted if the inter initial search does not contribute substantially to the neighbor search of the current point.

[0280] According to embodiments, a threshold number of nearest neighbor point candidates within an inter initial search range (e.g., inter_initial_NN_candidates_threshold1) may be signaled so that inter initial search is not performed if the number of candidates is less than the threshold number. Alternatively, the threshold number may be pre-set so that inter initial search is not performed if the number of candidates is less than the threshold number. According to embodiments, the threshold number may be 3 or 6.

[0281] According to the embodiments, based on the density of points, inter initial search may not be performed for areas below a specific density threshold (e.g., inter_density_threshold). In this case, since the density of an unsubsampled reference frame may differ from the density of a subsampled current frame, it may be efficient to use a different threshold separate from the density threshold (e.g., intra_density_threshold) used in intra initial search. According to the embodiments, density may be measured based on the average distance between points. Alternatively, density may be measured based on the number of points within a specific area.

[0282] Conditional Inter-Expanding Search Method

[0283] According to the embodiments, a method for determining whether to perform an inter-extended search based on specific conditions can be efficient.

[0284] According to the embodiments, a flag (e.g., enable_inter_extended_search) regarding whether to perform inter-extended search is signaled, and inter-extended search is performed only when the value of the flag is 1. This allows the search to be omitted in content where inter-extended search is unnecessary, thereby improving encoding / decoding speed.

[0285] According to the embodiments, an inter-expansion search can be performed when the distance between the current point and the farthest neighbor point among the previously selected nearest neighbor points is greater than or equal to a specific threshold (farthest_chosenNN_distance_threshold2). This ensures the quality of neighbor points above a certain standard by performing an additional inter-expansion search only when it is determined that the quality of neighbor points obtained in the previous search is insufficient, that is, when the distance between the current point and neighbor points is greater than or equal to the threshold.

[0286] According to the embodiments, an inter-extended search can be performed only when the number of candidates is less than the threshold number by signaling a threshold number of nearest neighbor point candidates within the inter-initial search area (e.g., inter_initial_NN_candidates_threshold2). Alternatively, an inter-extended search can be performed only when the number of candidates is less than the threshold number by pre-setting the threshold number. According to the embodiments, the threshold number may be 3. Or, the threshold number may be 6. As previously mentioned, in the conventional G-PCC standard technology, the inter-extended search was always performed regardless of the number of neighbor points previously searched, but according to the embodiments, whether to perform the inter-extended search can be controlled based on the number of nearest neighbor points searched in the inter-initial search (e.g., when the number of nearest neighbor points is less than 3).

[0287] Specifically, according to the embodiments, the step of performing an inter-frame extended search may be performed when the number of nearest neighbor points obtained in the inter-frame initial search is less than a first value. Here, the first value may be 3. That is, an inter-frame extended search based on the finest level of the LoDs of the reference slice may be performed only when the number of nearest neighbor points or points included in predictor candidates obtained in the inter-frame initial search based on blocks spatially dividing the reference frame or reference slice is less than 3. Through this, the search time can be reduced by not performing an inter-frame extended search when sufficient neighbor point candidates are obtained through the inter-frame initial search alone.

[0288] According to the embodiments, not all of the above-described conditions are applied simultaneously, but at least one of the above-described conditions may be applied. Specifically, according to the embodiments, an intra-extended search may be performed when the number of nearest neighbor points obtained in an intra-initial search is less than 3, which is a preset reference number, and an inter-extended search may be performed when the number of nearest neighbor points obtained in an inter-initial search is less than 3, which is a preset reference number.

[0289] FIG. 14 shows a block diagram of a PCC data encoder according to embodiments.

[0290] Referring to FIG. 14, each component of the PCC encoder may correspond to hardware, software, a processor, and / or a combination thereof. PCC data is input to the encoder and encoded, and a geometry information bitstream and an attribute information bitstream may be output.

[0291] According to the embodiments, the data input unit can read and set the received data (e.g., ply, configuration file, etc.).

[0292] According to the embodiments, the coordinate system transformation unit can support coordinate system transformation, such as changing the xyz axes or transforming from an xyz orthogonal coordinate system to a spherical coordinate system.

[0293] According to the embodiments, 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 (i.e., geometry quantization value) according to the scale setting.

[0294] According to the embodiments, the spatial partitioning unit can divide the point cloud frame into tiles or slices for region-specific access or parallel processing of content.

[0295] According to the embodiments, the geometry encoding unit can generate a geometry information bitstream by encoding spatially partitioned geometry information.

[0296] According to the embodiments, the color conversion processing unit can support attribute type conversion, such as converting RGB colors to YUV.

[0297] According to the embodiments, the color recalculation unit can predict an attribute value suitable for the changed location when a scale is applied to the geometry and the location information value is changed.

[0298] According to the embodiments, the attribute information encoding unit can generate an attribute information bitstream by receiving color-rescaled original attribute information, restored geometry information, and a reference frame as input and performing encoding. The detailed configuration of the attribute information encoding unit is described in detail below in FIG. 15.

[0299] According to the embodiments, the reference frame generation unit stores restored geometry information and restored attribute information in a reference frame buffer and can transmit reference frame data from the reference frame to another module.

[0300] FIG. 15 shows a block diagram of an attribute information encoding unit according to embodiments.

[0301] Referring to FIG. 15, each component of the attribute encoding unit may correspond to hardware, software, a processor, and / or a combination thereof.

[0302] According to the embodiments, the LoD generation unit can generate an LoD by receiving segmented point cloud data as input, and point-specific neighbors used for lifting transformation and prediction transformation can be determined. In this case, the LoD generation unit can be performed when LoD parameters exist.

[0303] According to the embodiments, when point-specific neighbors are determined in the LoD generation unit, an attribute reference frame may be received as input for more efficient neighbor search, and points in the attribute reference frame may also be utilized. Specifically, not only the process of obtaining nearest neighbor points for points included in the point cloud frame, but also the process of obtaining nearest neighbor points for points included in the reference frame for the point cloud frame can be performed. The detailed configuration of the LoD generation unit is described in detail below in FIG. 18.

[0304] According to the embodiments, 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 encoding unit. Additionally, the restored attribute information can be output by internally performing an inverse transform.

[0305] According to the embodiments, the predictive transform may include a process of predicting the current point using neighbor points determined during the LoD generation process. The value obtained by differentiating the predicted attribute information and the original attribute information can be referred to as the transform coefficient, and this 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 transform.

[0306] According to the embodiments, Region Adaptive Hierarchical Transform (RAHT) can be performed when there are no LoD parameters. RAHT can receive segmented point cloud data as input and perform RAHT transformation to generate transformation coefficients transformed into the frequency domain. Subsequently, the transformation coefficients can be quantized and transmitted to an attribute information entropy encoding unit.

[0307] According to embodiments, the attribute information encoding unit illustrated in FIG. 15 can be utilized to encode attribute data included in a point cloud frame for point cloud data. Specifically, the attribute information encoding unit can generate LoDs based on Morton code and perform the process of obtaining nearest neighbor points of points within the levels of the LoDs. The process of obtaining nearest neighbor points may include the step of obtaining nearest neighbor points for points included in the point cloud frame and the step of obtaining nearest neighbor points for points included in a reference frame for the point cloud frame.

[0308] PCC data decoder

[0309] FIG. 16 shows a block diagram of a PCC data decoder according to embodiments.

[0310] Referring to FIG. 16, each component may correspond to hardware, software, a processor, and / or a combination thereof. An encoded geometry information bitstream and an attribute information bitstream are input to the decoder, and PCC data that has been decoded and restored can be output.

[0311] According to the embodiments, the geometry information decoding unit can receive a geometry information bitstream, decode it, and restore the geometry information.

[0312] According to the embodiments, 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.

[0313] According to the embodiments, the geometry information conversion inverse quantization processing unit can restore the signaled scale (i.e., geometry quantization value) and apply it to the geometry position x, y, z values ​​of the restored point to restore it.

[0314] According to the embodiments, the attribute residual information entropy decoding unit can entropy decode the attribute bitstream.

[0315] According to the embodiments, the attribute information decoding unit can receive an attribute information bitstream, decode it, and restore the attribute information. The detailed configuration of the attribute information decoding unit is described in detail below in FIG. 17.

[0316] According to the embodiments, the color inverse conversion processing unit can restore the converted attribute to an RGB color.

[0317] According to the embodiments, the reference frame generation unit stores restored geometry information and restored attribute information in a reference frame buffer and can transmit reference frame data from the reference frame to another module.

[0318] FIG. 17 shows a block diagram of an attribute information decoding unit according to embodiments.

[0319] Referring to FIG. 17, each component of the attribute information decoding unit may correspond to hardware, software, a processor and / or a combination thereof.

[0320] According to the embodiments, the attribute information entropy decoder can receive an attribute information bitstream, decode it, and restore the conversion coefficient.

[0321] According to the embodiments, the LoD generation unit may generate LoD by receiving restored geometry information as input, and point-by-point neighbors used for lifting transformation and prediction transformation may be determined. In this case, the LoD generation unit may be performed when there are LoD parameters.

[0322] According to the embodiments, when point-specific neighbors are determined in the LoD generation unit, an attribute reference frame may be received as input for more efficient neighbor search, and points in the attribute reference frame may also be utilized. Specifically, not only the process of obtaining nearest neighbor points or predictor candidates for points included in the point cloud frame, but also the process of obtaining nearest neighbor points or predictor candidates for points included in the reference frame for the point cloud frame can be performed. The detailed configuration of the LoD generation unit is described in detail below in FIG. 18.

[0323] According to the embodiments, the lifting inverse transform unit 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 process for each LoD level.

[0324] According to the embodiments, the prediction inverse transform can restore attributes by combining the predicted value of the current point and the inverse transform value of the input transformation coefficient using neighbor points determined during the LoD generation process.

[0325] According to the embodiments, the Inverse RAHT can restore attribute information by receiving restored geometry information and transformation coefficients as input and performing the inverse process of the Region Adaptive Hierarchical Transform (RAHT).

[0326] According to embodiments, the attribute information decoding unit illustrated in FIG. 17 can be utilized to decode attribute data included in a point cloud frame for point cloud data. Specifically, the attribute information decoding unit can generate LoDs based on Morton code and perform a process of obtaining nearest neighbor points of points within the levels of the LoDs. The process of obtaining nearest neighbor points may include the step of obtaining nearest neighbor points for points included in the point cloud frame and the step of obtaining nearest neighbor points for points included in a reference frame for the point cloud frame.

[0327] Additionally, according to the embodiments, the attribute information decoding unit may perform a process of generating LoDs, obtaining predictor candidates for points within the levels of the LoDs, and obtaining a set of predictors for points based on the predictor candidates. The process of obtaining predictor candidates may include the step of obtaining predictor candidates for points included in a point cloud frame and the step of obtaining predictor candidates for points included in a reference frame for the point cloud frame.

[0328] FIG. 18 shows a block diagram of an LoD generation unit according to embodiments.

[0329] The LoD generation unit of FIG. 18 may be included in the attribute information encoding unit of FIG. 15 and / or the attribute information decoding unit of FIG. 17 described above.

[0330] Referring to FIG. 18, each component of the LoD generation unit may correspond to hardware, software, a processor, and / or a combination thereof. The LoD generation unit may receive restored geometry information and output a multi-layered LoD.

[0331] According to the embodiments, the Morton code assignment and alignment unit can assign Morton codes to points and then align the points according to a predetermined alignment method.

[0332] According to the embodiments, the subsampling unit can perform subsampling on an index list to distinguish current points as LoDs.

[0333] According to the embodiments, the nearest neighbor point search unit may find one or more neighbor points for each point based on geometry for the prediction and update process of lifting transformation for points classified by LoD, and store the index of the corresponding point. At this time, points of a reference frame may also be used to find neighbor points.

[0334] According to the embodiments, the nearest neighbor point search unit can perform an intra initial search (intra atlas search), an intra extended search (intra-full search), an inter initial search (inter atlas search), and an inter extended search (inter-full search). The detailed configuration of the nearest neighbor point search unit is described in detail below in FIG. 19.

[0335] According to embodiments, the LoD generation unit illustrated in FIG. 18 can generate LoDs based on Morton code and support the process of obtaining nearest neighbor points or predictor candidates for points within the level of the LoDs. Specifically, the process of obtaining nearest neighbor points or predictor candidates may include the step of obtaining nearest neighbor points or predictor candidates for points included in a point cloud frame and the step of obtaining nearest neighbor points or predictor candidates for points included in a reference frame for the point cloud frame. Additionally, the process of obtaining a set of predictors for a point based on the predictor candidates may be performed.

[0336] FIG. 19 shows a block diagram of a nearest neighbor point search unit according to embodiments.

[0337] The nearest neighbor point search unit of FIG. 19 may be included in the LoD generation unit of FIG. 18.

[0338] Referring to FIG. 19, each component of the nearest neighbor point search unit may correspond to hardware, software, a processor, and / or a combination thereof.

[0339] According to the embodiments, the intra-neighbor point search unit may perform intra-initial search and intra-extended search, and as described above in the conditional search method, intra-initial search or intra-extended search may not be performed depending on the condition. Information for verifying the fulfillment of the condition may be transmitted to a decoder via signaling, or the decision to perform the search may be made based on pre-set information received from the decoder.

[0340] According to the embodiments, the inter-neighbor point search unit may perform inter-initial search and inter-extended search, and as described above in the conditional search method, inter-initial search or inter-extended search may not be performed depending on the condition. Information for verifying the fulfillment of the condition may be transmitted to a decoder via signaling, or the decision to perform the search may be made based on pre-set information received from the decoder.

[0341] The operation of the intra-neighbor point search unit is specifically explained as follows.

[0342] According to the embodiments, the intra-neighbor point search unit can conditionally determine whether to perform an intra-initial search through the aforementioned conditional intra-initial search method. If the flag regarding whether to perform is false, if there are fewer intra-initial search neighbor point candidates than a reference number, or if the density is lower than a specific threshold, the intra-initial search may not be performed. The flag, the reference number, and the threshold can be known through signaling. Alternatively, without signaling, the decoder can determine whether to perform the search based on a preset value.

[0343] According to the embodiments, the intra-neighbor point search unit can conditionally determine whether to perform an intra-extended search through the aforementioned conditional intra-extended search method. An intra-extended search may be performed if the flag for execution is false, if there are more neighbor point candidates than a reference number, or if the distance to the farthest point among the nearest neighbor points found in the initial intra-search is greater than a specific threshold. The flag, the reference number, and the threshold can be known through signaling. Alternatively, without signaling, the decoder may determine whether to perform the search based on a preset value.

[0344] The operation of the inter-neighbor point search unit is specifically explained as follows.

[0345] According to the embodiments, the inter-neighbor point search unit can conditionally determine whether to perform an inter-initial search through the aforementioned conditional inter-initial search method. If the flag regarding whether to perform is false, the inter-initial search may not be performed if the number of inter-initial search neighbor point candidates is less than a reference number, or if the density is lower than a specific threshold. The flag, the reference number, and the threshold can be determined through signaling. Alternatively, without signaling, the decoder may determine whether to perform the search based on a preset value.

[0346] According to the embodiments, the inter-neighbor point search unit can conditionally determine whether to perform an inter-extended search through the aforementioned conditional inter-extended search method. An inter-extended search may be performed if the flag for execution is false, if there are more neighbor point candidates than a reference number, or if the distance to the farthest point among the previously searched nearest neighbor points is greater than a specific threshold. The flag, the reference number, and the threshold can be determined through signaling. Alternatively, without signaling, the decoder may determine whether to perform the search based on a preset value.

[0347] According to embodiments, the nearest neighbor point search unit illustrated in FIG. 19 may include a process of performing an inter-frame initial search based on a block for an area of ​​a reference frame and performing an inter-frame extended search based on a Morton code. Specifically, the step of performing an inter-frame extended search may be performed when the number of nearest neighbor points obtained in the inter-frame initial search is less than a first value, and the first value may be 3.

[0348] FIG. 20 shows a flowchart of intra-neighbor point search and inter-neighbor point search according to embodiments.

[0349] Referring to FIG. 20, a detailed flowchart of the search process performed in the intra-neighbor point search unit and the inter-neighbor point search unit described above can be seen. The flowchart illustrated in FIG. 20 includes a process for determining whether to conditionally perform each of the intra initial search, intra extended search, inter initial search, and inter extended search, and corresponds to the operation of the nearest neighbor point search unit of FIG. 19 described above.

[0350] Referring to FIG. 20, three conditions (flag, number of neighbor candidates, density, or distance) are applied to each of the intra initial search, intra extended search, inter initial search, and inter extended search, respectively, but the embodiments are not limited thereto, and at least one of the above-described conditions may be applied.

[0351] Signaling for applying embodiments

[0352] According to the embodiments, relevant information for performing the above-described encoding / decoding process can be signaled. The signaling information according to the embodiments can be used at a transmitting end or a receiving end, etc.

[0353] FIG. 21 shows a bitstream structure according to embodiments.

[0354] A transmitting device / method or encoding method / device according to embodiments (transmitting device (10000) of FIG. 1, point cloud video encoder (10002), transmitter (10003), encoding based on initial search of FIG. 2, Morton code of FIG. 12, encoding based on extended search of FIG. 13, PCC data encoder of FIG. 14, attribute information encoding unit of FIG. 15, LoD generation unit of FIG. 18, nearest neighbor point search unit of FIG. 19, encoding based on intra-neighbor point search and inter-neighbor point search of FIG. 20, encoding method of FIG. 24, etc.) can encode point cloud data, generate a bitstream of FIG. 21, encapsulate a file containing the bitstream, and transmit it to a receiving device.

[0355] A receiving device / method or decoding method / device according to embodiments (receiving device (10004), receiver (10005), point cloud video decoder (10006) of FIG. 1, transmission-decoding-rendering (20002-20003-20004) of FIG. 2, decoder of FIG. 7, receiving device of FIG. 9, device of FIG. 10, decoding based on initial search of FIG. 11, Morton code of FIG. 12, decoding based on extended search of FIG. 13, PCC data decoder of FIG. 16, attribute information decoding unit of FIG. 17, LoD generation unit of FIG. 18, nearest neighbor point search unit of FIG. 19, decoding based on intra-neighbor point search and inter-neighbor point search of FIG. 20, decoding method of FIG. 25, etc.) receives a file including a bitstream of FIG. 21, and based on parameter information included in the bitstream, points Cloud data can be decrypted.

[0356] The bitstream of FIG. 21 according to the embodiments may include parameter information (syntax elements) of FIG. 22 and FIG. 23.

[0357] Referring to FIG. 21, 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). Additionally, signaling information regarding the point cloud data can be generated and processed by a metadata processing unit of a point cloud data transmission device and included in the point cloud bitstream.

[0358] Each abbreviation used in FIG. 21 means the following. Each abbreviation may be referred to by other terms within the scope of equivalent meaning.

[0359] SPS: Sequence Parameter Set

[0360] GPS: Geometry Parameter Set

[0361] APS: Attribute Parameter Set

[0362] TPS: Tile Parameter Set

[0363] Geom: Geometry bitstream = Geometry slice header + [Geometry PU header + Geometry PU data] | Geometry slice data

[0364] Attr: Attribute bitstream = Attribute data unit header + [Attribute PU header + Attribute PU data] | Attribute data unit data

[0365] FIG. 22 shows the attribute parameter set syntax according to the embodiments.

[0366] The attribute parameter set of FIG. 22 can be included in the bitstream of FIG. 21.

[0367] Parameter information of a conditional search method for high-speed LoD generation according to the embodiments can be added to an Attribute Parameter Set (APS) and signaled. By combining the signaling information, it can be efficiently signaled to support a conditional nearest neighbor point search method for LoD generation. The name of the signaling information can be understood within the scope of the meaning and function of the signaling information.

[0368] The attribute parameter set ID (aps_attr_parameter_set_id) provides an identifier for the APS for reference by other syntax elements. The value of aps_attr_parameter_set_id can range from 0 to 15.

[0369] The sequence parameter set ID (aps_seq_parameter_set_id) specifies the value of sps_seq_parameter_set_id for the active SPS. The value of aps_seq_parameter_set_id can range from 0 to 15.

[0370] The extension presence flag (aps_extension_present) specifies whether aps_extension_data syntax elements exist within the APS syntax structure. In the bitstream according to the embodiments, aps_extension_present must be 0. A value of 1 for aps_extension_present is reserved for future use by ISO / IEC.

[0371] The distribution-based prediction enable flag (prediction_with_distribution_enabled) specifies whether prediction coefficients are derived based on the spatial distribution of predictors (value 1) or (value 0). If prediction_with_distribution_enabled is not present, the value may be estimated to be 0.

[0372] The enable_intra_initial_search flag indicates whether to perform an intra initial search. Based on the aforementioned conditional intra initial search method, whether to perform an intra initial search can be determined according to the value of the flag.

[0373] The enable_intra_extended_search flag indicates whether to perform intra-extended search. Based on the aforementioned conditional intra-extended search method, whether to perform intra-extended search can be determined according to the value of the flag.

[0374] The enable_inter_initial_search flag indicates whether to perform an inter initial search. Based on the aforementioned conditional inter initial search method, whether to perform an inter initial search can be determined according to the value of the flag.

[0375] The enable_inter_extended_search flag indicates whether to perform inter-extended search. Based on the aforementioned conditional inter-extended search method, whether to perform inter-extended search can be determined according to the value of the flag.

[0376] Referring to FIG. 22, the attribute_parameter_set() syntax structure may include aps_attr_parameter_set_id and aps_seq_parameter_set_id. If aps_extension_present exists, prediction_with_distribution_enabled, enable_intra_initial_search, enable_intra_extended_search, enable_inter_initial_search, and enable_inter_extended_search may be signaled only if the conditions are satisfied that attribute coding type (attr_coding_type) is 1 or 2 and pred_set_size_minus1 is 2 or greater.

[0377] That is, the four flags for applying the aforementioned conditional search method (enable_intra_initial_search, enable_intra_extended_search, enable_inter_initial_search, enable_inter_extended_search) are signaled only when the attribute coding method is based on a predictive transformation (attr_coding_type == 1) or a lifting transformation (attr_coding_type == 2) and the predictor set size (pred_set_size_minus1 + 1) is 3 or greater, so that the conditional search method can be applied when the conditions are met.

[0378] FIG. 23 shows the attribute data unit header syntax according to the embodiments.

[0379] The attribute_data_unit_header of FIG. 23 may be included in the bitstream of FIG. 21.

[0380] Parameter information of the conditional search method for high-speed LoD generation according to the embodiments can be added to the Attribute Data Unit Header and signaled. By combining the signaling information, it can be efficiently signaled to support the high-speed nearest neighbor point search method for LoD generation. The name of the signaling information can be understood within the scope of the meaning and function of the signaling information.

[0381] The attribute parameter set reference ID (adu_attr_parameter_set_id) specifies the active APS through aps_attr_parameter_set_id.

[0382] The attribute data unit time ID (adu_temporal_id) specifies the temporal ID of the frame associated with the attribute data unit.

[0383] Intra_initial_NN_candidates_threshold1 specifies the threshold number of intra-initial search neighbor point candidates. Whether to perform intra-initial search can be determined based on this threshold number.

[0384] The intra_density_threshold specifies the density threshold for determining whether to perform an intra initial search. The decision to perform an intra initial search can be made based on this density threshold.

[0385] The threshold number of intra-initial NN neighbor point candidates (intra_initial_NN_candidates_threshold2) specifies the threshold number of intra-initial NN neighbor point candidates for determining whether to perform intra-initial NN. The decision to perform intra-initial NN can be made based on this threshold number.

[0386] The intra farthest neighbor point distance threshold (farthest_chosenNN_distance_threshold1) specifies a threshold for the distance between the current point and the farthest neighbor point selected so far. Based on this threshold, it can be determined whether to perform an intra-extended search.

[0387] The threshold for inter initial search neighbor point candidates 1 (inter_initial_NN_candidates_threshold1) specifies the threshold number of inter initial search neighbor point candidates. Whether to perform an inter initial search can be determined based on this threshold number.

[0388] The inter_density_threshold specifies the density threshold for determining whether to perform an inter initial search. Based on this density threshold, the decision to perform an inter initial search can be made.

[0389] The threshold number of inter-initial NN neighbor point candidates (inter_initial_NN_candidates_threshold2) specifies the threshold number of inter-initial NN neighbor point candidates for determining whether to perform inter-initial NN. The decision to perform inter-initial NN can be made based on this threshold number.

[0390] The farthest_chosenNN_distance_threshold2 specifies a threshold for the distance between the current point and the farthest neighbor point selected so far. Based on this threshold, it can be determined whether to perform an inter-expansion search.

[0391] Referring to FIG. 23, the attribute_data_unit_header() syntax structure may include adu_attr_parameter_set_id and adu_temporal_id. According to embodiments, the attribute parameter set (APS) referenced by the current attribute data unit (ADU) can be identified through adu_attr_parameter_set_id. Referring to FIG. 22 described above, the values ​​of enable_intra_initial_search, enable_intra_extended_search, enable_inter_initial_search, and enable_inter_extended_search can be obtained from the identified APS, and conditional signaling within the attribute data unit header syntax of FIG. 23 can be determined based on the values ​​of each obtained flag.

[0392] Specifically, if enable_intra_initial_search of FIG. 22 described above is true, intra_initial_NN_candidates_threshold1 and intra_density_threshold may be signaled. If enable_intra_extended_search is true, intra_initial_NN_candidates_threshold2 and farthest_chosenNN_distance_threshold1 may be signaled. If enable_inter_initial_search is true, inter_initial_NN_candidates_threshold1 and inter_density_threshold may be signaled. If enable_inter_extended_search is true, inter_initial_NN_candidates_threshold2 and farthest_chosenNN_distance_threshold2 may be signaled.

[0393] FIG. 24 illustrates an encoding method according to embodiments.

[0394] The encoding method according to the embodiments may include the step of encoding geometry data of point cloud data (S2400); and / or the step of encoding attribute data of point cloud data (S2410). The step of encoding geometry data (S2400) and / or the step of encoding attribute data (S2410) may include the encoding operation of point cloud data described in FIGS. 1 to 23.

[0395] Specifically, the step of encoding geometry data (S2400) may include the encoding operation described above in FIGS. 1 to 4, FIG. 9, FIG. 10, FIG. 14, FIG. 21, etc. The step of encoding attribute data (S2410) may include the encoding operation described above in FIGS. 1 to 3, FIGS. 5 to 6, FIG. 8, FIGS. 10 to 15, FIGS. 18 to 23, etc.

[0396] Referring together to FIG. 15 and FIG. 18, the encoding method according to the embodiments includes geometry data included in a point cloud frame for point cloud data and attribute data included in a point cloud frame, and the step of encoding the attribute data of the point cloud data includes: generating Levels of Detail (LoDs) based on Morton code; obtaining nearest neighbor points of a point in a level of the LoDs; and the step of obtaining nearest neighbor points of a point may include obtaining nearest neighbor points for points included in a point cloud frame; and obtaining nearest neighbor points for points included in a reference frame for the point cloud frame.

[0397] Referring together with FIG. 19, the encoding method according to the embodiments may include the step of obtaining nearest neighbor points for points included in a reference frame, the step of performing an inter-frame initial search based on a block for a region of the reference frame; and the step of performing an inter-frame extended search based on a Morton code.

[0398] In the encoding method according to the embodiments, the step of performing an inter-frame extended search may be performed when the number of nearest neighbor points obtained in the inter-frame initial search is less than a first value. In the encoding method according to the embodiments, for example, the first value may be 3. However, the first value is not limited to 3 and may vary depending on the encoder / decoder settings. For example, the first value may be set differently depending on the profile, level, encoding / decoding conditions, memory performance, etc. Therefore, the embodiments may include not only the case where the first value is 3, but also the case where it is set to a value other than 3 depending on the settings according to the embodiments.

[0399] The encoding method is performed by an encoding device. The encoding device includes a memory; and at least one processor connected to the memory; and the at least one processor may be configured to: encode geometry data of point cloud data; and encode attribute data of point cloud data. The at least one processor may be configured to perform the operations of the above-described method.

[0400] The embodiments further include a computer-readable storage medium for storing a bitstream generated by the method according to FIG. 24.

[0401] The embodiments further include a method comprising the steps of: acquiring a bitstream for point cloud data; generating the bitstream based on the steps of encoding geometry data of the point cloud data and encoding attribute data of the point cloud data; and transmitting data including the bitstream.

[0402] FIG. 25 illustrates a decoding method according to embodiments.

[0403] The decoding method according to the embodiments may include a step of decoding geometric data of point cloud data within a bitstream (S2500); and / or a step of decoding attribute data of point cloud data (S2510). The step of decoding geometric data of point cloud data (S2500) and / or the step of decoding attribute data of point cloud data (S2510) may perform the decoding operation of FIGS. 1 to 23 described above.

[0404] Specifically, the step (S2500) of decoding geometry data of point cloud data within a bitstream may include the decoding operation described above in FIGS. 1, FIGS. 2, FIGS. 4, FIGS. 7, FIGS. 9, FIGS. 10, FIGS. 16, FIGS. 21, etc. The step (S2510) of decoding attribute data of point cloud data may include the decoding operation described above in FIGS. 1, FIGS. 2, FIGS. 5 to 7, FIGS. 9 to 13, FIGS. 16 to 23, etc.

[0405] Referring to FIG. 17 and FIG. 18 together, the decoding method according to the embodiments includes geometry data included in a point cloud frame for point cloud data and attribute data included in a point cloud frame, and the step of decoding attribute data of the point cloud data includes: generating Levels of Detail (LoDs) based on Morton code; obtaining nearest neighbor points of a point in a level of the LoDs; and the step of obtaining nearest neighbor points of a point may include: obtaining nearest neighbor points for points included in a point cloud frame; and obtaining nearest neighbor points for points included in a reference frame for the point cloud frame.

[0406] Referring together with FIG. 19, the decoding method according to the embodiments may include the step of obtaining nearest neighbor points for points included in a reference frame, the step of performing an inter-frame initial search based on a block for a region of the reference frame; and the step of performing an inter-frame extended search based on a Morton code.

[0407] The decoding method according to the embodiments may perform the step of performing an inter-frame extension search when the number of nearest neighbor points obtained in the inter-frame initial search is smaller than the first value.

[0408] In the decoding method according to the embodiments, for example, the first value may be 3. However, the first value is not limited to 3 and may vary depending on the encoder / decoder settings. For example, the first value may be set differently depending on the profile, level, encoding / decoding conditions, memory performance, etc. Therefore, the embodiments may include not only the case where the first value is 3, but also the case where it is set to a value other than 3 depending on the settings according to the embodiments.

[0409] Referring together to FIG. 17 and FIG. 18, the decoding method according to the embodiments includes geometry data included in a slice within a point cloud frame for point cloud data and attribute data included in the slice, and the step of decoding attribute data of point cloud data includes: generating Levels of Detail (LoDs); obtaining predictor candidates for a point in a level of the LoDs; and obtaining a set of predictors for a point based on the predictor candidates, and the step of obtaining predictor candidates for a point may include: obtaining predictor candidates for points included in the point cloud frame; and obtaining predictor candidates for points included in a reference frame for the point cloud frame.

[0410] The decoding method according to the embodiments includes the step of obtaining predictor candidates for points included in a reference frame, the step of obtaining predictor candidates based on blocks that spatially portion a reference slice for a slice within the reference frame; and the step of obtaining predictor candidates based on the finest level of the LoDs of the reference slice, wherein the step of obtaining predictor candidates based on the finest level of the LoDs of the reference slice may be performed when the number of points included in the predictor candidates obtained in the step of obtaining predictor candidates based on blocks is less than 3.

[0411] The decoding method is performed by a decoding device. The decoding device includes a memory; and at least one processor connected to the memory; and the at least one processor may be configured to: decode geometry data of point cloud data within a bitstream; and decode attribute data of point cloud data. The at least one processor may be configured to perform the operations of the aforementioned method.

[0412] The PCC encoding method, PCC decoding method, and signaling method according to the embodiments can provide the following effects.

[0413] Attribute compression of point clouds containing vast amounts of information is time-consuming. In particular, the generation of Lines of Detail (LoD) performed during the compression process involves searching for the nearest neighbor points, which can significantly degrade encoding and decoding speeds. In environments requiring low-latency encoding and decoding, such low speeds can become a problem.

[0414] The embodiments can support a conditional nearest neighbor point search method for high-speed LoD generation that can be utilized in point cloud attribute compression. The embodiments can support a method for increasing the speed of LoD generation by reducing the time required for nearest neighbor point search through adaptive execution of the nearest neighbor point search method according to the characteristics of the point cloud content.

[0415] Accordingly, the transmitting method / device according to the embodiments can transmit data by compressing point cloud data at high speed, and by transmitting signaling information for this purpose, the receiving method / device according to the embodiments can also decode / restore point cloud data at high speed.

[0416] The operation of the transmitting and receiving device according to the embodiments can be described in combination with the point cloud compression processing process described in FIGS. 1 to 10.

[0417] 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.

[0418] 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 claims 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.

[0419] 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.

[0420] 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.”

[0421] 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.

[0422] 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."

[0423] 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.

[0424] 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.

[0425] 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.

[0426]

[0427] As described above, the relevant details have been explained in the best mode for carrying out the embodiments.

[0428]

[0429] As described above, the embodiments may be applied wholly or partially to point cloud data transmission and reception devices and systems.

[0430] Those skilled in the art may make various changes or modifications to the embodiments within the scope of the embodiments.

[0431] The embodiments may include modifications / variations, and such modifications / variations do not exceed the scope of the claims and their equivalents.

Claims

1. 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.

2. In Paragraph 1, The above geometry data is included in a point cloud frame for the above point cloud data, and The above attribute data is included in the above point cloud frame, and The step of decoding attribute data of the above point cloud data is, A step of generating Levels of Detail (LoDs) based on Morton code; A step of obtaining the nearest neighbor points of a point in a level of the LoDs; and The step of obtaining the nearest neighbor points of the above point is, A step of obtaining nearest neighbor points for points included in the above point cloud frame; and A step comprising obtaining nearest neighbor points for points included in a reference frame for the above point cloud frame, Decryption method.

3. In Paragraph 2, The step of obtaining the nearest neighbor points for the points included in the reference frame above is, A step of performing an inter-frame initial search based on a block for the region of the above reference frame; and Based on the above Morton code, including the step of performing an inter-frame extension search, Decryption method.

4. In Paragraph 3, The step of performing the inter-frame extended search is performed when the number of nearest neighbor points obtained in the inter-frame initial search is less than a first value. Decryption method.

5. In Paragraph 4, The above first value is 3, Decryption method.

6. In Paragraph 1, The above geometry data is included in a slice within a point cloud frame of the above point cloud data, and The above attribute data is included in the above slice, and The step of decoding attribute data of the above point cloud data is, Step of generating Levels of Detail (LoDs); A step of obtaining predictor candidates for a point in a level of the LoDs; and Based on the above-mentioned predictor candidates, the method includes the step of obtaining a set of predictors for the above points. The step of obtaining predictor candidates for the above points is, A step of obtaining the predictor candidates for the points included in the above point cloud frame; and A step comprising obtaining the predictor candidates for points included in a reference frame for the above point cloud frame, Decryption method.

7. In Paragraph 6, The step of obtaining the predictor candidates for the points included in the above reference frame is, A step of obtaining the predictor candidates based on blocks that spatially portion the reference slice for the slice within the reference frame; and The method includes the step of obtaining the predictor candidates based on the finest level of the LoDs of the reference slice, and The step of obtaining the predictor candidates based on the finest level of the LoDs of the reference slice is performed when the number of points in the predictor candidates obtained in the step of obtaining the predictor candidates based on the blocks is less than three. Decryption method.

8. 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.

9. 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.

10. In Paragraph 9, The above geometry data is included in a point cloud frame for the above point cloud data, and The above attribute data is included in the above point cloud frame, and The step of encoding the attribute data of the above point cloud data is, A step of generating Levels of Detail (LoDs) based on Morton code; A step of obtaining the nearest neighbor points of a point in a level of the LoDs; and The step of obtaining the nearest neighbor points of the above point is, A step of obtaining the nearest neighbor points for the points included in the point cloud frame; and A step comprising obtaining the nearest neighbor points for points included in a reference frame for the point cloud frame, Encoding method.

11. In Paragraph 10, The step of obtaining the nearest neighbor points for the points included in the reference frame above is, A step of performing an inter-frame initial search based on a block for the region of the above reference frame; and Based on the above Morton code, including the step of performing an inter-frame extension search, Encoding method.

12. In Paragraph 10, The step of performing the inter-frame extended search is performed when the number of nearest neighbor points obtained in the inter-frame initial search is less than three. Encoding method.

13. 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.

14. A computer-readable storage medium for storing a bitstream generated by the method according to paragraph 9.

15. Step for 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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