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: 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 a device (encoder) for encoding point cloud data according to embodiments.

[0022] FIG. 12 shows a device (decoder) for decoding point cloud data according to embodiments.

[0023] FIG. 13 shows a detailed flowchart of the attribute restoration method according to the embodiments.

[0024] FIG. 14 shows a bitstream including point cloud data and parameter information according to embodiments.

[0025] FIG. 15 shows the syntax of an Attribute Parameter Set (APS) according to embodiments.

[0026] FIG. 16 illustrates an encoding method according to embodiments.

[0027] FIG. 17 illustrates a decoding method according to embodiments.

[0028] 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.

[0029] 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.

[0030] FIG. 1 shows an example of a point cloud content provision system according to embodiments.

[0031] 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.

[0032] 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.

[0033] 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.

[0034] 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.

[0035] 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.

[0036] 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.

[0037] 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)).

[0038] 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).

[0039] 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.

[0040] 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.

[0041] 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.

[0042] 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.

[0043] 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.

[0044] 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.

[0045] The elements of the point cloud content delivery system illustrated in FIG. 1 can be implemented in hardware, software, processors, and / or combinations thereof.

[0046] FIG. 2 is a block diagram illustrating a point cloud content provision operation according to embodiments.

[0047] 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).

[0048] 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.).

[0049] 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.

[0050] 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.

[0051] 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.

[0052] 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.

[0053] 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.).

[0054] 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.

[0055] FIG. 3 shows an example of a point cloud encoder according to embodiments.

[0056] 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.

[0057] 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.

[0058]

[0059] * 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, Includes 30011) and / or an Arithmetic Encoder (Arithmetic Encode, 30012).

[0060] 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.

[0061] 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.

[0062] 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.

[0063] 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.

[0064] 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.

[0065] 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.

[0066] 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.

[0067] 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.

[0068] 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).

[0069] 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.

[0070] 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).

[0071] 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.

[0072] As shown in the drawing, the converted attributes are input to the RAHT conversion unit (30008) and / or LOD generation unit (30009).

[0073] 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 sub-level of the octree.

[0074] 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.

[0075] 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.

[0076] The coefficient quantization unit (30011) according to the embodiments quantizes attribute-coded attributes based on coefficients.

[0077] An arismetic encoder (30012) according to the embodiments encodes quantized attributes based on arismetic coding.

[0078] 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).

[0079] FIG. 4 shows examples of octree and occupancy codes according to embodiments.

[0080] 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.

[0081] 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.

[0082] d =Ceil(Log2(Max(x_n^int,y_n^int,z_n^int,n=1,…,N)+1))

[0083] 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.

[0084] 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.

[0085] 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.

[0086] 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).

[0087] 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.

[0088] 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.

[0089] 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.

[0090] 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.

[0091] 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.

[0092]

[0093] 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.

[0094] Table 2-1. Triangles formed from vertices ordered 1,… ,n

[0095] n triangles

[0096] 3 (1,2,3)

[0097] 4 (1,2,3), (3,4,1)

[0098] 5 (1,2,3), (3,4,5), (5,1,3)

[0099] 6 (1,2,3), (3,4,5), (5,6,1), (1,3,5)

[0100] 7 (1,2,3), (3,4,5), (5,6,7), (7,1,3), (3,5,7)

[0101] 8 (1,2,3), (3,4,5), (5,6,7), (7,8,1), (1,3,5), (5,7,1)

[0102] 9 (1,2,3), (3,4,5), (5,6,7), (7,8,9), (9,1,3), (3,5,7), (7,9,3)

[0103] 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)

[0104] 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)

[0105] 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)

[0106] 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).

[0107] Figure 5 shows an example of a point configuration by LOD according to embodiments.

[0108] As described in FIGS. 1 to 4, the encoded geometry is reconstructed (decompressed) before attribute encoding is performed. When direct coding is applied, the geometry reconstruction operation may include changing the arrangement of the direct-coded points (e.g., placing the direct-coded points at the front of the point cloud data). When trisoop geometry encoding is applied, the geometry reconstruction process involves triangle reconstruction, upsampling, and voxelization. Since attributes depend on geometry, attribute encoding is performed based on the reconstructed geometry.

[0109] 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.

[0110] Figure 6 shows an example of a point configuration by LOD according to embodiments.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] graph. Attribute prediction residuals quantization pseudo code

[0117] int PCCQuantization(int value, int quantStep) {

[0118] if( value >=0) {

[0119] return floor(value / quantStep + 1.0 / 3.0);

[0120] } else {

[0121] return -floor(-value / quantStep + 1.0 / 3.0);

[0122] }

[0123] }

[0124] graph. Attribute prediction residuals inverse quantization pseudo code

[0125] int PCCInverseQuantization(int value, int quantStep) {

[0126] if( quantStep ==0) {

[0127] return value;

[0128] } else {

[0129] return value * quantStep;

[0130] }

[0131] }

[0132] 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.

[0133] A point cloud encoder according to the embodiments (e.g., a lifting transformation unit (30010)) can perform lifting transformation coding by generating a predictor for each point, setting the LOD calculated in the predictor, registering neighboring points, and setting weights based on the distance to neighboring points. The lifting transformation coding according to the embodiments is similar to the prediction transformation coding described above, but differs in that weights are cumulatively applied to attribute values. The process of cumulatively applying weights to attribute values ​​according to the embodiments is as follows.

[0134] 1) Create an array QW (QuantizationWift) to store the weight values ​​of each point. The initial value of all elements in QW is 1.0. Add the value obtained by multiplying the current point's predictor weight by the QW value of the predictor index of the neighboring node registered in the predictor.

[0135] 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.

[0136] 3) Create temporary arrays named updateweight and update, and initialize the temporary arrays to 0.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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 voxels to the entire region and repeats the merging process up to the root node, merging 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.

[0141] 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.

[0142]

[0143] 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 (400012)). 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.

[0144]

[0145] The gDC value is also quantized and entropy-coded, just like the high-pass coefficient.

[0146] FIG. 7 shows an example of a point cloud decoder according to embodiments.

[0147] 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.

[0148] As described in Fig. 1, the point cloud decoder can perform geometry decoding and attribute decoding. Geometry decoding is performed before attribute decoding.

[0149] 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).

[0150] An arismetic decoder (7000), an octree synthesis unit (7001), a surface offset synthesis unit (7002), a geometry reconstruction unit (7003), and a coordinate system inverse transformation unit (7004) can perform geometry decoding. Geometry decoding according to the embodiments may include direct coding and trisoup geometry decoding. Direct coding and trisoup geometry decoding are applied optionally. Additionally, geometry decoding is not limited to the above examples and is performed as the reverse process of geometry encoding described in FIGS. 1 through 6.

[0151] 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).

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] The arismetic decoder (7005) according to the embodiments decodes the attribute bitstream into arismetic coding.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] FIG. 8 is an example of a transmission device according to embodiments.

[0163] 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).

[0164] 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).

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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).

[0171] 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).

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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).

[0177] 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 regarding 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 ).

[0178] A slice refers to a series of syntax elements representing all or part of a coded point cloud frame.

[0179] According to the embodiments, the TPS may include information regarding each tile (e.g., coordinate value information of a bounding box and height / size information, etc.) for one or more tiles. The geometry bitstream may include a header and a payload. The header of the geometry bitstream according to the embodiments may include identification information of a parameter set included in the GPS (geom_parameter_set_id), a tile identifier (geom_tile_id), a slice identifier (geom_slice_id), and information regarding data included in the payload, etc. As described above, the metadata processing unit (8007) according to the embodiments may generate and / or process signaling information and transmit it to the transmission processing unit (8012). According to the embodiments, the elements performing geometry encoding and the elements performing attribute encoding may share data / information with each other as indicated by the dotted lines. The transmission processing unit (8012) according to the embodiments may perform an operation and / or transmission method identical or similar to the operation and / or transmission method of the transmitter (10003). A detailed explanation is omitted as it is the same as that described in FIGS. 1 and 2.

[0180] FIG. 9 is an example of a receiving device according to embodiments.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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).

[0185] 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.

[0186] 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).

[0187] 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).

[0188] The inverse quantization processing unit (9005) according to the embodiments can inverse quantize the decoded geometry.

[0189] 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.

[0190] 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.

[0191] 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).

[0192] 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).

[0193] 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.

[0194] FIG. 10 shows an example of a structure that can be linked with a point cloud data transmission / reception method / device according to embodiments.

[0195] 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.

[0196] 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.

[0197] 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).

[0198] 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.

[0199] 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.

[0200] <PCC+XR>

[0201] 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.

[0202] 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.

[0203] <PCC+XR+모바일폰>

[0204] The XR / PCC device (1030) can be implemented as a mobile phone (1040) or the like by applying PCC technology.

[0205] The mobile phone (1040) can decode and display point cloud content based on PCC technology.

[0206] <PCC+자율주행+XR>

[0207] The autonomous vehicle (1020) can be implemented as a mobile robot, vehicle, unmanned aerial vehicle, etc. by applying PCC technology and XR technology.

[0208] 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.

[0209] 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.

[0210] 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.

[0211] 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.

[0212] 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.

[0213] 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.

[0214] The PCC method / device according to the embodiments can be applied to a vehicle providing autonomous driving services.

[0215] Vehicles providing autonomous driving services are connected to PCC devices to enable wired / wireless communication.

[0216] 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.

[0217] The encoding method / device according to the embodiments may include and perform operations such as a transmitting device (10000) of FIG. 1, an acquisition unit (10001), an encoder (10002), a transmitter (10003), acquisition (20000) of FIG. 2, encoding (20001), transmission (20002), encoder of FIG. 3, encoder of FIG. 8, encoder of FIG. 11, generation of bitstream and parameter information (syntax element) of FIG. 14 to 15, encoding of FIG. 16, etc.

[0218] The decoding method / device according to the embodiments may include and perform operations such as a receiving device (10004) of FIG. 1, a receiving unit (10005), a decoder (10006), a renderer (10007), decoding (20003) of FIG. 2, rendering (20004), decoders of FIG. 7 and 9, each device of FIG. 10, decoder of FIG. 12, parsing (acquiring) bitstream and parameter information (syntax element) of FIG. 13, 14 to 15, and decoding of FIG. 17.

[0219] The decoding method / device according to the embodiments may follow the reverse process of the operation of the encoding method / device according to the embodiments.

[0220] The encoding / decoding device according to the embodiments of each drawing may be composed of memory and a processor. The components of each drawing may correspond to hardware, software, a processor, and / or a combination thereof. The encoding / decoding method / device according to the embodiments may be referred to as the method / device for short.

[0221] The method and apparatus according to the embodiments may include and perform an inter-prediction scheme for attributes of Geometry-based Point Cloud Compression (G-PCC) on point cloud frames captured by Spinning LiDAR equipment. The embodiments of the present invention can increase the inter-prediction speed and increase the attribute compression efficiency through a neighbor point set selection scheme used for attribute inter-prediction.

[0222] The method and apparatus according to the embodiments may include, for example, a reference frame configuration method for attribute inter-prediction, a neighbor point set selection method for attribute inter-prediction, and / or a signal method, and can be performed.

[0223] The embodiments may include methods to increase the attribute compression efficiency of G-PCC for 3D point cloud data compression.

[0224] A point cloud consists of a set of points, and each point may have geometry information (geometry data) and attribute information (attribute data). Geometry information is 3D position (XYZ) information, and attribute information may include color (RGB, YUV, etc.) or / and reflection (Reflectance) values.

[0225] The G-PCC encoding process can be composed of compressing geometry and compressing attribute information based on reconstructed geometry (decoded geometry) with location information changed through compression.

[0226] The G-PCC decoding process can be composed of receiving encoded geometry bitstreams and attribute bitstreams, decoding the geometry, and decoding attribute information based on the geometry reconstructed through the decoding process.

[0227] In the process of compressing attribute information, predicting transform, lifting transform, or RAHT techniques can be used.

[0228] In predictive transformation and lifting transformation techniques, points can be divided and grouped by Level of Detail (hereinafter referred to as LOD). This is called the LOD generation process, and hereinafter, groups with different LODs are referred to as LODs. i It can be referred to as a set.

[0229] Here, i represents the LoD and is a zero-based integer. LoD0 is the set of points with the largest distances between them, and as i increases, LoD i The distance between points belonging to becomes smaller (see Figs. 5 and 6). The LoD with the smallest distance between points n Sub-sampling points from to LoD n-1 It can generate. By recursively performing sub-sampling operations down to level 0, LoD nLoDs can be generated that include from LoD0. However, unlike this, LoD0 refers to a set composed of points with the smallest distances between them, LoD1 is generated by sub-sampling points from LoD0, LoD2 is generated by sub-sampling points from LoD1, and the LoD with the largest distances between points is generated by recursively sub-sampling points at each level. n It can generate.

[0230] A LoD can be configured to exist only once; in this case, points can exist within the single LoD in a molten order. Methods for generating LoDs include distance-based LoD generation, decimation-based LoD generation, and octree-based LoD generation. These settings can be configured differently depending on the characteristics of the content or service.

[0231] After creating a set of LoDs, X (>0) nearest neighbor points can be identified from the group with equal or smaller LoDs (large distances between nodes) based on the set, and registered as a set of neighbor points in the predictor. X is the maximum number of neighbor points that can be set and can be received as a user parameter.

[0232] For example, as shown in Fig. 6, neighbor points of P3 belonging to LOD1 can be found in LOD0 and LOD1. The three nearest neighbor nodes can be P2, P4, and P6. These three nodes are registered as a set of neighbor points in the predictor of P3. If only one LOD exists, the three nearest neighbor nodes can be selected from among the points that are sequentially earlier than the current point on the same LOD.

[0233] Every point can have a single predictor. Attributes can be predicted from neighboring points registered with the predictor. The predictor can predict attributes through a set of neighboring points. The residual between the attribute value of a point and the attribute value predicted by the point's predictor can be encoded and signaled to a receiver.

[0234] A method according to embodiments of the present invention includes a method for generating reference frames that can be used between frames for attribute inter-prediction of point cloud frames captured by spinning lidar equipment, and a method for searching for a set of neighboring points.

[0235] In LoD-based attribute compression methods, LoDs are constructed for intra-attribute prediction, but for inter-frame prediction, it is not necessary to construct LoDs; instead, performing inter-frame prediction based on adjacent points can improve compression efficiency. Therefore, it may be important to construct reference frames to quickly search for adjacent points.

[0236] In the embodiments, for attribute inter-prediction for point cloud frames captured by spinning lidar equipment, the reference frame configuration can be changed, and the optimization of attribute prediction can be supported by changing the neighbor point set configuration method. The reference frame configuration scheme and the neighbor point set configuration method can be applied to both the transmitter and the receiver.

[0237] The speed of attribute inter-prediction can be improved and compression efficiency increased through a method of constructing a set of neighbor points from a reference frame according to the embodiments.

[0238] Modifications and combinations between the embodiments are possible. The terms used herein may be understood based on their intended meanings within the scope of their widespread use in the field.

[0239] The embodiments propose a method for constructing a set of neighbor points from a reference frame. The generation of the set of neighbor points is performed during both the encoding and decoding of PCC attributes in the PCC encoder / decoder.

[0240] The method for constructing a set of neighbor points from a reference frame according to the embodiments may include 1) a method for generating a reference frame for coding attribute information, 2) a method for generating a set of neighbor points for predicting attribute inter-predictions, etc. The operation of each step of the flowchart of the method for constructing a set of neighbor points from a reference frame is described below.

[0241] 1) Method for generating reference frames for coding attribute information

[0242] Point cloud frames captured by spinning LiDAR equipment can use a spherical coordinate system, or angular mode, to compress attribute information based on LoD.

[0243] Performing inter-prediction based on adjacent points can improve the compression efficiency of inter-prediction. Therefore, it may be important to configure reference frames to enable rapid searching of adjacent points.

[0244] According to the embodiments, points can be stored by laser index based on a spherical coordinate system, and zones can be defined and stored by dividing the Φ value by the attr_quant value based on the azimuth. Points corresponding to the zones can be downsampled and selected to be stored in a reference frame by storing only up to attr_N points. For example, points within the azimuth can be sampled based on a specific angle to identify points that exist in detail, and points with the same point value can be removed. Subsequently, points can be stored up to attr_N points in order of having the smallest value in the azimuth range, or points can be stored up to attr_N points in order of having the largest value in the azimuth range.

[0245] 2) Method for Generating Neighbor Point Sets for Attribute Interpretation

[0246] According to the embodiments, a set of neighbor points of the current point can be generated from a reference frame generated through 1). The set of neighbor points can be configured with a maximum number of neighbor points in the intra (within the current frame) and a maximum number of neighbor points in the inter (reference frame).

[0247] According to the embodiments, a set of nearest neighbor points can be constructed based on the laser index range (attr_laser_search_range) and the azimuth range (attr_azimuth_search_range). Specifically, according to the embodiments, a set of nearest neighbor points can be searched based on distance among points within the laser index range and the azimuth range. Additionally, the azimuth range may include the end and beginning positions of the azimuth as neighbor search ranges by considering them.

[0248] According to the embodiments, the laser index range (attr_laser_search_range) and the azimuth range (attr_azimuth_search_range) can be transmitted to the decoder.

[0249] According to the embodiments, intra-neighbor points and inter-neighbor points can be selected and signaled in an optimal combination through Rate-Distortion Optimization (RDO).

[0250] FIG. 11 shows a device (encoder) for encoding point cloud data according to embodiments.

[0251] The encoder of FIG. 11 can perform operations corresponding to the encoder of FIG. 1 (10002), the encoding of FIG. 2 (20001), the encoder of FIG. 3, the components for point cloud encoding of the transmitter of FIG. 8, the encoding of FIG. 16, etc.

[0252] FIG. 11 shows a block diagram of a PCC data encoder, and each component may correspond to hardware, software, a processor, and / or a combination thereof. PCC data is fed into the encoder and encoded so that a geometry information bitstream and an attribute information bitstream can be output.

[0253] The data input section can read and configure received data (e.g., ply, configuration file, etc.).

[0254] The coordinate system transformation unit can support coordinate system changes, such as changing the xyz axes or transforming from an xyz orthogonal coordinate system to a spherical coordinate system.

[0255] The geometry information conversion quantization processing unit can adjust the scale by multiplying the geometry position x, y, and z values ​​of the point cloud points by the scale (scale = geometry quantization value) according to the scale setting.

[0256] The spatial partition can be divided into tiles or slices for area-based access or parallel processing of content.

[0257] The voxelization processing unit can support the process of rounding the geometry position values ​​of scaled points to integers.

[0258] The geometry information intra prediction unit can apply geometry intra coding. Intra coding methods may include octree coding, predictive tree coding, trisoup coding, etc.

[0259] The LPU / PU splitting unit can divide the points divided into slices into LPUs / PUs to support inter-prediction when the frame is a P-frame, and find and assign motion vectors corresponding to the split areas.

[0260] The Motion Compensation application unit can generate a predicted point cloud by applying motion vectors to the divided LPU / PU.

[0261] The geometry information inter-prediction unit can perform octree-based inter-coding, predictive-tree-based inter-coding, and trisoup-based inter-coding based on the difference between the current frame and the motion-compensated reference frame.

[0262] The geometry entropy encoding unit can support entropy coding of the results of the geometry information intra / inter prediction unit.

[0263] The color conversion processing unit can support attribute type conversion, such as changing RGB colors to YUV.

[0264] The color recalculation unit can predict an attribute value suitable for the changed location when the geometry is scaled and the location information value is changed.

[0265] The attribute information intra prediction unit can apply attribute information intra coding. Intra coding methods may include the Predicting Transform coding method, Lift Transform coding method, RAHT coding method, etc.

[0266] The attribute information inter-prediction unit may include methods for coding residual values ​​based on the difference in attribute prediction values ​​between the current frame and a motion-compensated reference frame.

[0267] Attribute information entropy encoding can support entropy coding of the results of the attribute information intra / inter prediction section.

[0268] The reference frame generation unit can store the restored geometry and restored attribute information in the reference frame buffer and transfer the reference frame data from the reference frame to other modules.

[0269] The reference frame generation unit can store a reference frame for attribute information inter-prediction in the reference frame buffer.

[0270] The attribute information inter prediction unit can search for a set of neighbor points for inter prediction based on a reference frame generated by the reference frame generation unit. The search method may follow 2) the method for generating a set of neighbor points for attribute inter prediction. The laser index range (attr_laser_search_range) and azimuth range (attr_azimuth_search_range) can be signaled to the receiver.

[0271] The attribute information intra-prediction unit can search for a set of neighboring points based on LoD.

[0272] Predicted attribute values ​​can be generated by selecting a combination of points with a low bit rate and low distortion via RDO from the intra-searched set of neighbor points and the inter-searched set of neighbor points, and the residual value with respect to the current attribute value can be signaled.

[0273] FIG. 12 shows a device (decoder) for decoding point cloud data according to embodiments.

[0274] The decoder of FIG. 12 can perform operations corresponding to the decoder of FIG. 1 (10006), the decoding of FIG. 2 (20003), the decoder of FIG. 7, the components for point cloud decoding of the receiver of FIG. 9, the decoding of FIG. 17, etc.

[0275] FIG. 12 shows a block diagram of a PCC data decoder, and 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 inputs to the decoder, and PCC data that has been decoded and restored can be output.

[0276] The geometry entropy decoding unit can entropy decode the geometry bitstream.

[0277] The geometry information intra prediction restoration unit can restore predicted values ​​predicted by geometry intra coding. Intra coding methods may include octree coding, predictive-tree coding, trisoup coding methods, etc.

[0278] The LPU / PU splitting unit can split the reference frame into LPU / PU by restoring the region value signaled for the LPU / PU splitting indication to support inter-prediction when the frame is a P-frame.

[0279] The Motion Compensation application unit can generate a predicted point cloud by applying motion vectors to the divided LPU / PU.

[0280] The geometry information inter-prediction restoration unit can restore predicted values ​​predicted by geometry inter-coding. Inter-coding methods may include octree-based inter-coding, predictive-tree-based inter-coding, and trisoup-based inter-coding methods.

[0281] 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.

[0282] The geometry information conversion inverse quantization processing unit can restore the signaled scale (scale = geometry quantization value) and apply it to the geometry position x, y, and z values ​​of the restored point to restore it.

[0283] Attribute residual information entropy decoding can entropy decode the attribute bitstream.

[0284] The attribute information intra prediction restoration unit can restore predicted values ​​predicted by attribute information intra coding. Intra coding methods may include the Predicting Transform coding method, Lift Transform coding method, RAHT coding method, etc.

[0285] The attribute information inter-prediction restoration unit can restore predicted values ​​predicted by attribute information inter-coding.

[0286] The color inverse conversion processing unit can restore the converted attribute to RGB color.

[0287] The reference frame generation unit can store the restored geometry and restored attribute information in the reference frame buffer and transfer the reference frame data from the reference frame to other modules.

[0288] The reference frame generation unit may store a reference frame for attribute information inter-prediction in a reference frame buffer. The reference frame may be generated before the geometry and / or attribute decoding process for the current frame. For example, the reference frame may be generated after the geometry decoding process for a frame restored prior to the current frame is performed, and / or after the attribute decoding process is performed.

[0289] The attribute information inter-prediction unit can search for a set of neighbor points for inter-prediction based on a reference frame generated by the reference frame generation unit. The search method may follow 2) the method for generating a set of neighbor points for attribute inter-prediction. The laser index range (attr_laser_search_range) and azimuth range (attr_azimuth_search_range) can be applied when searching for neighbor points by restoring the signaled values ​​from the transmitter.

[0290] The attribute information intra-prediction unit can search for a set of neighboring points based on LoD.

[0291] By constructing sets of intra-searched and inter-searched neighbor points, attribute prediction values ​​can be generated using information that allows selecting neighbor points signaled from the transmitter. Attribute values ​​can be restored by adding the residual values ​​signaled from the transmitter and the attribute prediction values.

[0292] FIG. 13 shows a detailed flowchart of the attribute restoration method according to the embodiments.

[0293] When the encoder and / or decoder according to the embodiments encodes or decodes attribute data based on a reference frame, it may generate a reference frame for coding attribute information. For example, in the case of an angle mode, points may be downsampled and divided into up to attr_N points based on laser_index and attr_quant, and the points may be stored in the reference frame. The generated reference frame may be stored in a reference frame buffer.

[0294] The encoder and / or decoder according to the embodiments can perform inter-neighbor point search when attribute inter-coding is applied. For example, in the case of an angle mode, a search area can be found from a reference frame based on laser_search_range and azimuth_quant_range, and N points closest by distance among the points within the search area can be set as the inter-searched Nearest Neighbors (NN).

[0295] The encoder and / or decoder according to the embodiments can perform intra-neighbor point search. For example, according to the LoD-based neighbor point search method, the N points closest by distance can be set as the intra-searched NNs.

[0296] The encoder and / or decoder according to the embodiments can perform attribute prediction. For example, selected inter / intra-neighbor points can be selected through RDO to select points predicted to have the lowest bit stream rate and the smallest distortion.

[0297] The encoder according to the embodiment can signal residual values ​​based on predicted attribute values. For example,

[0298] The decoder according to the embodiments can restore attribute values ​​based on predicted attribute values. For example, attribute values ​​can be restored by adding predicted attribute values ​​to residual values ​​signaled from a transmitter.

[0299] FIG. 14 shows a bitstream including point cloud data and parameter information according to embodiments.

[0300] An encoder such as Fig. 11 can encode point cloud data and generate related parameter information (syntax information or syntax elements) to generate a bitstream such as Fig. 14.

[0301] A decoder such as Fig. 12 can decode point cloud data within a bitstream based on relevant parameter information (syntax information or syntax elements) within the bitstream.

[0302] Relevant information may be signaled to add / perform embodiments. The signaling information according to the embodiments may be used at a transmitting end or a receiving end, etc.

[0303] The encoded point cloud configuration is as follows. A point cloud data encoder that performs geometry encoding and / or attribute encoding processes can generate an encoded point cloud (or a bitstream containing a point cloud) as follows. Additionally, signaling information regarding point cloud data can be generated and processed by a metadata processing unit of a point cloud data transmitting device and included in the point cloud as follows.

[0304] Each abbreviation means the following. Each abbreviation may be referred to by other terms within the scope of equivalent meaning: SPS(Sequence Parameter Set), GPS(Geometry Parameter Set), APS(Attribute Parameter Set), TPS(Tile Parameter Set), Geom(Geometry bitstream = geometry slice header+ [geometry PU header + Geometry PU data] | geometry slice data), Attr(Attribute bitstream = attribute data unit header + [attribute PU header + attribute PU data] | attribute data unit data).

[0305] During the attribute information encoding / decoding process, relevant information for the attribute inter-prediction function for the sequence can be added to the SPS or APS and signaled.

[0306] If tiles or slices with different attribute features exist within the same sequence, relevant information for the attribute inter-prediction function for the sequence can be added to the TPS or Attr for each slice and signaled.

[0307] - Provides tiles or slices to divide and process point clouds by region.

[0308] - When dividing by region, different neighbor point set generation options can be set for each region to provide a choice that offers low complexity but slightly lower reliability of results, or conversely, high complexity but high reliability. These settings can be configured differently depending on the receiver's capacity.

[0309] - When dividing by region, the attribute characteristics of a specific region may differ from the attribute characteristics of the sequence, so they can be set differently.

[0310] Therefore, when a point cloud is divided into tiles, a different maximum range of neighboring points can be applied to each tile.

[0311] - When a point cloud is divided into slices, a different maximum range of neighboring points can be applied to each slice.

[0312] PU-related information for the attribute inter-prediction function can be added to the Attribute PU header.

[0313] In the following, signaling information (which may be referred to as parameter information, syntax elements, etc.) included in the bitstream of FIG. 14 is described.

[0314] FIG. 15 shows the syntax of an Attribute Parameter Set (APS) according to embodiments.

[0315] In the attribute information encoding / decoding process according to the embodiments, information related to the attribute inter-prediction function can be added to the APS (Attribute Parameter Set) and signaled.

[0316] Attribute parameter set ID (aps_attr_parameter_set_id): Provides an identifier for APS so that other syntax elements can reference it. The value of aps_attr_parameter_set_id must be within the range of 0 to 15 (inclusive).

[0317] Sequence parameter set ID (aps_seq_parameter_set_id): Specifies the value of seq_parameter_set_id for the active SPS. The value of aps_seq_parameter_set_id must be within the range of 0 to 15 (inclusive).

[0318] Attribute Coding Type (attr_coding_type): Specifies the attribute coding method. For example, 0 = Region Adaptive Hierarchical Transform (RAHT), 1 = LoD with Predicting Transform, 2 = LoD with Lifting Transform, 3 = Raw attribute data

[0319] APS extension present (aps_extension_present): Specifies whether the aps_extension_data syntax elements exist in the APS syntax structure. In bitstreams conforming to the current version (DIS stage) of the G-PCC standard document (ISO / IEC 23090-38), the value of aps_extension_present must be 0. A value of 1 for aps_extension_present is reserved for future use by ISO / IEC.

[0320] Interprediction Enable (attr_inter_prediction_enabled): Specifies whether interprediction can be used to code attributes of a point cloud (when the value is 1) or not (when the value is 0). If attr_inter_prediction_enabled does not exist, its value should be assumed to be 0.

[0321] Inter-prediction search range (attr_inter_prediction_search_range): Specifies a range of indices in the detail-level refinement list within the reference slice to search for nearest neighbors to include in a point's predictor set. If attr_inter_prediction_search_range does not exist, its value should be assumed to be 0.

[0322] Spherical coordinate-based reference frame buffer enable (attr_sc_reference_frame_buffer_enabled): Specifies whether to use a spherical coordinate system-based reference frame buffer when the inter prediction (attr_inter_prediction_enabled) applied to the frame is 1. When geometry compression is applied, if predictive tree geometry coding is applied, the spherical coordinate system-based reference frame buffer used for inter prediction is used as is, and if occupancy tree geometry coding is applied, the spherical coordinate system-based reference frame buffer can be created as in the reference frame creation method for attribute information coding 1) above.

[0323] LoD generation disabled (attr_lod_generation_disabled): Specifies whether to generate LoD when compressing inter-attributes applied to a frame. If the value of attr_lod_generation_disabled is 1, NN (Nearest Neighbours) search can be performed from a reference frame buffer based on a spherical coordinate system. The search method is the same as the neighbor point set generation method for attribute inter-prediction in 2) above.

[0324] Laser search range (attr_laser_search_range): Specifies the laser index search range when searching for neighboring points from the attribute reference frame configured when attr_lod_generation_disabled = true(1).

[0325] Azimuth search range (attr_azimuth_search_range): Specifies the azimuth search range when searching for neighboring points from attribute reference frames configured when attr_lod_generation_disabled = true(1).

[0326] APS extension data (aps_extension_data): Can have any value. Its presence and value do not affect the decoder's conformance to the profiles specified in the current version (DIS stage) of the G-PCC standard document (ISO / IEC 23090-38). The decoder must ignore all aps_extension_data syntax elements.

[0327] FIG. 16 illustrates an encoding method according to embodiments.

[0328] The encoding method according to the embodiments may include the step of encoding geometry data of point cloud data (S1600); and / or the step of encoding attribute data of point cloud data (S1610). The step of encoding geometry data (S1600) and / or the step of encoding attribute data (S1610) may include the encoding operation of point cloud data described in FIGS. 1 to 15.

[0329] Referring together with FIG. 11, the encoding method according to the embodiments further includes the step of generating a reference frame for a point cloud frame of point cloud data, and the step of encoding attribute data may include the step of predicting attribute data for a point cloud frame based on the reference frame and the step of restoring attribute values ​​of a point cloud frame based on the predicted attribute data.

[0330] Referring together with Fig. 13, the attribute data of the point cloud data is encoded based on the angle mode, and the reference frame can be generated based on the laser index and azimuth.

[0331] Referring together with FIG. 13, the step of predicting attribute data for a point cloud frame includes the step of searching for neighbor points of a reference frame for a point in the point cloud frame, and neighbor points can be searched based on distances to points within a laser index range and an azimuth range.

[0332] Referring together to FIGS. 14 and 15, encoded geometry data and encoded attribute data are included in a bitstream, and the bitstream includes at least one of information regarding whether to use a reference frame buffer based on a spherical coordinate system; or information to disable LoD generation for attribute data, and based on a first value of the information to disable LoD generation for attribute data, a nearest neighbor point is searched from the reference frame buffer based on the spherical coordinate system, and the bitstream may further include at least one of information indicating a laser index search range for searching neighbor points or an azimuth search range for searching neighbor points based on the first value of the information to disable LoD generation for attribute data.

[0333] 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.

[0334] The embodiments further include a computer-readable storage medium that stores a bitstream generated by the method according to FIG. 16.

[0335] 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.

[0336] FIG. 17 illustrates a decoding method according to embodiments.

[0337] The decoding method according to the embodiments may include the step of decoding geometry data of point cloud data within a bitstream (S1700); and / or the step of decoding attribute data of point cloud data (S1710).

[0338] Referring together with FIG. 12, the decoding method according to the embodiments further includes the step of generating a reference frame for a point cloud frame of point cloud data, and the step of decoding attribute data may include the step of predicting attribute data for the point cloud frame based on the reference frame, and the step of restoring attribute values ​​of the point cloud frame based on the predicted attribute data.

[0339] Referring to Fig. 13, the attribute data of the point cloud data is decoded based on the angle mode, and the subtraction frame can be generated based on the laser index and azimuth.

[0340] Referring together with FIG. 13, the step of predicting attribute data for a point cloud frame includes the step of searching for nearest neighbor points of a reference frame for a point (a point) of the point cloud frame, and said neighbor points can be searched based on distances to points within a laser index range and an azimuth range.

[0341] Referring together with FIG. 13, the step of decoding attribute data further includes the step of intra-predicting attribute data for a point cloud frame, and the step of intra-predicting attribute data for a point cloud frame may include the step of searching for nearest neighbor points of a point cloud frame for the point of the point cloud frame based on Levels of Detail (LoDs).

[0342] Referring to Fig. 13, the attribute data of a point can be restored based on neighbor points retrieved in the inter-prediction stage and neighbor points retrieved in the intra-prediction stage.

[0343] Referring to FIGS. 14 and 15 together, the bitstream may include information about the laser index range and information about the azimuth range.

[0344] Referring together to FIGS. 14 and 15, the bitstream includes at least one of information regarding whether to use a reference frame buffer based on a spherical coordinate system; or information to disable LoD generation for attribute data, and based on a first value of the information to disable LoD generation for attribute data, a nearest neighbor point is searched from a reference frame buffer based on a spherical coordinate system, and the bitstream may further include at least one of information indicating a laser index search range for searching for a neighbor point or information indicating an azimuth search range for searching for a neighbor point based on the first value of the information to disable LoD generation for attribute data.

[0345] 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.

[0346] The PCC encoding method, PCC decoding method, and signaling method of the above-described embodiments can provide the following effects.

[0347] Interpretive coding is a necessary technique for the efficient compression of multi-frame point cloud data. Rather than compressing every frame using independent interpretive coding, analyzing the redundancy and movement of content within the frames over time and compressing accordingly can reduce the bitstream size.

[0348] To perform attribute inter-prediction in the G-PCC attribute encoding / decoding process of the above-described embodiments, a neighbor point set selection method and an attribute inter-prediction method using the same PU-specific attribute change vector were provided.

[0349] The embodiments can provide increased attribute compression efficiency by not only performing LoD configuration in the encoder / decoder of the G-PCC for 3D point cloud data compression, but also by finding an optimal prediction value through RDO without performing LoD configuration and finding a set of neighboring points from each other.

[0350] Due to the method for configuring a neighbor point set of a point cloud data encoder (e.g., a neighbor point set generation unit) and the signaling method for doing so according to the embodiments described above, the point cloud data transmission / reception method / device according to the embodiments can have the effect of increasing attribute compression / decompression efficiency with an optimal neighbor point set.

[0351] Thus, in these embodiments, an efficient attribute bitstream can be provided for inter-predictive coding of the encoder / decoder of the G-PCC for 3D point cloud data compression, thereby providing the effect of efficiently encoding / decoding point cloud data.

[0352] Accordingly, as described above, based on the attribute inter-prediction method and / or related signaling information according to the embodiments, the method / device according to the embodiments can efficiently compress data contained in a frame over time to reduce the size of the bitstream. In addition, the method / device according to the embodiments can quickly and accurately restore point cloud data and provide it to the user.

[0353] 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 herein. The processor may be referred to as a controller, etc. Operations according to 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.

[0354] 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.

[0355] 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.

[0356] 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.

[0357] 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.”

[0358] 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.

[0359] 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."

[0360] 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.

[0361] 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.

[0362] 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.

[0363] As described above, the relevant details have been explained in the best mode for carrying out the embodiments.

[0364] As described above, the embodiments can be applied wholly or partially to point cloud data transmission and reception devices and systems.

[0365] Those skilled in the art may make various changes or modifications to the embodiments within the scope of the embodiments.

[0366] 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 method is, The method further includes the step of generating a reference frame for a point cloud frame of the above point cloud data, The step of decoding the above attribute data is, A step of inter-predicting attribute data for the point cloud frame based on the above reference frame; and A method comprising the step of restoring attribute values ​​of the point cloud frame based on the predicted attribute data above. Decryption method.

3. In Paragraph 2, The attribute data of the above point cloud data is decoded based on an angle mode, and The above-mentioned submerged frame is generated based on a laser index and azimuth, Decryption method.

4. In Paragraph 3, The step of predicting attribute data for the above point cloud frame is, The method includes the step of searching for the nearest neighbor points of the reference frame for a point (a point) of the point cloud frame, and The above neighbor points are searched based on the distance to points within the laser index range and azimuth range, Decryption method.

5. In Paragraph 4, The step of decoding the above attribute data is, The method further includes a step of intra-predicting attribute data for the above point cloud frame, and The step of intra-predicting attribute data for the above point cloud frame is, A method comprising the step of searching for the nearest neighbor points of a point cloud frame for the point of the point cloud frame based on Levels of Detail (LoDs). Decryption method.

6. In Paragraph 5, The attribute data of the above point is, Restored based on the neighbor points retrieved in the inter-prediction step and the neighbor points retrieved in the intra-prediction step, Decryption method.

7. In Paragraph 4, The above bitstream includes information regarding the laser index range and information regarding the azimuth range, Decryption method.

8. In Paragraph 1, The above bitstream is, Information on whether to use a reference frame buffer based on a spherical coordinate system; or It includes at least one of the LoD generation disable information for the above attribute data, and Based on the first value of the LoD generation disable information for the above attribute data, the nearest neighbor point is searched from the reference frame buffer based on the above spherical coordinate system, and The bitstream further comprises at least one of information indicating a laser index search range for searching the neighbor point or information indicating an azimuth search range for searching the neighbor point, based on a first value of LoD generation disable information for the attribute data. Decryption method.

9. 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.

10. 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.

11. In Paragraph 9, The above method is, The method further includes the step of generating a reference frame for a point cloud frame of the above point cloud data, The step of encoding the above attribute data is, A step of inter-predicting attribute data for the point cloud frame based on the above reference frame; and A method comprising the step of restoring attribute values ​​of the point cloud frame based on the predicted attribute data above. Encoding method.

12. In Paragraph 10, The attribute data of the above point cloud data is encoded based on an angle mode, and The above-mentioned submerged frame is generated based on a laser index and azimuth, Encoding method.

13. In Paragraph 11, The step of predicting attribute data for the above point cloud frame is, The method includes the step of searching for the nearest neighbor points of the reference frame for a point (a point) of the point cloud frame, and The above neighbor points are searched based on the distance to points within the laser index range and azimuth range, Encoding method.

14. In Paragraph 10, The above-mentioned encoded geometry data and the above-mentioned encoded attribute data are included in a bitstream, and The above bitstream is, Information on whether to use a reference frame buffer based on a spherical coordinate system; or It includes at least one of the LoD generation disable information for the above attribute data, and Based on the first value of the LoD generation disable information for the above attribute data, the nearest neighbor point is searched from the reference frame buffer based on the above spherical coordinate system, and The bitstream further comprises at least one of information indicating a laser index search range for searching the neighbor point or information indicating an azimuth search range for searching the neighbor point, based on a first value of LoD generation disable information for the attribute data. Encoding method.

15. 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; Decoding device.

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

17. 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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