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

Abstract

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

Description

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

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

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

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

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

[0005] To achieve the above-described purpose and other advantages, the method according to the embodiments may include the step of decoding geometry data of point cloud data within a bitstream; and the step of decoding attribute data of the point cloud data.

[0006] A method according to the embodiments may include the step of encoding geometry data of point cloud data; and the step of encoding attribute data of the point cloud data.

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

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

[0009] The device and method according to the embodiments can provide point cloud content for providing general-purpose services such as VR services and autonomous driving services.

[0010] Drawings are included to further understand the embodiments, and the drawings illustrate the embodiments along with descriptions related to the embodiments. For a better understanding of the various embodiments described below, one must refer to the description of the embodiments below in relation to the following drawings, which include parts corresponding to similar reference numerals throughout the drawings.

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

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

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

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

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

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

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

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

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

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

[0021] FIG. 11 shows an encoder block diagram according to embodiments.

[0022] FIG. 12 shows a block diagram of an attribute information encoding unit according to embodiments.

[0023] FIG. 13 illustrates the encoding process of the lifting conversion unit according to the embodiments.

[0024] FIG. 14 shows a block diagram of a decoder according to embodiments.

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

[0026] FIG. 16 illustrates the decoding process of the lifting inverse conversion unit according to the embodiments.

[0027] FIG. 17 shows a bitstream structure according to embodiments.

[0028] FIG. 18 shows a set of sequence parameters according to the embodiments.

[0029] FIG. 19 shows a set of attribute parameters according to the embodiments.

[0030] FIG. 20 shows a set of attribute parameters according to embodiments.

[0031] FIG. 21 shows an attribute data header according to embodiments.

[0032] FIG. 22 shows an attribute data header according to embodiments.

[0033] FIG. 23 illustrates an encoding method according to embodiments.

[0034] FIG. 24 illustrates a decoding method according to embodiments.

[0035] Preferred embodiments of the embodiments are described in detail, and examples thereof are shown in the accompanying drawings. The detailed description below, 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 can be practiced without these details.

[0036] 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.

[0037] FIG. 1 shows an example of a point cloud content provision system according to embodiments.

[0038] 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.

[0039] 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.

[0040] 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.

[0041] 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.

[0042] 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.

[0043] 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.

[0044] 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)).

[0045] 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 decapsulate the received file / segment and output a bitstream. 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).

[0046] 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.

[0047] 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.

[0048] 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.

[0049] 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.

[0050] 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.

[0051] 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.

[0052] The elements of the point cloud content delivery system illustrated in FIG. 1 can be implemented in hardware, software, processors, and / or combinations thereof.

[0053] FIG. 2 is a block diagram illustrating a point cloud content provision operation according to embodiments.

[0054] 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).

[0055] 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.).

[0056] A point cloud content providing system (e.g., a transmission device (10000) or a point cloud video encoder (10002)) according to embodiments 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.

[0057] 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.

[0058] 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.

[0059] 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.

[0060] 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.).

[0061] 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.

[0062] FIG. 3 shows an example of a point cloud encoder according to embodiments.

[0063] 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.

[0064] 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.

[0065] The point cloud encoder according to the embodiments comprises a coordinate system transformation unit (Transformation Coordinates, 30000), a quantization unit (Quantize and Remove Points (Voxelize), 30001), an octree analysis unit (Analyze Octree, 30002), a surface approximation analysis unit (Analyze Surface Approximation, 30003), an arithmetic encoder (Arithmetic Encode, 30004), a geometry reconstruction unit (Reconstruct Geometry, 30005), a color transformation unit (Transform Colors, 30006), an attribute transformation unit (Transfer Attributes, 30007), a RAHT transformation unit (30008), an LOD generation unit (Generated LOD, 30009), a lifting transformation unit (Lifting) (30010), and a coefficient quantization unit (Quantize Coefficients, 30011). Includes an and / or arithmetic encoder (Arithmetic Encode, 30012).

[0066] 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.

[0067] 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.

[0068] 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.

[0069] 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.

[0070] 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.

[0071] 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.

[0072] 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.

[0073] 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.

[0074] 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).

[0075] 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.

[0076] 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).

[0077] 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.

[0078] As shown in the drawing, the converted attributes are input to the RAHT conversion unit (30008) and / or LOD generation unit (30009).

[0079] The RAHT transformation unit (30008) according to the embodiments performs RAHT coding to predict attribute information based on reconstructed geometry information. For example, the RAHT transformation unit (30008) can predict attribute information of a node at an upper level of the octree based on attribute information associated with a node at a lower level of the octree.

[0080] 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.

[0081] 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.

[0082] The coefficient quantization unit (30011) according to the embodiments quantizes attribute-coded attributes based on coefficients.

[0083] An arismetic encoder (30012) according to the embodiments encodes quantized attributes based on arismetic coding.

[0084] 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).

[0085] FIG. 4 shows examples of octree and occupancy codes according to embodiments.

[0086] 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.

[0087] 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.

[0088]

[0089] 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.

[0090] 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.

[0091] 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.

[0092] 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).

[0093] 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.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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.

[0098]

[0099] 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.

[0100] Table 2-1. Triangles formed from vertices ordered 1,… ,n

[0101] n triangles

[0102] 3 (1,2,3)

[0103] 4 (1,2,3), (3,4,1)

[0104] 5 (1,2,3), (3,4,5), (5,1,3)

[0105] 6 (1,2,3), (3,4,5), (5,6,1), (1,3,5)

[0106] 7 (1,2,3), (3,4,5), (5,6,7), (7,1,3), (3,5,7)

[0107] 8 (1,2,3), (3,4,5), (5,6,7), (7,8,1), (1,3,5), (5,7,1)

[0108] 9 (1,2,3), (3,4,5), (5,6,7), (7,8,9), (9,1,3), (3,5,7), (7,9,3)

[0109] 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)

[0110] 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)

[0111] 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)

[0112] 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).

[0113] Figure 5 shows an example of a point configuration by LOD according to embodiments.

[0114] 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.

[0115] 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.

[0116] Figure 6 shows an example of a point configuration by LOD according to embodiments.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] graph. Attribute prediction residuals quantization pseudo code

[0123] int PCCQuantization(int value, int quantStep) {

[0124] if( value >=0) {

[0125] return floor(value / quantStep + 1.0 / 3.0);

[0126] } else {

[0127] return -floor(-value / quantStep + 1.0 / 3.0);

[0128] }

[0129] }

[0130] graph. Attribute prediction residuals inverse quantization pseudo code

[0131] int PCCInverseQuantization(int value, int quantStep) {

[0132] if( quantStep ==0) {

[0133] return value;

[0134] } else {

[0135] return value * quantStep;

[0136] }

[0137] }

[0138] 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.

[0139] 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.

[0140] 1) Create an array QW (QuantizationWight) to store the weight values ​​of each point. The initial value of all elements in QW is 1.0. Add the value obtained by multiplying the current point's predictor weight by the QW value of the predictor index of the neighboring node registered in the predictor.

[0141] 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.

[0142] 3) Create temporary arrays named updateweight and update, and initialize the temporary arrays to 0.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148]

[0149] gl-1 x, y, z are low-pass values ​​used in the merging process at the next higher level. hl-1 x, y, z are high-pass coefficients, and the high-pass coefficients at each step are quantized and entropy-coded (e.g., encoding of an arismetic encoder (300012)). The weights are calculated as wl-1 x, y, z = wl 2x, y, z + wl 2x + 1, y, z. The root node is the last g 1 0, 0, 0 and g 1 0, 0, 1 It is generated as follows through.

[0150]

[0151] The gDC value is also quantized and entropy-coded, just like the high-pass coefficient.

[0152] FIG. 7 shows an example of a point cloud decoder according to embodiments.

[0153] 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.

[0154] As described in Fig. 1, the point cloud decoder can perform geometry decoding and attribute decoding. Geometry decoding is performed before attribute decoding.

[0155] 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).

[0156] An arismetic decoder (7000), an octree composite unit (7001), a surface offset composite unit (7002), a geometry reconstruction unit (7003), and a coordinate system inverse transformation unit (7004) can perform geometry decoding. Geometry decoding according to the embodiments may include direct coding and trisoup geometry decoding. Direct coding and trisoup geometry decoding are applied optionally. Additionally, geometry decoding is not limited to the above examples and is performed as the reverse process of geometry encoding described in FIGS. 1 through 6.

[0157] 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).

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] The arismetic decoder (7005) according to the embodiments decodes the attribute bitstream into arismetic coding.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] FIG. 8 is an example of a transmission device according to embodiments.

[0169] 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).

[0170] 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).

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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).

[0177] 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).

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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).

[0183] 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 ).

[0184] A slice refers to a series of syntax elements representing all or part of a coded point cloud frame.

[0185] 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.

[0186] FIG. 9 is an example of a receiving device according to embodiments.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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).

[0191] 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.

[0192] 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).

[0193] 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).

[0194] The inverse quantization processing unit (9005) according to the embodiments can inverse quantize the decoded geometry.

[0195] 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.

[0196] 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.

[0197] 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).

[0198] 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).

[0199] 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.

[0200] FIG. 10 shows an example of a structure that can be linked with a point cloud data transmission / reception method / device according to embodiments.

[0201] 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.

[0202] 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.

[0203] 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).

[0204] 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.

[0205] 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.

[0206] <PCC+XR>

[0207] 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.

[0208] 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.

[0209] <PCC+XR+모바일폰>

[0210] The XR / PCC device (1030) can be implemented as a mobile phone (1040) or the like by applying PCC technology.

[0211] The mobile phone (1040) can decode and display point cloud content based on PCC technology.

[0212] <PCC+자율주행+XR>

[0213] The autonomous vehicle (1020) can be implemented as a mobile robot, vehicle, unmanned aerial vehicle, etc. by applying PCC technology and XR technology.

[0214] 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.

[0215] 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.

[0216] 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.

[0217] 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.

[0218] 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.

[0219] 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.

[0220] The PCC method / device according to the embodiments can be applied to a vehicle providing autonomous driving services.

[0221] Vehicles providing autonomous driving services are connected to PCC devices to enable wired / wireless communication.

[0222] 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.

[0223] A point cloud transmission method / device (or encoding method and device) according to embodiments may include, and can be performed, a transmission device (10000) of FIG. 1, a point cloud video encoder (10002), a transmitter (10003), an acquisition-encoding-transmission (20000-20001-20002) of FIG. 2, a transmission device of FIG. 8, a device of FIG. 10, an encoding process of FIG. 11 to FIG. 13, a bitstream and parameter information (syntax element) generation of FIG. 17 to FIG. 22, an encoding method of FIG. 23, etc.

[0224] A point cloud receiving method / device (or decoding method and device) according to embodiments may include, and can perform the receiving device (10004) of FIG. 1, a receiver (10005), a point cloud video decoder (10006), the transmission-decoding-rendering (20002-20003-20004) of FIG. 2, the decoder of FIG. 7, the receiving device of FIG. 9, the device of FIG. 10, the decoding process of FIG. 14 to 16, the acquisition of bitstream and parameter information (syntax elements) of FIG. 17 to 22, the decoding method of FIG. 24, etc.

[0225] Additionally, the point cloud data transmission / reception method / device (or encoding / decoding method / device) according to the embodiments may be referred to simply as the method / device according to the embodiments.

[0226] According to the embodiments, geometry data, geometry information, location information, etc., constituting point cloud data are interpreted as having the same meaning. Attribute data, attribute information, attribute information, etc., constituting point cloud data are interpreted as having the same meaning. In addition, encoding can be interpreted as encoding, and the encoding unit or encoder can be interpreted as an encoder, and decoding can be interpreted as decoding, and the decoding unit or decoder can be interpreted as a decoder.

[0227] The contents of this specification may be understood based on G-PCC standard specification documents, including ISO / IEC 23090-38, which were disclosed at the time of the priority date or filing date of this application.

[0228] Embodiments according to the present invention may include a method of omitting the encoding / decoding of lifting transform coefficients for chrominance components for some points by reflecting human cognitive perception characteristics in order to increase the compression efficiency of geometry-based point cloud compression (G-PCC) of a point cloud frame. Specifically, the embodiments may include a chroma subsampling method and a signaling method.

[0229] Embodiments according to the present invention may include a method for increasing the compression efficiency of Enhanced Geometry-based Point Cloud Compression (E-GPCC) for three-dimensional point cloud data compression.

[0230] A point cloud consists of a set of points, and each point can have geometry information and attribute information. Geometry information is 3D position (XYZ) information, and attribute information is color (RGB, YUV, etc.) or reflection values.

[0231] The G-PCC encoding process can divide the point cloud into tiles and divide each tile into slices for parallel processing. It can be composed of a process of compressing geometry on a slice-by-slice basis and compressing attribute information based on reconstructed geometry (or decoded geometry) with location information changed through compression.

[0232] The G-PCC decoding process can be composed of receiving a geometry bitstream and an attribute bitstream in the form of encoded slice units, decoding the geometry, and decoding attribute information based on the geometry reconstructed through the decoding process.

[0233] Octree-based, predictive tree-based, or trisoup-based compression techniques can be used to compress geometry information.

[0234] For attribute information compression, predicting transform-based, lifting transform-based, or RAHT transform-based compression techniques can be used.

[0235] In predictive transformation and lifting transformation techniques, points can be grouped by dividing them into Levels of Detail (hereinafter referred to as LoD).

[0236] Every point can have a single predictor. The attributes of the current point can be predicted from neighboring points registered in the predictor. As a prediction method, the Inverse Distance Weighting (IDW) technique can be used, which applies weights calculated based on the Euclidean distance between neighboring points and the current point.

[0237] In a Predicting Transform or Lifting Transform, a predictor can be used to predict the attribute value of the current point. The predicted result can be the average of the weighted attribute values ​​of neighboring points belonging to the predictor, or the attribute value of a specific point belonging to the predictor can be used. The choice of method depends on pre-calculating the compressed result in the encoder and selecting the method that generates the smallest stream.

[0238] In the case of lifting transformations, an update process can be performed following the prediction process. The update process removes high-frequency components from points in higher-level LoDs (levels coarser than the current LoD) using residual values ​​calculated through the prediction process; consequently, the points in higher-level LoDs may consist of low-frequency components. The prediction-update process can be performed sequentially along the hierarchy of LoDs (levels of detail).

[0239] The residual between the original attribute value of the current point and the attribute value predicted by the point's predictor can be encoded along with the prediction method selected by the predictor and signaled to the receiver.

[0240] The decoder can decode the prediction method and predict the attribute value according to that method. Additionally, it can recover the attribute value by decoding the residual value and adding the predicted value.

[0241] In conventional point cloud compression, a single point has a luminance (luma, Y) component and a color difference (chroma, Cb or Cr) component, and since the unit of all compression is a point, the luminance and color difference components are encoded and decoded together. Considering the cognitive perceptual characteristics of people who have poor ability to perceive color difference components, the color difference component can be viewed as a signal with a lot of spatial redundancy.

[0242] To eliminate such spatial redundancy, the embodiments may group points and omit the coding of lifting transformation coefficients of color difference components for specific groups. By omitting the lifting transformation or coefficient coding of color difference components for specific groups, the embodiments can reflect human cognitive perception characteristics while simultaneously improving encoding efficiency.

[0243] The chroma subsampling method proposed in the embodiments can be performed and applied in both the PCC encoder and decoder.

[0244] LoD subsampling

[0245] To perform lifting and inverse transformations, each point must have neighbor points. Therefore, for lifting transformations, sampling is typically performed on all points, and the sampled group is separated from the remaining group. Then, sampling can be performed iteratively on the sampled group. In point cloud compression, this process is referred to as the Level of Detail (LoD) generation process or LoD subsampling. The encoder and decoder can generate a hierarchical structure of Levels of Detail (LoDs) by iteratively performing LoD subsampling based on the reconstructed geometry data and decoded geometry data, respectively.

[0246] LoD subsampling methods include three main types of sampling methods: distance-based sampling, octree-based sampling, and periodic sampling. All sampling methods reflect spatial characteristics by sampling only one point in a specific area, thereby enabling high compression efficiency in lifting transformations.

[0247] Subsampling omission method

[0248] The embodiments utilize the features of the aforementioned LoD to utilize point groups classified by LoD, thereby omitting the encoding / decoding of conversion coefficients for color difference components for points corresponding to a specific group.

[0249] For example, the embodiments may omit the encoding / decoding of transform coefficients for chrominance components for points included in an LoD group having a level higher than a specific LoD level. Here, a lower LoD level may mean that points within the LoD are spatially sparse (i.e., the coarser level), and a higher LoD level may mean that points within the LoD are spatially dense (the finer level).

[0250] However, this is not limited to this, and by definition, a higher LoD level may mean that points within the LoD are spatially sparse (i.e., the coarser level), and a lower LoD level may mean that points within the LoD are spatially dense (the finer level).

[0251] Unless otherwise noted, the following description is based on the assumption that a lower LoD level is the coarser level and a higher LoD level is the finer level.

[0252] The embodiments can reflect human cognitive perception characteristics by omitting the encoding / decoding of transformation coefficients for color difference components for spatially close points.

[0253] The embodiments may omit the encoding / decoding of transform coefficients for chrominance components for LoDs above a certain level. In this case, the threshold value of the specific level can be obtained by analyzing the spatial characteristics of the point cloud in the encoder, and this can be included in the bitstream and transmitted from the encoder to the decoder. Alternatively, the threshold value of the specific level can be obtained based on a representative value obtained using already restored geometry data, and this can operate identically for both the encoder and the decoder. Or, the same specific level threshold value agreed upon between the encoder and the decoder can be used.

[0254] The embodiments may omit the encoding / decoding of transform factors for chrominance components for points included in a specific LoD level. For example, the encoding / decoding of transform factors for chrominance components may be omitted for points included in the last level (i.e., the finest level) among all LoDs. The specific LoD level value may be a value obtained by analyzing the spatial characteristics of the point cloud in the encoder, and this value may be included in the bitstream and transmitted from the encoder to the decoder. Alternatively, the specific LoD level value may be obtained based on a representative value obtained using already restored geometry data, and this may operate identically for both the encoder and the decoder. Or, the same specific level value agreed upon between the encoder and the decoder may be used.

[0255] The embodiments may omit the process of encoding / decoding the conversion factor value for the first component and encoding / decoding the conversion factor value for the second and third components for the finest level of LoDs. The first component may represent a luminance component, and the second and third components may represent chrominance components. That is, for the finest level of LoDs, encoding / decoding may be performed only on the conversion factor for the luminance component among the luminance components and chrominance components.

[0256] The encoding device / method and the decoding device / method according to the embodiments may be configured such that, at the finest level of LoDs, the conversion coefficient value for the first component (luminance component) is signaled (encoded / decoded) through a bitstream, while the signaling of the conversion coefficient value for the second component (chrominance component) and the third component (chrominance component) is omitted and the value is inferred to a specific value (e.g., 0).

[0257] For example, as shown below, when chroma subsampling enable (chroma_subsampling_enabled) is 1, when the attribute coding type is 2 (i.e., lifting transformation method), and when the finest level of LoDs (i.e., dpth == lod_max_levels_minus1), the absolute value information (coeff_abs) and sign information (coeff_sign) of the transformation coefficients from the bitstream can be parsed to derive the transformation coefficient value for the first component (luminance component). The transformation coefficient values ​​for the second component (chrominance component) and the third component (chrominance component), respectively, can be inferred to be 0.

[0258] if (chroma_subsampling_enabled &&

[0259] (attr_coding_type == 2 && dpth == lod_max_levels_minus1)) {

[0260] absVal = coeff_abs[0] +1

[0261] AttrCoeff[coeffIdx][0] = (1 - 2 × coeff_sign[0]) × absVal

[0262] for (c = 1; c < AttrDim; c++){

[0263] AttrCoeff[coeffIdx][(c + 1) % AttrDim] = 0

[0264] }

[0265] In the embodiments, if color difference component encoding / decoding is omitted for a specific point group, the process of obtaining the Last Component Prediction (LCP) coefficient for that group may also be omitted. Here, LCP (Last Component Prediction) or ICP (Inter Component Prediction) may refer to a method of predicting the attribute value for the last component using the attribute value for another previously restored component when the attribute has one or more components. For example, if the attribute is YUV or RGB, the prediction can be performed using the already restored transformation coefficient for U or G for the transformation coefficient for V or B.

[0266] In the encoder, a coefficient corresponding to the ratio between the attribute value for the last component and the attribute value for the previously restored component can be calculated for each LoD level. The LCP coefficient calculated in the encoder can be included in the bitstream and transmitted to the decoder. According to the embodiments, if the encoding / decoding of the transformation coefficient for the chrominance component is omitted for a specific group of points, the data is not used in the encoder / decoder; therefore, depending on the LoD level threshold value or LoD level value that distinguishes the encoding / decoding of the transformation coefficient for the chrominance component, the step of calculating and encoding the LCP coefficient for a specific LoD level or a specific LoD region may be omitted in the encoder. In the decoder, along with whether the transformation coefficient for the chrominance component is omitted, the step of decoding the LCP coefficient for a specific LoD level or a specific LoD region may be omitted depending on the LoD level threshold value or a specific LoD level that distinguishes the encoding / decoding of the chrominance component.

[0267] In the following, the case where the attribute is YUV, that is, the case including a luminance component (Y) and a chrominance component (U, V), is described as an example. That is, the case where the encoded / decoded attribute is composed of three components, where the first component represents the luminance component (Y), and the second and third components represent the chrominance components (U, V), respectively, is described as an example. In addition, the omission of the conversion coefficient encoding / decoding for the chrominance component for a specific point group according to the embodiments may be referred to as chroma subsampling.

[0268] Lifting Transform Coefficient Encoding / Decoding Method

[0269] In lifting transform factor encoding / decoding, run-length coding may be used. In the embodiments, if the transform factor encoding for the chrominance component is omitted in the encoder, a zero run length can be calculated using only the luminance component following the existing length value. In the decoder, if the point corresponds to the omission of the chrominance component, only the transform factor for the luminance component can be decoded.

[0270] PCC data encoder

[0271] FIG. 11 shows an encoder block diagram according to embodiments.

[0272] Each component of FIG. 11 may correspond to hardware, software, processes, and / or combinations thereof. Point cloud data may be input to the encoder of FIG. 11, and the data may be encoded to output a geometry information bitstream and an attribute information bitstream.

[0273] The data input section can read and configure received data (e.g., ply, configuration file, etc.).

[0274] The coordinate system transformation unit can support coordinate system transformations, such as changing the xyz axes or transforming from an xyz orthogonal coordinate system to a spherical coordinate system.

[0275] 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.

[0276] The spatial partition can be divided into tiles or slices for area-based access or parallel processing of content.

[0277] The geometry encoding unit can encode spatially partitioned geometry information to generate a geometry information bitstream.

[0278] The color conversion processing unit can support attribute type conversion, such as changing RGB colors to YUV.

[0279] 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.

[0280] The attribute information encoding unit can receive the color-restored original attribute information, restored geometry information, and reference frame as inputs, perform encoding, and generate an attribute information bitstream.

[0281] 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.

[0282] Attribute information encoding section

[0283] FIG. 12 shows a block diagram of an attribute information encoding unit according to embodiments.

[0284] The attribute information encoding unit of Fig. 12 may be included in the encoder of Fig. 11.

[0285] Each component of the attribute information encoding unit of Fig. 12 may correspond to hardware, software, a processor, and / or a combination thereof.

[0286] The LoD generation unit can receive segmented point cloud data as input, sample points, and generate point groups by LoD (Level of Detail). The generated point groups by LoD can be transmitted to the nearest point group determination unit.

[0287] The nearest point group determination unit may use points included in a point group of a higher LoD (a LoD that is coarser than the current LoD, i.e., a LoD with a lower level than the current LoD) to determine the nearest point group for points within the point group of the current LoD using the received LoD-specific point group. At this time, whether a point is included in the nearest point group can be determined based on the distance between the current point and the point included in the higher LoD point group. The determined nearest point group may be transmitted to the weight calculation unit. The nearest point group according to the embodiments may be referred to as a predictor or a predictor set.

[0288] The weight calculation unit can calculate weights used for lifting transformation or prediction transformation using points within the received nearest point group.

[0289] The lifting transform unit can generate transform coefficients by performing a frequency transform on attribute information through a prediction and update process for each LoD level using the generated LoD. The generated transform coefficients can be quantized and transmitted to the attribute information entropy encoder. Additionally, it can output the restored attribute information by internally performing an inverse transform.

[0290] The predictive transform can be performed to predict the current point using neighbor points determined during the LoD generation process. The difference between the predicted attribute information and the original attribute information can be referred to as the transform coefficient, which can be quantized and transmitted to the attribute information entropy encoding unit. Additionally, attribute information can be restored and output through inverse quantization and inverse predictive transform.

[0291] RAHT can be performed when there are no LoD parameters. RAHT can receive segmented point cloud data as input and perform RAHT (Region Adaptive Hierarchical Transform) to transform it into the frequency domain and generate transformation coefficients. Subsequently, the transformation coefficients can be quantized and transmitted to the attribute information entropy encoding unit.

[0292] Lifting conversion unit

[0293] FIG. 13 illustrates the encoding process of the lifting conversion unit according to the embodiments.

[0294] The lifting converter of Fig. 13 may be included in the encoder of Fig. 11.

[0295] In lifting transformation, the transformation can be performed on attribute values ​​for points composed of LoDs. Through lifting transformation, each point can have a transformation coefficient. The generated transformation coefficient can be used to calculate the LCP coefficient.

[0296] LCP coefficients can be calculated by determining the coefficient between the conversion coefficient of the first chrominance component and the conversion coefficient of the second chrominance component for each LoD level. In this case, if the encoding / decoding of the chrominance components is omitted at the last LoD level (the finest level), the step of calculating LCP coefficients can be omitted.

[0297] After checking whether the current point is the last LoD level, if it is not the last LoD level, run-length encoding of the luminance-chrominance components can be performed. First, it can be determined whether the transformation coefficients of the luminance and chrominance components of the current point are both zero, and if they are both zero, the zero_run count can be increased and the process can proceed to the next point. Conversely, if either one is not zero, luminance component transformation coefficient encoding can be performed.

[0298] In the first chrominance component conversion coefficient encoding, encoding is performed on the first chrominance component conversion coefficient, and the decoded value can be used in the LCP after restoration.

[0299] In LCP, the second color difference component can be predicted by multiplying the restored first color difference component by an LCP coefficient based on the current LOD level.

[0300] Subsequently, in the second chrominance component conversion coefficient encoding, the predicted second chrominance component value can be encoded by differing the original value.

[0301] Afterwards, you can check if the current point is the last point, and if it is not the last point, you can repeat the previous process.

[0302] If the current point belongs to the last LoD, luminance component run-length encoding can be performed. In this case, luminance component run-length encoding can first determine whether the luminance conversion factor of the current point is zero or not, and if it is not zero, luminance component conversion factor encoding can be performed. If it is zero, the zero_run count can be increased and the process can proceed to the next point.

[0303] PCC data decoder

[0304] FIG. 14 shows a block diagram of a decoder according to embodiments.

[0305] Each component of the decoder of FIG. 14 may correspond to hardware, software, a processor, and / or a combination thereof. An encoded geometry information bitstream and an attribute information bitstream may be input to the decoder of FIG. 14, and PCC data that has been decoded and restored may be output.

[0306] The geometry information decoding unit can receive a geometry information bitstream, decode it, and restore the geometry information.

[0307] 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.

[0308] 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.

[0309] Attribute residual information entropy decoding can entropy decode the attribute bitstream.

[0310] The attribute information decoding unit can receive an attribute information bitstream as input, decode it, and restore the attribute information.

[0311] The color inverse conversion processing unit can restore the converted attribute to RGB color.

[0312] 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.

[0313] Attribute information decoding section

[0314] FIG. 15 shows a block diagram of an attribute information decoding unit according to embodiments.

[0315] The attribute information decoding unit of FIG. 15 may be included in the decoder of FIG. 14. Each component of the attribute information decoding unit of FIG. 15 may correspond to hardware, software, a processor, and / or a combination thereof.

[0316] The attribute information entropy decoding unit can receive an attribute information bitstream as input, decode it, and restore the conversion coefficients.

[0317] The LoD generation unit can receive segmented point cloud data as input, sample points, and generate point groups by LoD. The generated point groups by LoD can be transmitted to the nearest point group determination unit. In this case, the LoD generation unit can be executed when LoD parameters are available.

[0318] The nearest point group determination unit may use points included in a point group of a higher LoD (a LoD rougher than the current LoD, i.e., a LoD with a lower level than the current LoD) to determine the nearest point group for points within the point group of the current LoD using the received LoD-specific point group. At this time, whether a point is included in the nearest point group can be determined based on the distance between the current point and the point included in the higher LoD point group. The determined nearest point group may be transmitted to the weight calculation unit. The nearest point group according to the embodiments may be referred to as a predictor or a predictor set.

[0319] The weight calculation unit can calculate weights used for lifting transformation or prediction transformation using points within the received nearest point group.

[0320] The lifting inverse transform can restore attribute information by inverse transforming the input transformation coefficients and performing frequency inverse transform through the generated LoD and LoD level-specific update and prediction processes.

[0321] The predictive inverse transform can restore attributes by using neighbor points determined during the LoD generation process to combine the predicted value of the current point with the inverse transformed value of the input transformation coefficient.

[0322] Inverse RAHT can restore attribute information by receiving restored geometry information and transformation coefficients as input and performing the inverse process of RAHT.

[0323] The lifting transformation / lifting inverse transformation according to the embodiments can all be referred to as lifting transformation, and the prediction transformation / prediction inverse transformation can all be referred to as prediction transformation.

[0324] Attribute information decoding section

[0325] FIG. 16 illustrates the decoding process of the lifting inverse conversion unit according to the embodiments.

[0326] The lifting inverse converter of Fig. 16 may be included in the decoder of Fig. 14.

[0327] First, it is checked whether the current point is at the last LoD level; if it is not at the last LoD level (the finest level), run-length decoding of the luminance-chrominance component can be performed. In this case, run-length decoding of the luminance-chrominance component can decode the zero_run value. Then, for each point, the zero_run value is differed by 1, and if zero_run is 0, decoding of the luminance component transformation coefficient can be performed.

[0328] In luminance component conversion coefficient decoding, the luminance component conversion coefficient can be decoded.

[0329] In the first chrominance component conversion coefficient decoding, decoding is performed on the first chrominance component conversion coefficient, and the decoded value can be used in the LCP.

[0330] In LCP, the second color difference component can be predicted by multiplying the restored first color difference component by an LCP coefficient based on the current LoD level.

[0331] Subsequently, in the decoding of the second color difference component transformation coefficient, the second color difference component is decoded and combined with the predicted second color difference component value to restore the transformation coefficient of the second color difference component.

[0332] Afterwards, you can check if the current point is the last point, and if it is not, you can repeat the previous process.

[0333] If the current point corresponds to the last LoD, luminance component run-length decoding can be performed. The zero_run value can be decoded. Then, for each point, the zero_run value is differed by 1, and if zero_run is 0, luminance component transformation coefficient decoding can be performed.

[0334] FIG. 17 shows a bitstream structure according to embodiments.

[0335] Related information may be signaled to include or perform the device / method according to the above-described embodiments. The signaling information according to the embodiments may be used at a transmitting end or a receiving end, etc.

[0336] The encoding device illustrated in FIG. 1, FIG. 2, FIG. 8, FIG. 11, etc., according to embodiments can encode point-point cloud data and generate a bitstream containing the encoded point cloud data.

[0337] The decoding device illustrated in FIG. 1, FIG. 2, FIG. 7, FIG. 9, FIG. 14, etc., according to embodiments can acquire the FIG. 17 bitstream and decode the point cloud data in FIG. 17 based on parameter information in the FIG. 17 bitstream.

[0338] A point cloud data encoder that performs geometry encoding and / or attribute encoding processes can generate the encoded point cloud of FIG. 17 (or a bitstream containing the point cloud). Additionally, signaling information regarding the point cloud data can be generated and processed by a metadata processing unit of the point cloud data transmission device and included in the bitstream of FIG. 17.

[0339] Each abbreviation used in FIG. 17 means the following. Each abbreviation may be referred to by other terms within the scope of equivalent meaning.

[0340] SPS: Sequence Parameter Set

[0341] GPS: Geometry Parameter Set

[0342] APS: Attribute Parameter Set

[0343] TPS: Tile Parameter Set

[0344] Geom: Geometry bitstream = Geometry slice header + [Geometry PU header + Geometry PU data] | Geometry slice data

[0345] Attr: Attribute bitstream = attribute data unit header | attribute data unit data

[0346] Chroma subsampling related option information can be added to and signaled in SPS and APS.

[0347] Chroma subsampling option information can be signaled by adding it to the TPS or the attribute header for each slice.

[0348] Tile or slice configurations can be provided to allow the point cloud to be processed by dividing it into regions. When dividing the point cloud by region, different options for generating neighbor point sets can be set for each region. This allows for options that result in lower complexity but slightly lower reliability, or conversely, higher reliability and slightly higher complexity. These settings can be configured differently depending on the receiver's processing capacity. Therefore, when the point cloud is divided into tiles, different options can be applied to each tile; similarly, when the point cloud is divided into slices, different options can be applied to each slice.

[0349] The encoding device illustrated in FIG. 1, FIG. 2, FIG. 8, FIG. 11, etc., according to embodiments can encode point-point cloud data and generate parameter information related to the encoded point cloud data.

[0350] Parameter information in FIG. 17 can be obtained based on parameter information in FIG. 17 bitstream by a decoding device illustrated in FIG. 1, FIG. 2, FIG. 7, FIG. 9, FIG. 14, etc., according to embodiments.

[0351] Hereinafter, the syntax of the parameter information included in the bitstream of FIG. 17 will be explained with reference to each figure.

[0352] FIG. 18 shows a set of sequence parameters according to the embodiments.

[0353] The sequence parameter set of Fig. 18 can be included in the bitstream of Fig. 17.

[0354] Option information related to chroma subsampling can be added to the sequence parameter set and signaled. The name of the signaling information can be understood within the scope of the meaning and function of the signaling information.

[0355] The profile indicator (profile_idc) indicates the profile that the bitstream complies with, as specified in Annex A of the E-GPCC standard document (ISO / IEC 23090-38). The bitstream must not contain a profile_idc value other than that specified in Annex A. Other values ​​of profile_idc are reserved for future use by ISO / IEC.

[0356] If profile_compatibility_flags is 1, it indicates that the bitstream complies with the profile where profile_idc is j.

[0357] The number of SPS attribute sets (sps_num_attribute_sets) represents the number of coded attributes in the bitstream. The value of sps_num_attribute_sets must be in the range of 0 to 63.

[0358] The attribute dimension (attribute_dimension[ i ]) specifies the number of components of the i-th attribute. This can be expressed as AttrDim. An AttrDim of 3 means that the i-th attribute consists of a first component, a second component, and a third component.

[0359] The attribute instance ID (attribute_instance_id[ i ]) specifies the instance ID of the i-th attribute.

[0360] The SPS attribute chroma subsampling enable (sps_attr_chroma_subsampling_enabled) specifies whether to apply chroma subsampling to the sequence.

[0361] FIG. 19 shows a set of attribute parameters according to the embodiments.

[0362] The set of attribute parameters of Fig. 19 can be included in the bitstream of Fig. 17.

[0363] During the attribute information encoding / decoding process, option information related to chroma subsampling can be added to the attribute parameter set and signaled. The name of the signaling information can be understood within the scope of its meaning and function.

[0364] The attribute parameter set ID (aps_attr_parameter_set_id) provides the identifier of the APS for reference by the Data Unit (DU).

[0365] The sequence parameter set ID (aps_seq_parameter_set_id) specifies the active SPS using sps_seq_parameter_set_id.

[0366] The attribute coding type (attr_coding_type) specifies the attribute coding method. Valid values ​​are 0 to 3. 0 = RAHT (Region Adaptive Layer Transform), 1 = Predicting Transform, 2 = Lifting Transform, 3 = Raw attribute data. Other values ​​are reserved for future use by ISO / IEC. The decoder must ignore (remove from the bitstream and discard) attribute data units encoded with an attr_coding_type of a reserved value.

[0367] The attribute chroma subsampling enable (aps_attr_chroma_subsampling_enabled) specifies whether to apply chroma subsampling to the frame.

[0368] The chroma subsampling level (aps_chroma_subsampling_level) specifies the LoD level to apply chroma subsampling to the frame.

[0369] Last component prediction enable (last_comp_pred_enabled) specifies whether the second coefficient component of a three-component attribute should be used to predict the value of the third coefficient component (when 1) or not (when 0). If last_comp_pred_enabled does not exist, the value should be inferred as 0.

[0370] LoD max_levels_minus1 specifies the maximum number of detail levels that can be generated through the LoD generation process.

[0371] The last component prediction coefficient difference (last_comp_pred_coeff_diff[ dpth ]) specifies a two-fractional-bit fixed-point scale factor applied to the second coefficient component at the depth dpth of the Level of Detail (LoD) layer to predict the third coefficient component.

[0372] According to the embodiments, dpth represents the depth of the LoDs structure. dpth is 0 at the coarsest level of the LoDs and may have a maximum value at the finest level of the LoDs. Here, the maximum value may mean the number of levels of the LoDs minus 1. According to the above-described definition of the levels of the LoDs, dpth may have the same value as the level value of the LoDs.

[0373] FIG. 20 shows a set of attribute parameters according to embodiments.

[0374] The set of attribute parameters of Fig. 20 can be included in the bitstream of Fig. 17.

[0375] Referring to FIG. 20, the attribute parameter set according to the embodiments may include information about the LoD level threshold to which chroma subsampling is to be applied, instead of information about the specific level to which chroma subsampling is applied.

[0376] The chroma subsampling level threshold (aps_chroma_subsampling_level_threshold) specifies the threshold value of the LoD level to apply chroma subsampling to the frame.

[0377] FIG. 21 shows an attribute data header according to embodiments.

[0378] The attribute data header of Fig. 21 can be included in the bitstream of Fig. 17.

[0379] Option information related to chroma subsampling can be added to the attribute slice header and signaled. The name of the signaling information can be understood within the scope of its meaning and function.

[0380] The attribute parameter set ID (adh_attr_parameter_set_id) specifies the aps_attr_parameter_set_id value of the active APS.

[0381] The attribute SPS attribute index (adh_attr_sps_attr_idx) specifies the attribute set within the active SPS. The value of adh_attr_sps_attr_idx must be in the range from 0 to sps_num_attribute_sets within the active SPS.

[0382] The attribute geometry slice ID (adh_attr_geom_slice_id) specifies the gsh_slice_id value of the active geometry slice header.

[0383] The attribute chroma subsampling enable (adh_attr_chroma_subsampling_enabled) specifies whether to apply chroma subsampling to the slice.

[0384] The chroma subsampling level (adh_chroma_subsampling_level) specifies the LoD level to apply chroma subsampling to the slice.

[0385] FIG. 22 shows an attribute data header according to embodiments.

[0386] The attribute data header of Fig. 22 can be included in the bitstream of Fig. 17.

[0387] Referring to FIG. 22, the attribute data header according to the embodiments may include information about the LoD level threshold to which chroma subsampling is to be applied, instead of information about the specific level to which chroma subsampling is applied.

[0388] The chroma subsampling level threshold (adh_chroma_subsampling_level_threshold) specifies the threshold value of the LoD level to apply chroma subsampling to the frame.

[0389] The syntax structure of FIGS. 19 to 22 according to the embodiments is described as an example where the depth dpth of the LoDs is equal to the level value, based on the case where the level value of the finest level of the LoDs is the highest and the level value of the coarsest level is the lowest. Taking FIG. 19 as an example, the if statement below means that if last_comp_pred_enabled is 1 and the attribute data is data for an attribute composed of 3 components, parsing the last_comp_pred_coeff_diff[ dpth ] value for the finest level of the LoDs is omitted.

[0390] if( last_comp_pred_enabled && AttrDim == 3)

[0391] for( dpth = 0; dpth ≤ lod_max_levels_minus1; dpth++ ){

[0392] if (aps_chroma_subsampling_level != dpth)

[0393] last_comp_pred_coeff_diff[dpth]

[0394] }

[0395] In contrast, if we consider the case where the level value of the finest level of LoDs is set to the lowest and the level value of the coarsest level is set to the highest, dpth and the if statements of FIGS. 19 to 22 may be set differently from those described above. For example, the depth dpth of LoDs can be defined as dpth := LodCnt - 1 - Lvl. Here, LoDCnt represents the total number of levels of LoDs, and Lvl represents the level value of the current LoD. That is, dpth can be set inversely to the level value.

[0396] Under the above definition, the if statements in FIGS. 19 and FIGS. 21 can be changed as follows.

[0397] if( last_comp_pred_enabled && AttrDim == 3)

[0398] for( dpth = 0; dpth ≤ lod_max_levels_minus1; dpth++ ){

[0399] if (aps_chroma_subsampling_level != LodCnt-1-dpth)

[0400] last_comp_pred_coeff_diff[dpth]

[0401] }

[0402] Likewise, the if statements in FIGS. 20 and FIGS. 22 can be changed as follows.

[0403] if( last_comp_pred_enabled && AttrDim == 3)

[0404] for( dpth = 0; dpth ≤ lod_max_levels_minus1; dpth++ ){

[0405] if (aps_chroma_subsampling_level_threshold >= LodCnt-1-dpth)

[0406] last_comp_pred_coeff_diff[dpth]

[0407] }

[0408] The if condition included in the if statement of FIGS. 19 to 22 according to the embodiments and the if condition when the level definition of the LoDs described above is different can be changed within the scope of not parsing the last_comp_pred_coeff_diff[ dpth ] value when it is the finest level of the LoDs.

[0409] For example, if the level value of the finest level of LoDs is defined to be the lowest and the level value of the coarsest level is defined to be the highest, and the depth dpth of LoDs is defined as dpth := LodCnt - 1 - Lvl, then the if of FIGS. 19 to 22 according to the embodiments may be changed as follows.

[0410] When chrama_subsampling_enable is 1, dpthoffset becomes 1

[0411] if( last_comp_pred_enabled && AttrDim == 3)

[0412] for( dpth = 0; dpth ≤ lod_max_levels_minus1 - dpthoffset; dpth++ )

[0413] last_comp_pred_coeff_diff[dpth]

[0414] In this case, dpthoffset can be set to a value of 1 when chroma_subsampling_enable is 1.

[0415] dpthoffset = chroma_subsampling_enabled ? 1:0

[0416] FIG. 23 illustrates an encoding method according to embodiments.

[0417] The encoding method according to the embodiments may include the step of encoding geometry data of point cloud data (S2300) and the step of encoding attribute data of point cloud data (S2310).

[0418] The encoding method according to the embodiments may include the method described in FIGS. 1 to 22 above. Specifically, the step of encoding geometry data of point cloud data (S2300) may include the method described in FIGS. 1 to 4, FIG. 8, FIG. 11, FIG. 17, etc. above. The step of encoding attribute data of point cloud data (S2310) may include the method described in FIGS. 1 to 3, FIGS. 5 to 6, FIG. 8, FIGS. 11 to 13, FIGS. 17 to 22, etc. above.

[0419] A encoding method according to embodiments includes encoded geometry data and encoded attribute data in a bitstream, the bitstream includes first information indicating whether chroma subsampling is performed, the attribute data is encoded based on Levels of Detail (LoDs), and the step of encoding the attribute data may include the step of encoding a transform coefficient of a luma component for a point in a level of the LoDs based on a first value of the first information.

[0420] The encoding method according to the embodiments may omit the step of encoding attribute data, which encodes the conversion coefficient of the color difference component for the point within the finest level based on the first value of the first information.

[0421] The encoding method according to the embodiments may further include the step of encoding attribute data, the step of encoding a transform coefficient of a luminance component for a point in a level other than the finest level among the LoDs; and the step of encoding a transform coefficient of a chroma component for the point.

[0422] Referring together with FIG. 17, the encoding method according to the embodiments includes encoded geometry data and encoded attribute data in a bitstream, the attribute data is encoded based on Levels of Detail (LoDs), and the attribute data includes data for an attribute composed of a first component, a second component, and a third component, and the bitstream includes first information indicating whether chroma subsampling is performed, second information regarding the maximum number of levels of LoDs, and third information indicating whether the value of an attribute coefficient for a third component is predicted based on the value of an attribute coefficient for a second component, and based on the first information, the second information, and the third information, the step of encoding the attribute data is for an attribute for a second component to predict the value of an attribute coefficient for a third component for a level other than a finest level among the LoDs. The method includes a step of obtaining information about a scale factor applied to the value of a coefficient, and the step of obtaining information about a scale factor applied to the value of an attribute coefficient for a second component for predicting the value of an attribute coefficient for a third component for the finest level of the LoDs may be omitted.

[0423] 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.

[0424] The embodiments may further include a computer-readable storage medium that stores a bitstream generated by the method of FIG. 23.

[0425] 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.

[0426] FIG. 24 illustrates a decoding method according to embodiments.

[0427] The decoding method according to the embodiments may include the step of decoding geometry data of point cloud data within a bitstream (S2400); and the step of decoding attribute data of point cloud data (S2410).

[0428] The decoding method according to the embodiments may include the method described in FIGS. 1 to 22 above. Specifically, the step (S2400) of decoding geometry data of point cloud data within a bitstream may include the method described in FIGS. 1 to 2, FIG. 4, FIG. 7, FIG. 9, FIG. 14, FIG. 17, etc. above. The step (S2410) of decoding attribute data of point cloud data may include the method described in FIGS. 1 to 2, FIGS. 5 to 7, FIG. 9, FIGS. 14 to 22, etc. above.

[0429] A decoding method according to the embodiments includes first information indicating whether the bitstream performs chroma subsampling, attribute data is decoded based on Levels of Detail (LoDs), and the step of decoding attribute data may include the step of decoding a transform coefficient of a luma component for a point in a level of the finest level of the LoDs based on a first value of the first information.

[0430] The decoding method according to the embodiments can decode only the transformation coefficient of the luminance component among the luminance component and the color difference components in the step of decoding attribute data.

[0431] The decoding method according to the embodiments may omit the step of decoding attribute data, which involves decoding the conversion coefficient of the color difference component for a point within the finest level based on the first value of the first information.

[0432] The decoding method according to the embodiments may further include the step of decoding attribute data, the step of decoding a transform coefficient of a luminance component for a point in a level other than the finest level among the LoDs; and the step of decoding a transform coefficient of a chroma component for the point.

[0433] Referring together with FIG. 17, a decoding method according to embodiments wherein attribute data is decoded based on Levels of Detail (LoDs), and the attribute data includes data for an attribute composed of a first component, a second component, and a third component, and the bitstream includes first information indicating whether chroma subsampling is performed, second information regarding the maximum number of levels of LoDs, and third information indicating whether the value of an attribute coefficient for a third component is predicted based on the value of an attribute coefficient for a second component, and the step of decoding attribute data based on the first information, the second information, and the third information further includes the step of obtaining information regarding a scale factor applied to the value of an attribute coefficient for a second component for predicting the value of an attribute coefficient for a third component for a level other than a finest level among the LoDs. It is possible.

[0434] Referring together with FIG. 17, the decoding method according to the embodiments may omit the step of decoding attribute data, wherein the step of obtaining information about a scale factor applied to the value of an attribute coefficient for a second component for predicting the value of an attribute coefficient for a third component for the finest level of the LoDs may be omitted.

[0435] Referring together with FIG. 17, the decoding method according to the embodiments may have the first component representing the luminance component of the attribute, and the second and third components each representing the color difference component of the attribute.

[0436] 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.

[0437] The PCC encoding method / device, PCC decoding method / device, signaling method, etc. of the embodiments described above can provide the following effects.

[0438] The embodiments can have the effect of reducing the size of the bitstream by omitting the encoding / decoding of the conversion coefficients of the color difference component for some point groups for which humans do not perceive the difference, taking into account human cognitive perception characteristics.

[0439] Accordingly, the encoding method / device according to the embodiments can efficiently compress point cloud data and transmit the data, and by transmitting signaling information for this purpose, the decoding method / device according to the embodiments can also efficiently decode / restore point cloud data.

[0440] The operation of the encoding / decoding device according to the embodiments described above can be explained in combination with the point cloud compression processing process described above.

[0441] 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.

[0442] 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.

[0443] 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.

[0444] 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.”

[0445] 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.

[0446] 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."

[0447] 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.

[0448] 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.

[0449] 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.

[0450]

[0451] As described above, the relevant details have been explained in the best mode for carrying out the embodiments.

[0452]

[0453] As described above, the embodiments may be applied wholly or partially to point cloud data transmission and reception devices and systems.

[0454] Those skilled in the art may make various changes or modifications to the embodiments within the scope of the embodiments.

[0455] 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 bitstream includes first information indicating whether chroma subsampling is performed, and The above attribute data is decoded based on LoDs (Levels of Detail), and The step of decoding the above attribute data is, A step comprising decoding a transform coefficient of a luma component for a point in a level of the LoDs based on a first value of the first information, Decryption method.

3. In Paragraph 2, The step of decoding the above attribute data is, Decoding only the conversion coefficient of the luminance component among the above luminance component and color difference components, Decryption method.

4. In paragraph 2, the step of decoding the attribute data is, Based on the first value of the first information above, the step of decoding the conversion coefficient of the color difference component for the point within the finest level above is omitted. Decryption method.

5. In Paragraph 2, The step of decoding the above attribute data is, A step of decoding the transformation coefficient of a luminance component for a point in a level other than the finest level among the LoDs; and A step further comprising decoding a transform coefficient of a chroma component for the above point, Decryption method.

6. In Paragraph 1, The above attribute data is decoded based on LoDs (Levels of Detail), and The above attribute data includes data for an attribute composed of a first component, a second component, and a third component, and The bitstream includes first information indicating whether chroma subsampling is performed, second information regarding the maximum number of levels of the LoDS, and third information indicating whether the value of the attribute coefficient for the third component is predicted based on the value of the attribute coefficient for the second component. The step of decoding the attribute data based on the first information, the second information, and the third information is, The method further includes the step of obtaining information on a scale factor applied to the value of an attribute coefficient for the second component for predicting the value of an attribute coefficient for the third component for a level other than a finest level among the LoDs. Decryption method.

7. In Paragraph 6, The step of decoding the above attribute data is, The step of obtaining information on the scale factor applied to the value of the attribute coefficient for the second component for predicting the value of the attribute coefficient for the third component for the finest level of the LoDs is omitted. Decryption method.

8. In Paragraph 7, The first component above represents the luma component of the attribute, and The second component and the third component each represent a color difference component of the attribute, 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 10, The above-mentioned encoded geometry data and the above-mentioned encoded attribute data are included in a bitstream, and The above bitstream includes first information indicating whether chroma subsampling is performed, and The above attribute data is encoded based on LoDs (Levels of Detail), and The step of encoding the above attribute data is, A step comprising encoding a transform coefficient of a luma component for a point in a level of the LoDs based on a first value of the first information, Encoding method.

12. In Paragraph 11, The step of encoding the above attribute data is, Based on the first value of the first information above, the step of encoding the conversion coefficient of the color difference component for the point within the finest level above is omitted. Encoding method.

13. In Paragraph 11, The step of encoding the above attribute data is, A step of encoding a transformation coefficient of a luminance component for a point in a level other than the finest level among the LoDs; and A method further comprising the step of encoding a transform coefficient of a chroma component for the above point, 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 attribute data is encoded based on LoDs (Levels of Detail), and The above attribute data includes data for an attribute composed of a first component, a second component, and a third component, and The bitstream includes first information indicating whether chroma subsampling is performed, second information regarding the maximum number of levels of the LoDS, and third information indicating whether the value of the attribute coefficient for the third component is predicted based on the value of the attribute coefficient for the second component. Based on the first information, the second information, and the third information, the step of encoding the attribute data is: For a level other than a finest level among the LoDs, the method includes the step of obtaining information on a scale factor applied to the value of an attribute coefficient for the second component to predict the value of an attribute coefficient for the third component. The step of obtaining information on the scale factor applied to the value of the attribute coefficient for the second component for predicting the value of the attribute coefficient for the third component for the finest level of the LoDs is omitted. 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 10.

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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