Point cloud data sending device, point cloud data sending method, point cloud data receiving device and point cloud data receiving method

Abstract

According to an embodiment, the point cloud data sending method may include the following steps: acquiring point cloud data; encoding geometric information of the positions of points including the point cloud data; generating one or more levels of detail (LODs) based on the geometric information, and selecting one or more neighboring points of each point to be attribute-encoded based on the one or more LODs; encoding attribute information about each point based on the one or more neighboring points of each selected point; and sending the encoded geometric information, the encoded attribute information and signaling information.

Description

Technical Field

[0001] Embodiments relate to methods and apparatus for processing point cloud content. Background Art

[0002] Point cloud content is represented by a point cloud, which is a collection of points belonging to a coordinate system representing three-dimensional space. Point cloud content can represent media configured in three dimensions and is used to provide various services such as virtual reality (VR), augmented reality (AR), mixed reality (MR), XR (extended reality), and autonomous driving. However, tens of thousands to hundreds of thousands of points of data are required to represent point cloud content. Therefore, methods for efficiently processing large amounts of point data are needed. Summary of the Invention

[0003] Technical issues

[0004] The present disclosure, designed to solve the above-mentioned problems, aims to provide a point cloud data transmitting device, a point cloud data transmitting method, a point cloud data receiving device and a point cloud data receiving method for efficiently transmitting and receiving point clouds.

[0005] Another object of the present disclosure is to provide a point cloud data transmitting device, a point cloud data transmitting method, a point cloud data receiving device, and a point cloud data receiving method for coping with waiting time and encoding / decoding complexity.

[0006] Another object of the present disclosure is to provide a point cloud data sending device, a point cloud data sending method, a point cloud data receiving device and a point cloud data receiving method for improving the compression performance of point clouds by improving the encoding technology of attribute information of geometry-based point cloud compression (G-PCC).

[0007] Another object of the present disclosure is to provide a point cloud data transmitting device, a point cloud data transmitting method, a point cloud data receiving device, and a point cloud data receiving method for enhancing compression efficiency while supporting parallel processing of attribute information of G-PCC.

[0008] Another object of the present disclosure is to provide a point cloud data sending device, a point cloud data sending method, a point cloud data receiving device, and a point cloud data receiving method that reduce the size of the attribute bit stream and enhance the compression efficiency of the attributes by selecting neighboring points for attribute prediction taking into account the attribute correlation between points in the content when the attribute information of G-PCC is encoded.

[0009] Another object of the present disclosure is to provide a point cloud data sending device, a point cloud data sending method, a point cloud data receiving device, and a point cloud data receiving method that reduce the size of the attribute bit stream and enhance the compression efficiency of the attribute by applying the maximum neighboring point distance when the attribute information of the G-PCC is encoded and selecting neighboring points for attribute prediction.

[0010] The objects of the present disclosure are not limited to the above-mentioned objects, and other objects of the present disclosure not mentioned above will become clear to those skilled in the art after reviewing the following description.

[0011] Technical Solution

[0012] To achieve these objectives and other advantages and in accordance with the purposes of the present disclosure, as implemented and broadly described herein, a method for sending point cloud data may include the following steps: acquiring point cloud data; encoding geometric information of the positions of points comprising the point cloud data; generating one or more levels of detail (LODs) based on the geometric information, and searching for one or more neighboring points of each point to be attribute encoded based on the one or more LODs; encoding attribute information of each point based on the one or more neighboring points of each selected point; and sending the encoded geometric information, the encoded attribute information and signaling information.

[0013] According to an embodiment, the one or more neighboring points of each selected point may be located within a maximum neighboring point distance.

[0014] According to an embodiment, the maximum neighboring point distance may be determined based on the basic neighboring point distance and the maximum neighboring point range.

[0015] According to an embodiment, when the one or more LODs are generated based on an octree, the basic neighbor distance may be determined based on a diagonal distance of a node at a specific LOD.

[0016] According to an embodiment, the maximum neighboring point range may be the number of neighboring nodes for each point and may be signaled in the signaling information.

[0017] According to an embodiment, a point cloud data sending device may include: an acquisition unit configured to acquire point cloud data; a geometry encoder configured to encode geometric information of the positions of points including the point cloud data; an attribute encoder configured to generate one or more levels of detail (LODs) based on the geometric information, select one or more neighboring points of each point to be attribute-encoded based on the one or more LODs, and encode attribute information of each point based on the one or more neighboring points of each selected point; and a transmitter configured to send the encoded geometric information, the encoded attribute information and signaling information.

[0018] According to an embodiment, the one or more neighboring points of each selected point may be located within a maximum neighboring point distance.

[0019] According to an embodiment, the maximum neighboring point distance may be determined based on the basic neighboring point distance and the maximum neighboring point range.

[0020] According to an embodiment, when the one or more LODs are generated based on an octree, the basic neighbor distance may be determined based on a diagonal distance of a node at a specific LOD.

[0021] According to an embodiment, the maximum neighboring point range may be the number of neighboring nodes for each point and may be signaled in the signaling information.

[0022] According to an embodiment, a point cloud data receiving device may include: a receiver configured to receive geometric information, attribute information and signaling information; a geometry decoder configured to decode the geometric information based on the signaling information; an attribute decoder configured to generate one or more levels of detail (LODs) based on the geometric information, select one or more neighboring points of each point to be attribute decoded based on the one or more LODs, and decode the attribute information of each point based on the one or more neighboring points of each selected point and the signaling information; and a renderer configured to render the recovered point cloud data based on the decoded geometric information and the decoded attribute information.

[0023] According to an embodiment, the one or more neighboring points of each selected point may be located within a maximum neighboring point distance.

[0024] According to an embodiment, the maximum neighboring point distance may be determined based on the basic neighboring point distance and the maximum neighboring point range.

[0025] According to an embodiment, when the one or more LODs are generated based on an octree, the basic neighbor distance may be determined based on a diagonal distance of a node at a specific LOD.

[0026] According to an embodiment, the maximum neighboring point range may be the number of neighboring nodes for each point and may be signaled in the signaling information.

[0027] Beneficial effects

[0028] The point cloud data transmitting method, point cloud data transmitting device, point cloud data receiving method, and point cloud receiving device according to the embodiments can provide high-quality point cloud services.

[0029] The point cloud data transmitting method, the point cloud data transmitting device, the point cloud data receiving method, and the point cloud receiving device according to the embodiments may implement various video encoding methods.

[0030] The point cloud data transmitting method, the point cloud data transmitting device, the point cloud data receiving method, and the point cloud receiving device according to the embodiments can provide general point cloud content such as an automatic driving service (or autonomous driving service).

[0031] The point cloud data transmitting method, point cloud data transmitting device, point cloud data receiving method, and point cloud data receiving device according to the embodiments can perform spatially adaptive division of point cloud data for independent encoding and decoding of point cloud data, thereby improving parallel processing and providing scalability.

[0032] According to the embodiment, the point cloud data sending method, point cloud data sending device, point cloud data receiving method and point cloud data receiving device can perform encoding and decoding by spatially dividing the point cloud data and thus the signal necessary data in units of blocks and / or slices, thereby improving the encoding and decoding performance of the point cloud.

[0033] The point cloud data sending method and apparatus and the point cloud data receiving method and apparatus according to the embodiment reduce the size of the attribute bitstream and enhance the compression efficiency of the attribute by selecting neighboring points for attribute prediction considering the attribute correlation between points in the content when the attribute information of G-PCC is encoded.

[0034] The point cloud data transmitting method and apparatus and the point cloud data receiving method and apparatus according to the embodiment reduce the size of the attribute bitstream and enhance the compression efficiency of the attribute by applying the maximum neighboring point distance to select neighboring points for attribute prediction when the attribute information of the G-PCC is encoded. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] The accompanying drawings are included to provide a further understanding of the present disclosure and are incorporated in and constitute a part of this application. They illustrate embodiments of the present disclosure and together with the description serve to explain the principles of the present disclosure. In the drawings:

[0036] Figure 1 An exemplary point cloud content providing system according to an embodiment is illustrated.

[0037] Figure 2 is a block diagram illustrating a point cloud content providing operation according to an embodiment.

[0038] Figure 3 An exemplary process for capturing point cloud video according to an embodiment is illustrated.

[0039] Figure 4 An exemplary block diagram of a point cloud video encoder according to an embodiment is illustrated.

[0040] Figure 5 An example of voxels in a 3D space according to an embodiment is illustrated.

[0041] Figure 6 Examples of octrees and occupancy codes according to embodiments are illustrated.

[0042] Figure 7 An example of a neighboring node pattern according to an embodiment is illustrated.

[0043] Figure 8 An example of point configuration of point cloud content for each LOD according to an embodiment is illustrated.

[0044] Figure 9 An example of point configuration of point cloud content for each LOD according to an embodiment is illustrated.

[0045] Figure 10 An example of a block diagram of a point cloud video decoder according to an embodiment is illustrated.

[0046] Figure 11 An example of a point cloud video decoder according to an embodiment is illustrated.

[0047] Figure 12 The configuration of point cloud video encoding of the transmitting device according to the embodiment is illustrated.

[0048] Figure 13 The configuration of point cloud video decoding of a receiving device according to an embodiment is illustrated.

[0049] Figure 14 An exemplary structure operatively connectable with a method / apparatus for transmitting and receiving point cloud data according to an embodiment is illustrated.

[0050] Figure 15 An example of a point cloud transmitting device according to an embodiment is illustrated.

[0051] Figure 16 (a) to Figure 16 (c) illustrates an implementation of segmenting the bounding box into one or more tiles.

[0052] Figure 17 Examples of a geometry encoder and an attribute encoder according to an embodiment are illustrated.

[0053] Figure 18 is a diagram illustrating an example of generating LOD based on an octree according to an embodiment.

[0054] Figure 19 is a diagram illustrating an example of arranging points of a point cloud in a Morton code order according to an embodiment.

[0055] Figure 20 is a diagram illustrating an example of searching for neighboring points based on LOD according to an embodiment.

[0056] Figure 21 (a) and Figure 21 (b) illustrates an example of point cloud content according to an embodiment.

[0057] Figure 22 (a) and Figure 22 (b) illustrates an example of an average distance, a minimum distance, and a maximum distance of each point belonging to a neighboring point set according to an embodiment.

[0058] Figure 23 (a) illustrates an example of obtaining a diagonal distance of an octree node of LOD0 according to an embodiment.

[0059] Figure 23 (b) illustrates an example of obtaining the diagonal distance of the octree node of LOD1 according to an embodiment.

[0060] Figure 24 (a) and Figure 24 (b) illustrates an example of a range that may be selected as a neighboring point at each LOD.

[0061] Figure 25 An example of a basic neighboring point distance belonging to each LOD according to an embodiment is illustrated.

[0062] Figure 26 Another example of searching for neighboring points based on LOD according to an embodiment is illustrated.

[0063] Figure 27 Another example of searching for neighboring points based on LOD according to an embodiment is illustrated.

[0064] Figure 28 An example of a point cloud receiving device according to an embodiment is illustrated.

[0065] Figure 29 Examples of a geometry decoder and an attribute decoder according to an embodiment are illustrated.

[0066] Figure 30 An exemplary bitstream structure for transmitting / receiving point cloud data according to an embodiment is illustrated.

[0067] Figure 31 An exemplary bitstream structure of point cloud data according to an embodiment is illustrated.

[0068] Figure 32 The connection relationship between components in a bit stream of point cloud data according to an embodiment is illustrated.

[0069] Figure 33 Implementations of the syntax structure of a sequence parameter set according to an implementation are illustrated.

[0070] Figure 34 Implementations of the syntax structure of a geometry parameter set according to implementations are illustrated.

[0071] Figure 35 An embodiment of a syntax structure of a property parameter set according to an embodiment is illustrated.

[0072] Figure 36 Another embodiment of the syntax structure of the attribute parameter set according to the embodiment is illustrated.

[0073] Figure 37 Implementations of a syntax structure of a tile parameter set according to implementations are illustrated.

[0074] Figure 38 An embodiment of the syntax structure of a geometry slice bitstream() according to an embodiment is illustrated.

[0075] Figure 39 An embodiment of a syntax structure of a geometry slice header according to an embodiment is illustrated.

[0076] Figure 40 An embodiment of a syntax structure of geometry slice data according to an embodiment is illustrated.

[0077] Figure 41 An embodiment of a syntax structure of an attribute slice bitstream() according to an embodiment is illustrated.

[0078] Figure 42 An embodiment of a syntax structure of an attribute slice header according to an embodiment is illustrated.

[0079] Figure 43 Another embodiment of the syntax structure of the attribute slice header according to the embodiment is illustrated.

[0080] Figure 44 An embodiment of a syntax structure of attribute slice data according to an embodiment is illustrated.

[0081] Figure 45 is a flowchart of a method of transmitting point cloud data according to an embodiment.

[0082] Figure 46 is a flowchart of a method of receiving point cloud data according to an embodiment. DETAILED DESCRIPTION

[0083] Now, a detailed description will be given based on the exemplary embodiments disclosed herein with reference to the accompanying drawings. For the sake of brevity with reference to the accompanying drawings, identical or equivalent components may be given the same reference numerals, and their descriptions will not be repeated. It should be noted that the following examples are merely illustrative of the present disclosure and do not limit the scope of the present disclosure. Anything that can be easily inferred from the detailed description and examples of the present disclosure by an expert in the technical field to which the present invention pertains is to be construed as falling within the scope of the present disclosure.

[0084] The detailed description in this specification should be interpreted in all aspects as illustrative and not restrictive. The scope of the present disclosure should be determined by the appended claims and their legal equivalents, and all changes coming within the meaning and equivalency range of the appended claims are intended to be embraced herein.

[0085] Reference will now be made in detail to the preferred embodiments of the present disclosure, examples of which are illustrated in the accompanying drawings. The detailed description given below with reference to the accompanying drawings is intended to explain exemplary embodiments of the present disclosure, rather than to show the only embodiments that can be implemented according to the present disclosure. The following detailed description includes specific details to provide a thorough understanding of the present disclosure. However, it will be apparent to those skilled in the art that the present disclosure can be practiced without these specific details. Although most of the terms used in this specification have been selected from general terms widely used in the art, the applicant has arbitrarily selected some terms, and their meanings will be explained in detail as needed in the following description. Therefore, the present disclosure should be understood based on the original intention of the terms rather than their simple names or meanings. In addition, the following drawings and detailed description should not be interpreted as being limited to the specifically described embodiments, but should be interpreted as including equivalents or alternatives of the embodiments described in the drawings and detailed description.

[0086] Figure 1 An exemplary point cloud content providing system according to an embodiment is shown.

[0087] Figure 1 The point cloud content providing system illustrated in FIG. 1 may include a transmitting device 10000 and a receiving device 10004. The transmitting device 10000 and the receiving device 10004 may perform wired or wireless communication to transmit and receive point cloud data.

[0088] The point cloud data sending device 10000 according to an embodiment can protect and process point cloud video (or point cloud content) and send the point cloud video (or point cloud content). According to an embodiment, the sending 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 a server. According to an embodiment, the sending device 10000 may include a device configured to communicate with a base station and / or other wireless devices using a radio access technology (e.g., 5G New RAT (NR), Long Term Evolution (LTE)), a robot, a vehicle, an AR / VR / XR device, a portable device, a home appliance, an Internet of Things (IoT) device and an AI device / server.

[0089] According to an embodiment, the sending device 10000 includes a point cloud video acquisition unit 10001, a point cloud video encoder 10002 and / or a transmitter (or communication module) 10003.

[0090] The point cloud video acquisition unit 10001 according to an embodiment acquires a point cloud video through a process such as capture, synthesis, or generation. A point cloud video is point cloud content represented by a point cloud, which is a collection of points in 3D space, and can be referred to as point cloud video data. A point cloud video according to an embodiment can include one or more frames. A frame represents a still image / picture. Therefore, a point cloud video can include point cloud images / frames / pictures and can be referred to as a point cloud image, frame, or picture.

[0091] The point cloud video encoder 10002 according to the embodiment encodes the acquired 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 embodiment may include geometry-based point cloud compression (G-PCC) coding and / or video-based point cloud compression (V-PCC) coding or next-generation coding. The point cloud compression coding according to the embodiment is not limited to the above-mentioned embodiment. The point cloud video encoder 10002 can output a bit stream containing the encoded point cloud video data. The bit stream may contain not only the encoded point cloud video data, but also signaling information related to the encoding of the point cloud video data.

[0092] The transmitter 10003 according to the embodiment transmits a bitstream containing encoded point cloud video data. The bitstream according to the embodiment is encapsulated in a file or segment (e.g., a streaming segment) and transmitted via various networks such as a broadcast network and / or a broadband network. Although not shown in the figure, the transmitting device 10000 may include an encapsulator (or encapsulation module) configured to perform an encapsulation operation. According to the embodiment, the encapsulator may be included in the transmitter 10003. According to the embodiment, the file or segment can be sent to the receiving device 10004 via a network, or stored in a digital storage medium (e.g., USB, SD, CD, DVD, Blu-ray, HDD, SSD, etc.). The transmitter 10003 according to the embodiment is capable of wired / wireless communication with the receiving device 10004 (or receiver 10005) via a 4G, 5G, 6G, etc. network. In addition, the transmitter can perform necessary data processing operations according to the network system (e.g., a 4G, 5G, or 6G communication network system). The transmitting device 10000 can send the encapsulated data on demand.

[0093] The receiving device 10004 according to an embodiment includes a receiver 10005, a point cloud video decoder 10006, and / or a renderer 10007. According to an embodiment, the receiving device 10004 may include a device configured to communicate with a base station and / or other wireless devices using a radio access technology (e.g., 5G New RAT (NR), Long Term Evolution (LTE)), a robot, a vehicle, an AR / VR / XR device, a portable device, a home appliance, an Internet of Things (IoT) device, and an AI device / server.

[0094] According to the embodiment, the receiver 10005 receives a bit stream containing point cloud video data or a file / segment in which a bit stream is encapsulated from a network or storage medium. The receiver 10005 can perform necessary data processing according to a network system (for example, a communication network system such as 4G, 5G, 6G, etc.). According to the embodiment, the receiver 10005 can decapsulate the received file / segment and output a bit stream. According to the embodiment, the receiver 10005 may include a decapsulator (or decapsulation module) configured to perform a decapsulation operation. The decapsulator can be implemented as an element (or component or module) separate from the receiver 10005.

[0095] The point cloud video decoder 10006 decodes the bitstream containing the point cloud video data. The point cloud video decoder 10006 can decode the point cloud video data according to the method used to encode the point cloud video data (e.g., in the reverse process of the operation of the point cloud video encoder 10002). Therefore, the point cloud video decoder 10006 can decode the point cloud video data by performing point cloud decompression encoding, which is the reverse process of point cloud compression. Point cloud decompression encoding includes G-PCC encoding.

[0096] The renderer 10007 renders the decoded point cloud video data. The renderer 10007 may output point cloud content by rendering not only the point cloud video data but also the audio data. Depending on the embodiment, the renderer 10007 may include a display configured to display the point cloud content. Depending on the embodiment, the display may be implemented as a separate device or component rather than being included in the renderer 10007.

[0097] The arrow indicated by the dotted line in the figure represents the transmission path of the feedback information obtained by the receiving device 10004. Feedback information is information used to reflect the interactivity with the user consuming the point cloud content, and includes information about the user (for example, head orientation information, viewport information, etc.). In particular, when the point cloud content is the content of a service that requires interaction with the user (for example, an autonomous driving service, etc.), the feedback information can be provided to the content sender (for example, the sending device 10000) and / or the service provider. Depending on the embodiment, the feedback information may be used in the receiving device 10004 and the sending device 10000, or may not be provided.

[0098] According to an embodiment, head orientation information refers to information regarding the user's head position, orientation, angle, movement, and the like. According to an embodiment, the receiving device 10004 may calculate viewport information based on the head orientation information. Viewport information may be information related to the area of ​​the point cloud video that the user is viewing. The viewpoint is the point through which the user is viewing the point cloud video and may refer to the center point of the viewport area. In other words, the viewport is the area centered on the viewpoint, and the size and shape of this area may be determined by the field of view (FOV). Therefore, in addition to head orientation information, the receiving device 10004 may also extract viewport information based on the vertical or horizontal FOV supported by the device. Furthermore, the receiving device 10004 may perform gaze analysis, etc., to examine how the user consumes the point cloud, the area of ​​the point cloud video the user gazes at, the duration of the gaze, and the like. According to an embodiment, the receiving device 10004 may transmit feedback information including the gaze analysis results to the sending device 10000. According to an embodiment, the feedback information may be obtained during the rendering and / or display process. The feedback information according to an embodiment may be protected by one or more sensors included in the receiving device 10004. According to an embodiment, the feedback information may be protected by the renderer 10007 or a separate external element (or device, component, etc.).

[0099] Figure 1 The dotted line in represents the process of sending feedback information protected by the renderer 10007. The point cloud content providing system can process (encode / decode) the point cloud data based on the feedback information. Therefore, the point cloud video decoder 10006 can perform a decoding operation based on the feedback information. The receiving device 10004 can send the feedback information to the sending device 10000. The sending device 10000 (or the point cloud video encoder 10002) can perform an encoding operation based on the feedback information. Therefore, the point cloud content providing system can efficiently process necessary data (for example, point cloud data corresponding to the user's head position) based on the feedback information instead of processing (encoding / decoding) the entire point cloud data, and provide point cloud content to the user.

[0100] According to an embodiment, the transmitting device 10000 may be referred to as an encoder, a transmitting device, a transmitter, a transmitting system, etc., and the receiving device 10004 may be referred to as a decoder, a receiving device, a receiver, a receiving system, etc.

[0101] (Through a series of processes of obtaining / encoding / sending / decoding / rendering) in accordance with the embodiment Figure 1 The point cloud data processed in the point cloud content providing system may be referred to as point cloud content data or point cloud video data. According to an embodiment, point cloud content data may be used as a concept covering metadata or signaling information related to point cloud data.

[0102] Figure 1The elements of the point cloud content providing system illustrated in can be implemented by hardware, software, a processor and / or a combination thereof.

[0103] Figure 2 is a block diagram illustrating a point cloud content providing operation according to an embodiment.

[0104] Figure 2 The block diagram shows Figure 1 Operation of the point cloud content providing system described in As described above, the point cloud content providing system can process point cloud data based on point cloud compression coding (e.g., G-PCC).

[0105] According to an embodiment, a point cloud content providing system (e.g., a point cloud sending device 10000 or a point cloud video acquisition unit 10001) can acquire a point cloud video (20000). The point cloud video is represented by a point cloud belonging to a coordinate system for representing a 3D space. According to an embodiment, a point cloud video may include a Ply (polygon file format or Stanford triangle format) file. When a 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 point geometry and / or attributes. The geometry includes the position of the point. The position of each point can be represented by parameters (e.g., the values ​​of the X, Y, and Z axes) representing a three-dimensional coordinate system (e.g., a coordinate system consisting of X, Y, and Z axes). Attributes include attributes of the point (e.g., information about the texture, color (YCbCr or RGB), reflectivity r, transparency, etc. of each point). A point has one or more attributes. For example, a point can have an attribute as a color or two attributes as color and reflectivity.

[0106] Depending on the embodiment, the geometry may be referred to as a position, geometry information, geometry data, etc., and the attribute may be referred to as an attribute, attribute information, attribute data, etc.

[0107] The point cloud content providing system (eg, the point cloud sending device 10000 or the point cloud video acquiring unit 10001 ) can protect point cloud data from information related to the acquisition process of the point cloud video (eg, depth information, color information, etc.).

[0108] According to an embodiment, a point cloud content providing system (e.g., a transmitting device 10000 or a point cloud video encoder 10002) can encode point cloud data (20001). The point cloud content providing system can encode point cloud data based on point cloud compression coding. As described above, point cloud data may include the geometric structure and attributes of the points. Therefore, the point cloud content providing system can perform geometric coding for encoding the geometric structure and output a geometric bitstream. The point cloud content providing system can perform attribute coding for encoding the attributes and output an attribute bitstream. According to an embodiment, the point cloud content providing system can perform attribute coding based on geometric coding. The geometric bitstream and the attribute bitstream according to the embodiment can be multiplexed and output as one bitstream. The bitstream according to the embodiment may also include signaling information related to geometric coding and attribute coding.

[0109] According to the embodiment, the point cloud content providing system (eg, the transmitting device 10000 or the transmitter 10003) may transmit the encoded point cloud data (20002). Figure 1 As illustrated in , the encoded point cloud data can be represented by a geometry bitstream and an attribute bitstream. In addition, the encoded point cloud data can be sent in the form of a bitstream together with signaling information related to the encoding of the point cloud data (for example, signaling information related to geometry encoding and attribute encoding). The point cloud content providing system can encapsulate the bitstream carrying the encoded point cloud data and send the bitstream in the form of a file or segment.

[0110] According to an embodiment, a point cloud content providing system (e.g., receiving device 10004 or receiver 10005) can receive a bitstream containing encoded point cloud data. In addition, the point cloud content providing system (e.g., receiving device 10004 or receiver 10005) can demultiplex the bitstream.

[0111] The point cloud content providing system (e.g., the receiving device 10004 or the point cloud video decoder 10005) can decode the encoded point cloud data (e.g., geometry bitstream, attribute bitstream) sent in the bitstream. The point cloud content providing system (e.g., the receiving device 10004 or the point cloud video decoder 10005) can decode the point cloud video data based on the signaling information related to the encoding of the point cloud video data contained in the bitstream. The point cloud content providing system (e.g., the receiving device 10004 or the point cloud video decoder 10005) can decode the geometry bitstream to reconstruct the position (geometry) of the point. The point cloud content providing system can reconstruct the attributes of the point by decoding the attribute bitstream based on the reconstructed geometry. The point cloud content providing system (e.g., the receiving device 10004 or the point cloud video decoder 10005) can reconstruct the point cloud video based on the position according to the reconstructed geometry and the decoded attributes.

[0112] According to an embodiment, a point cloud content providing system (e.g., receiving device 10004 or renderer 10007) can render the decoded point cloud data (20004). The point cloud content providing system (e.g., receiving device 10004 or renderer 10007) can use various rendering methods to render the geometric structure and attributes decoded by the decoding process. The points in the point cloud content can be rendered as vertices with a certain thickness, cubes with a specific minimum size centered at the corresponding vertex position, or circles centered at the corresponding vertex position. All or part of the rendered point cloud content is provided to the user through a display (e.g., a VR / AR display, a common display, etc.).

[0113] The point cloud content providing system (e.g., receiving device 10004) according to the embodiment can protect the feedback information (20005). The point cloud content providing system can encode and / or decode the point cloud data based on the feedback information. The feedback information and operation of the point cloud content providing system according to the embodiment are similar to those of reference 1. Figure 1 The feedback information and operations described are the same, so a detailed description thereof is omitted.

[0114] Figure 3 An exemplary process for capturing point cloud video according to an embodiment is illustrated.

[0115] Figure 3 Reference Figures 1 to 2 An exemplary point cloud video capture process for a point cloud content providing system is described.

[0116] Point cloud content includes point cloud videos (images and / or videos) representing objects and / or environments located in various 3D spaces (e.g., 3D spaces representing real environments, 3D spaces representing virtual environments, etc.). Therefore, a point cloud content providing system according to an embodiment may use one or more cameras (e.g., an infrared camera capable of protecting depth information, an RGB camera capable of extracting color information corresponding to the depth information, etc.), a projector (e.g., an infrared pattern projector for protecting depth information), LiDRA, etc. to capture point cloud videos. A point cloud content providing system according to an embodiment may extract the shape of a geometric structure composed of points in the 3D space from the depth information, and extract the attributes of each point from the color information to protect the point cloud data. Images and / or videos according to an embodiment may be captured based on at least one of an inward-facing technology and an outward-facing technology.

[0117] Figure 3The left portion of FIG illustrates an inward-facing technique. An inward-facing technique refers to a technique in which an image of a central object is captured using one or more cameras (or camera sensors) positioned around the central object. The inward-facing technique can be used to generate point cloud content that provides a 360-degree image of a key object to a user (e.g., VR / AR content that provides a 360-degree image of an object (e.g., a key object such as a character, player, object, or actor) to a user).

[0118] Figure 3 The right portion of the figure illustrates an outward-facing technique. Outward-facing techniques are techniques that use one or more cameras (or camera sensors) positioned around a central object to capture the environment surrounding the central object, rather than an image of the central object. Outward-facing techniques can be used to generate point cloud content that provides a user's perspective of the surrounding environment (e.g., content representing the external environment that can be provided to a user of an autonomous vehicle).

[0119] like Figure 3 As shown in , point cloud content can be generated based on the capture operation of one or more cameras. In this case, the coordinate system is different in each camera, so the point cloud content providing system can calibrate one or more cameras to set the global coordinate system before the capture operation. In addition, the point cloud content providing system can generate point cloud content by synthesizing arbitrary images and / or videos with images and / or videos captured by the above-mentioned capture technology. The point cloud content providing system cannot perform Figure 3 The point cloud content providing system according to an embodiment may perform post-processing on the captured image and / or video. In other words, the point cloud content providing system may remove unnecessary areas (e.g., background), identify the space to which the captured image and / or video is connected, and perform an operation of filling the space hole when there is a space hole.

[0120] The point cloud content providing system can generate a piece of point cloud content by performing coordinate transformation on the points of the point cloud video captured by each camera. The point cloud content providing system can perform coordinate transformation on the points based on the coordinates of each camera position. Therefore, the point cloud content providing system can generate content representing a wide range or point cloud content with a high density of points.

[0121] Figure 4 An exemplary point cloud video encoder according to an embodiment is illustrated.

[0122] Figure 4 Shown Figure 1An example of a point cloud video encoder 10002 is provided. The point cloud video encoder reconstructs and encodes point cloud data (e.g., the position and / or attributes of a point) to adjust the quality of the point cloud content (e.g., lossless, lossy, or near-lossless) according to network conditions or applications. When the total size of the point cloud content is large (e.g., for 30fps, 60Gbps of point cloud content is given), the point cloud content providing system may not be able to stream the content in real time. Therefore, the point cloud content providing system can reconstruct the point cloud content based on the maximum target bit rate to provide the point cloud content according to the network environment, etc.

[0123] As reference Figures 1 to 2 As described above, the point cloud video encoder can perform both geometry encoding and attribute encoding. Geometry encoding is performed before attribute encoding.

[0124] According to an embodiment, the point cloud video encoder includes a coordinate transformation unit 40000, a quantization unit 40001, an octree analysis unit 40002, a surface approximation analysis unit 40003, an arithmetic encoder 40004, a geometric reconstruction unit 40005, a color transformation unit 40006, an attribute transformation unit 40007, a RAHT unit 40008, an LOD generation unit 40009, a lifting transformation unit 40010, a coefficient quantization unit 40011 and / or an arithmetic encoder 40012.

[0125] The coordinate transformation unit 40000, the quantization unit 40001, the octree analysis unit 40002, the surface approximation analysis unit 40003, the arithmetic encoder 40004, and the geometric reconstruction unit 40005 may perform geometric coding. Geometric coding according to the embodiment may include octree geometric coding, direct coding, trisoup geometric coding, and entropy coding. Direct coding and trisoup geometric coding are applied selectively or in combination. Geometric coding is not limited to the above examples.

[0126] As shown in the figure, the coordinate transformation unit 40000 according to an embodiment receives a position and transforms it into coordinates. For example, the position can be transformed into position information in a three-dimensional space (e.g., a three-dimensional space represented by an XYZ coordinate system). The position information in the three-dimensional space according to an embodiment can be referred to as geometric information.

[0127] The quantization unit 40001 according to the embodiment quantizes the geometric information. For example, the quantization unit 40001 can quantize the points based on the minimum position value of all points (e.g., the minimum value on each of the X, Y, and Z axes). The quantization unit 40001 performs the following quantization operation: the difference between the position value of each point and the minimum position value is multiplied by a preset quantization scaling value, and then the nearest integer value is found by rounding the value obtained by the multiplication. Therefore, one or more points can have the same quantized position (or position value). The quantization unit 40001 according to the embodiment performs voxelization based on the quantized position to reconstruct the quantized point. Voxelization means the smallest unit representing position information in 3D space. The points of the point cloud content (or 3D point cloud video) according to the embodiment can be included in one or more voxels. The term voxel, which is a compound word of volume and pixel, refers to the 3D cubic space generated when the 3D space is divided into units (unit = 1.0) based on the axes representing the 3D space (e.g., X-axis, Y-axis, and Z-axis). The quantization unit 40001 can match groups of points in 3D space to voxels. Depending on the embodiment, a voxel may include only one point. Depending on the embodiment, a voxel may include one or more points. To represent a voxel as a point, the position of the voxel's center point can be set based on the positions of the one or more points included in the voxel. In this case, the attributes of all positions included in a voxel can be combined and assigned to the voxel.

[0128] The octree analysis unit 40002 according to the embodiment performs octree geometry encoding (or octree encoding) to represent voxels in an octree structure. The octree structure represents points matched to voxels based on the octree structure.

[0129] The surface approximation analysis unit 40003 according to an embodiment may analyze and approximate an octree. The octree analysis and approximation according to an embodiment is a process of analyzing a region including a plurality of points to efficiently provide an octree and voxelization.

[0130] According to an embodiment, the arithmetic encoder 40004 performs entropy coding on the octree and / or the approximate octree. For example, the coding scheme includes arithmetic coding. As a result of the coding, a geometry bitstream is generated.

[0131] The color transform unit 40006, the attribute transform unit 40007, the RAHT unit 40008, the LOD generation unit 40009, the lifting transform unit 40010, the coefficient quantization unit 40011, and / or the arithmetic encoder 40012 perform attribute coding. As described above, a point can have one or more attributes. Attribute coding according to an embodiment is also applied to the attributes of a point. However, when an attribute (e.g., color) includes one or more elements, attribute coding is applied independently to each element. Attribute coding according to an embodiment includes color transform coding, attribute transform coding, region adaptive hierarchical transform (RAHT) coding, interpolation-based hierarchical nearest neighbor prediction (prediction transform) coding, and interpolation-based hierarchical nearest neighbor prediction coding with an update / lifting step (lifting transform). Depending on the point cloud content, the above-mentioned RAHT coding, prediction transform coding, and lifting transform coding can be selectively used, or a combination of one or more coding schemes can be used. Attribute coding according to an embodiment is not limited to the above examples.

[0132] The color conversion unit 40006 according to an embodiment performs color conversion encoding of the color values ​​(or textures) included in the attributes. For example, the color conversion unit 40006 can convert the format of the color information (e.g., from RGB to YCbCr). The operation of the color conversion unit 40006 according to an embodiment can be selectively applied based on the color values ​​included in the attributes.

[0133] The geometry reconstruction unit 40005 according to an embodiment reconstructs (decompresses) an octree and / or an approximate octree. The geometry reconstruction unit 40005 reconstructs the octree / voxel based on the result of analyzing the distribution of the points. The reconstructed octree / voxel can be referred to as a reconstructed geometry structure (restored geometry structure).

[0134] The attribute transformation unit 40007 according to the embodiment performs attribute transformation to transform the attribute based on the position and / or the reconstructed geometry for which geometric coding is not performed. As described above, since the attribute depends on the geometry, the attribute transformation unit 40007 can transform the attribute based on the reconstructed geometry information. For example, based on the position value of the point included in the voxel, the attribute transformation unit 40007 can transform the attribute of the point at the position. As described above, when the position of the voxel center is set based on the position of one or more points included in the voxel, the attribute transformation unit 40007 transforms the attributes of the one or more points. When triangulated geometry coding is performed, the attribute transformation unit 40007 can transform the attribute based on triangulated geometry coding.

[0135] The attribute conversion unit 40007 can perform attribute conversion by calculating the average value of the attributes or attribute values ​​(e.g., the color or reflectance of each point) of neighboring points within a specific position / radius from the position (or position value) of the center of each voxel. The attribute conversion unit 40007 can apply a weight based on the distance from the center to each point when calculating the average value. Therefore, each voxel has a position and a calculated attribute (or attribute value).

[0136] The attribute transformation unit 40007 can search for neighbor points within a specific position / radius from the center of each voxel based on a KD tree or a Morton code. The KD tree is a binary search tree and supports the ability to manage point data structures based on position so that a nearest neighbor search (NNS) can be performed quickly. The Morton code is generated by presenting the coordinates (e.g., (x, y, z)) representing the 3D positions of all points as bit values ​​and mixing the bits. For example, when the coordinates representing the position of the point are (5, 9, 1), the bit values ​​of the coordinates are (0101, 1001, 0001). Mixing the bit values ​​according to the bit index in the order of z, y, and x produces 010001000111. This value is represented as a decimal number 1095. That is, the Morton code value of the point with coordinates (5, 9, 1) is 1095. The attribute transformation unit 40007 can sort the points based on the Morton code value and perform NNS by depth-first traversal processing. After the attribute transform operation, when NNS is required in another transform process for attribute encoding, a KD tree or Morton code is used.

[0137] As shown in the figure, the transformed attributes are input to the RAHT unit 40008 and / or the LOD generation unit 40009.

[0138] The RAHT unit 40008 according to an embodiment performs RAHT encoding for predicting attribute information based on the reconstructed geometric information. For example, the RAHT unit 40008 may predict attribute information of a higher-level node in the octree based on attribute information associated with a lower-level node in the octree.

[0139] According to an embodiment, the LOD generation unit 40009 generates a level of detail (LOD). According to an embodiment, the LOD is the level of detail of the point cloud content. As the LOD value decreases, the level of detail of the point cloud content decreases. As the LOD value increases, the detail of the point cloud content increases. Points can be classified according to the LOD.

[0140] The lifting transform unit 40010 according to an embodiment performs lifting transform coding for transforming attributes of a point cloud based on weights. As described above, lifting transform coding may be optionally applied.

[0141] The coefficient quantization unit 40011 according to the embodiment quantizes the attribute after attribute encoding based on the coefficient.

[0142] The arithmetic encoder 40012 according to the embodiment encodes the quantized attributes based on arithmetic coding.

[0143] Although not shown in this figure, Figure 4 The elements of the point cloud video encoder may be implemented by hardware, software, firmware, or a combination thereof including one or more processors or integrated circuits configured to communicate with one or more memories included in the point cloud content providing device. The one or more processors may execute the above Figure 4 At least one of the operations and / or functions of the elements of the point cloud video encoder. In addition, one or more processors can operate or execute a set of software programs and / or instructions to perform Figure 4 The operation and / or functionality of the elements of the point cloud video encoder. According to an embodiment, one or more memories may include high-speed random access memory, or include non-volatile memory (e.g., one or more disk storage devices, flash memory devices, or other non-volatile solid-state storage devices).

[0144] Figure 5 An example of a voxel according to an embodiment is shown.

[0145] Figure 5 , which is a voxel located in a 3D space represented by a coordinate system consisting of three axes, namely, an X-axis, a Y-axis, and a Z-axis. Figure 4 As described, the point cloud video encoder (e.g., quantization unit 40001) can perform voxelization. A voxel refers to a 3D cubic space generated when a 3D space is divided into units (unit=1.0) based on axes representing the 3D space (e.g., X-axis, Y-axis, and Z-axis). Figure 5 An example of voxels generated by an octree structure is shown, in which the voxels are generated by two extreme points (0, 0, 0) and (2 d ,2 d ,2 d ) is recursively subdivided. A voxel consists of at least one point. The spatial coordinates of a voxel can be estimated based on its positional relationship with the voxel group. As mentioned above, a voxel has properties like a pixel of a 2D image / video (such as color or reflectivity). The details of the voxel are similar to those of the reference Figure 4 The details described are the same, so their description is omitted.

[0146] Figure 6 Examples of octrees and occupancy codes are shown according to an embodiment.

[0147] As reference Figures 1 to 4As described, the point cloud content providing system (point cloud video encoder 10002) or the octree analysis unit 40002 of the point cloud video encoder performs octree geometry encoding (or octree encoding) based on the octree structure to efficiently manage the area and / or position of voxels.

[0148] Figure 6 The upper part of FIG shows an octree structure. The 3D space of the point cloud content according to the embodiment is represented by the axes of the coordinate system (e.g., X axis, Y axis, and Z axis). The octree structure is formed by recursively subdividing the two poles (0, 0, 0) and (2 d ,2 d ,2 d ) is created by defining the cubic axis-aligned bounding box. Here, 2 d It can be set to the value of the minimum bounding box that forms all points around the point cloud content (or point cloud video). Here, d represents the depth of the octree. The value of d is determined in Formula 1. In Formula 1, (x int n ,y int n ,z int n ) represents the position (or position value) of the quantization point.

[0149] [Formula 1]

[0150]

[0151] like Figure 6 As shown in the middle of the upper part, the entire 3D space can be divided into eight spaces according to the partition. Each partition space is represented by a cube with six faces. Figure 6 As shown in the upper right side of the octree, each of the eight spaces is further divided based on the axes of the coordinate system (e.g., X-axis, Y-axis, and Z-axis). Thus, each space is divided into eight smaller spaces. The divided smaller spaces are also represented by cubes with six faces. This division scheme is applied until the leaf nodes of the octree become voxels.

[0152] Figure 6 The lower part shows the octree occupancy code. The occupancy code of the octree is generated to indicate whether each of the eight divided spaces generated by dividing one space contains at least one point. Therefore, a single occupancy code is represented by eight child nodes. Each child node represents the occupancy of the divided space, and the child node has a 1-bit value. Therefore, the occupancy code is represented as an 8-bit code. That is, when at least one point is contained in the space corresponding to the child node, the node is assigned a value of 1. When no point is contained in the space corresponding to the child node (the space is empty), the node is assigned a value of 0. Since Figure 6The occupancy code shown in is 00100001, so it indicates that the spaces corresponding to the third child node and the eighth child node among the eight child nodes each contain at least one point. As shown in the figure, each of the third child node and the eighth child node has 8 child nodes, and the child nodes are represented by an 8-bit occupancy code. The figure shows that the occupancy code of the third child node is 10000111, and the occupancy code of the eighth child node is 01001111. The point cloud video encoder according to the embodiment (e.g., the arithmetic encoder 40004) can perform entropy coding on the occupancy code. In order to improve compression efficiency, the point cloud video encoder can perform intra-frame / inter-frame coding on the occupancy code. The receiving device according to the embodiment (e.g., the receiving device 10004 or the point cloud video decoder 10006) reconstructs the octree based on the occupancy code.

[0153] The point cloud video encoder (e.g., octree analysis unit 40002) according to an embodiment can perform voxelization and octree encoding to store the positions of points. However, points are not always evenly distributed in 3D space, so there are specific areas where there are fewer points. Therefore, performing voxelization on the entire 3D space is inefficient. For example, when a specific area contains fewer points, it is not necessary to perform voxelization in the specific area.

[0154] Therefore, for the above-mentioned specific area (or nodes other than the leaf nodes of the octree), the point cloud video encoder according to the embodiment can skip voxelization and perform direct encoding to directly encode the positions of the points included in the specific area. The coordinates of the directly encoded points according to the embodiment are called direct coding mode (DCM). The point cloud video encoder according to the embodiment can also perform triangulation geometry coding based on the surface model to reconstruct the positions of the points in the specific area (or node) based on voxels. Triangulation geometry coding is a geometry coding that represents an object as a series of triangular meshes. Therefore, the point cloud video decoder can generate a point cloud from the mesh surface. Triangulation geometry coding and direct encoding according to the embodiment can be performed selectively. In addition, triangulation geometry coding and direct encoding according to the embodiment can be performed in combination with octree geometry coding (or octree coding).

[0155] In order to perform direct encoding, the option to use direct mode to apply direct encoding should be enabled. The node to which direct encoding is to be applied is not a leaf node, and there should be fewer than a threshold number of points within a specific node. In addition, the total number of points to which direct encoding is to be applied should not exceed a preset threshold. When the above conditions are met, the point cloud video encoder (or arithmetic encoder 40004) according to the embodiment can perform entropy encoding on the position (or position value) of the point.

[0156] A point cloud video encoder according to an embodiment (e.g., a surface approximation analysis unit 40003) may determine a specific level of the octree (a level less than the depth d of the octree), and may perform triangulated geometry encoding using a surface model starting from that level to reconstruct the positions of points in the region of the node based on voxels (triangulated mode). A point cloud video encoder according to an embodiment may specify the level at which triangulated geometry encoding is to be applied. For example, when the specific level is equal to the depth of the octree, the point cloud video encoder does not operate in triangulated mode. In other words, the point cloud video encoder according to an embodiment may operate in triangulated mode only when the specified level is less than the depth value of the octree. A 3D cubic area of ​​a node at a specified level according to an embodiment is referred to as a block. A block may include one or more voxels. A block or voxel may correspond to a brick. The geometric structure is represented as a surface within each block. A surface according to an embodiment may intersect each edge of a block at most once.

[0157] A block has 12 edges, so there are at least 12 intersections in a block. Each intersection is called a vertex (or top point). A vertex along an edge is detected when there is at least one occupied voxel adjacent to the edge among all blocks that share the edge. An occupied voxel according to an embodiment refers to a voxel containing a point. The position of a vertex detected along an edge is the average position of the edge of all voxels adjacent to the edge among all blocks that share the edge.

[0158] Once the vertex is detected, the point cloud video encoder according to the embodiment can perform entropy coding on the starting point (x, y, z) of the edge, the direction vector (Δx, Δy, Δz) of the edge, and the vertex position value (relative position value within the edge). When triangle soup geometry coding is applied, the point cloud video encoder according to the embodiment (e.g., the geometry reconstruction unit 40005) can generate a restored geometry structure (reconstructed geometry structure) by performing triangle reconstruction, upsampling, and voxelization.

[0159] Vertices at the edges of a block determine a surface that passes through the block. The surface according to an embodiment is a non-planar polygon. In a triangle reconstruction process, a surface represented by a triangle is reconstructed based on the starting point of the edge, the direction vector of the edge, and the position values ​​of the vertices. According to Equation 2, the triangle reconstruction process is performed by: i) calculating the centroid value of each vertex, ii) subtracting the center value from each vertex value, and iii) estimating the sum of the squares of the values ​​obtained by the subtraction.

[0160] [Formula 2]

[0161]

[0162] Then, the minimum value of the sum is estimated, and the projection process is performed according to the axis with the minimum value. For example, when the element x is the smallest, each vertex is projected onto the x-axis relative to the center of the block and projected onto the (y, z) plane. When the value obtained by projecting onto the (y, z) plane is (ai, bi), the value of θ is estimated by atan2(bi, ai), and the vertices are sorted according to the value of θ. Table 1 below shows the vertex combination for creating a triangle according to the number of vertices. The vertices are sorted from 1 to n. Table 1 below shows that for four vertices, two triangles can be constructed according to the combination of vertices. The first triangle can be composed of vertices 1, 2, and 3 among the sorted vertices, and the second triangle can be composed of vertices 3, 4, and 1 among the sorted vertices.

[0163] Table 1. Triangles formed by vertices sorted in order 1,…,n [Table 1]

[0164]

[0165] An upsampling process is performed to add points in the middle along the sides of the triangle and perform voxelization. The added points are generated based on the upsampling factor and the width of the block. The added points are called refinement vertices. The point cloud video encoder according to an embodiment can voxelize the refinement vertices. In addition, the point cloud video encoder can perform attribute encoding based on the voxelized positions (or position values). Figure 7 An example of a neighbor node pattern according to an embodiment is illustrated. In order to improve the compression efficiency of a point cloud video, a point cloud video encoder according to an embodiment may perform entropy coding based on context-adaptive arithmetic coding.

[0166] As reference Figures 1 to 6 Described, Figure 1 Point cloud content providing system or point cloud video encoder 10002 or Figure 4 The point cloud video encoder or arithmetic encoder 40004 can immediately perform entropy coding on the occupancy code. In addition, the point cloud content providing system or the point cloud video encoder can perform entropy coding (intra-frame coding) based on the occupancy code of the current node and the occupancy of the neighboring nodes, or perform entropy coding (inter-frame coding) based on the occupancy code of the previous frame. The frame representation according to the embodiment is a collection of point cloud videos generated simultaneously. The compression efficiency of the intra-frame coding / inter-frame coding according to the embodiment may depend on the number of neighboring nodes being referenced. When the number of bits increases, the operation becomes complicated, but the coding can be biased to one side, which can increase the compression efficiency. For example, when a 3-bit context is given, 2 bits need to be used. 3 = 8 ways to perform encoding. The division into parts for encoding affects the complexity of the implementation. Therefore, an appropriate level of compression efficiency and complexity must be met.

[0167] Figure 7The present invention illustrates a process for obtaining an occupancy pattern based on the occupancy of neighboring nodes. A point cloud video encoder according to an embodiment determines the occupancy of neighboring nodes for each node in an octree and obtains a value of a neighbor pattern. The neighbor pattern is used to infer the occupancy pattern of the node. Figure 7 The top portion of the diagram shows the cube corresponding to the node (the middle cube) and six cubes that share at least one face with the cube (neighboring nodes). The nodes shown in the diagram are at the same depth. The numbers shown in the diagram represent the weights associated with the six nodes (1, 2, 4, 8, 16, and 32), respectively. The weights are assigned sequentially based on the positions of the neighboring nodes.

[0168] Figure 7 The lower part shows the neighbor node pattern value. The neighbor node pattern value is the sum of the values ​​multiplied by the weights of the occupied neighbor nodes (neighbor nodes with points). Therefore, the neighbor node pattern value is 0 to 63. When the neighbor node pattern value is 0, it indicates that there is no node with a point (unoccupied node) among the neighbor nodes of the node. When the neighbor node pattern value is 63, it indicates that all neighbor nodes are occupied nodes. As shown in the figure, since the neighbor nodes assigned weights 1, 2, 4 and 8 are occupied nodes, the neighbor node pattern value is 15, which is the sum of 1, 2, 4 and 8. The point cloud video encoder can perform encoding according to the neighbor node pattern value (for example, when the neighbor node pattern value is 63, 64 types of encoding can be performed). According to an embodiment, the point cloud video encoder can reduce the encoding complexity by changing the neighbor node pattern value (for example, based on a table through which 64 is changed to 10 or 6).

[0169] Figure 8 An example of point configuration in each LOD according to an embodiment is illustrated.

[0170] As reference Figures 1 to 7 As described above, before performing attribute encoding, the coded geometry is reconstructed (decompressed). When direct encoding is applied, the geometry reconstruction operation may include changing the placement of directly coded points (e.g., placing directly coded points in front of the point cloud data). When triangulated geometry encoding is applied, the geometry reconstruction process is performed by triangle reconstruction, upsampling, and voxelization. Since the attributes depend on the geometry, attribute encoding is performed based on the reconstructed geometry.

[0171] The point cloud video encoder (e.g., LOD generation unit 40009) can classify (reorganize or group) points by LOD. Figure 8 The point cloud content corresponding to the LOD is shown. Figure 8 The leftmost image in the figure shows the original point cloud content. Figure 8 The second picture from the left shows the distribution of points in the lowest LOD, and Figure 8The rightmost picture in the figure shows the distribution of points in the highest LOD. That is, the points in the lowest LOD are sparsely distributed, and the points in the highest LOD are densely distributed. That is, as the LOD increases by Figure 8 As you go up in the direction indicated by the arrow at the bottom, the space (or distance) between the points becomes narrower.

[0172] Figure 9 An example of point configuration for each LOD according to an embodiment is illustrated.

[0173] As reference Figures 1 to 8 As described, a point cloud content providing system or a point cloud video encoder (e.g., Figure 1 Point cloud video encoder 10002, Figure 4 The point cloud video encoder or LOD generation unit 40009 can generate LOD. 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 not only by the point cloud video encoder but also by the point cloud video decoder.

[0174] Figure 9 The upper part of shows examples of points (P0 to P9) of point cloud contents distributed in 3D space. Figure 9 In , the original order means the order of points P0 to P9 before LOD generation. Figure 9 In the example, the LOD-based order indicates the order of points generated according to the LOD. Points are reorganized by LOD. In addition, a high LOD contains points belonging to a lower LOD. Figure 9 As shown in the figure, LOD0 contains P0, P5, P4, and P2. LOD1 contains the points of LOD0, P1, P6, and P3. LOD2 contains the points of LOD0, the points of LOD1, P9, P8, and P7.

[0175] As reference Figure 4 As described, the point cloud video encoder according to the embodiment may selectively or in combination perform LOD-based prediction transform coding, LOD-based lifting transform coding, and RAHT transform coding.

[0176] The point cloud video encoder according to an embodiment can generate a predictor for each point to perform LOD-based predictive transform coding to set the prediction attribute (or prediction attribute value) of each point. That is, N predictors can be generated for N points. The predictor according to an embodiment can calculate a weight (=1 / distance) based on the LOD value of each point, index information about neighboring points within a set distance for each LOD, and the distance to the neighboring point.

[0177] The predicted attribute (or attribute value) according to the embodiment is set to the average value of the value obtained by multiplying the attribute (or attribute value) of the neighboring point set in the predictor of each point (for example, color, reflectivity, etc.) by the weight (or weight value) calculated based on the distance to each neighboring point. The point cloud video encoder (for example, the coefficient quantization unit 40011) according to the embodiment can quantize and dequantize the residual (which can be called residual attribute, residual attribute value, attribute prediction residual value or prediction error attribute value, etc.) of each point obtained by subtracting the predicted attribute (or attribute value) of each point from the attribute (that is, the original attribute value) of each point. The quantization processing performed on the residual attribute value in the transmitting device is configured as shown in Table 2. The dequantization processing performed on the residual attribute value in the receiving device is configured as shown in Table 3.

[0178] [Table 2]

[0179]

[0180] [Table 3]

[0181]

[0182] When the predictor of each point has neighboring points, the point cloud video encoder (e.g., the arithmetic encoder 40012) according to the embodiment can perform entropy coding on the residual attribute value after quantization and inverse quantization as described above. When the predictor of each point has no neighboring points, the point cloud video encoder (e.g., the arithmetic encoder 40012) according to the embodiment can perform entropy coding on the attribute of the corresponding point without performing the above operation. The point cloud video encoder (e.g., the lifting transform unit 40010) according to the embodiment can generate a predictor for each point, set the calculated LOD and register the neighboring points in the predictor, and set weights according to the distance to the neighboring points to perform lifting transform coding. The lifting transform coding according to the embodiment is similar to the above-mentioned predictive transform coding, but is different in that the weights are cumulatively applied to the attribute values. The process of cumulatively applying weights to the attribute values ​​according to the embodiment is configured as follows.

[0183] 1) Create an array, Quantization Weight (QW), to store the weight values ​​for each point. All elements of QW are initially set to 1.0. Multiply the QW values ​​of the predictor indexes of the neighboring nodes registered in the predictor by the weight of the predictor for the current point, and add the resulting values.

[0184] 2) Boosting prediction processing: A value obtained by multiplying the attribute value of a point by a weight is subtracted from the existing attribute value to calculate a predicted attribute value.

[0185] 3) Create a temporary array called updateweight, update the temporary array and initialize it to zero.

[0186] 4) The weight calculated by multiplying the weight calculated for all predictors by the weight corresponding to the predictor index stored in QW is cumulatively added to the update weight array as the index of the neighbor node. The value obtained by multiplying the attribute value of the index of the neighbor node by the calculated weight is cumulatively added to the update array.

[0187] 5) Boosting update processing: Divide the attribute value of the update array for all predictors by the weight value of the update weight array of the predictor index, and add the existing attribute value to the value obtained by the division.

[0188] 6) The predicted attribute is calculated by multiplying the attribute value updated by the lifting update process by the weight updated by the lifting prediction process (stored in the QW) for all predictors. The point cloud video encoder (e.g., the coefficient quantization unit 40011) according to the embodiment quantizes the predicted attribute value. In addition, the point cloud video encoder (e.g., the arithmetic encoder 40012) performs entropy coding on the quantized attribute value.

[0189] A point cloud video encoder according to an embodiment (e.g., RAHT unit 40008) can perform RAHT transform coding, in which attributes associated with nodes at a lower level in an octree are used to predict attributes of nodes at a higher level. RAHT transform coding is an example of attribute intra-frame coding performed by backward scanning of an octree. A point cloud video encoder according to an embodiment scans the entire area from voxels and repeats a merging process of merging voxels into larger blocks in each step until a root node is reached. The merging process according to an embodiment is performed only on occupied nodes. The merging process is not performed on empty nodes. The merging process is performed in a higher mode just above an empty node.

[0190] The following formula 3 represents the RAHT transformation matrix. In formula 3, Represents the average attribute value of the voxels at level l. It can be based on and To calculate and The weight is and

[0191] [Formula 3]

[0192]

[0193] Here, θ l-1x，y，z is the low-pass value and is used in the merging process at the next higher level. Denotes the high-pass coefficient. The high-pass coefficient in each step is quantized and undergoes entropy coding (e.g., encoded by the arithmetic encoder 40012). The weight is calculated as As shown in formula 4, and Calculate the root node.

[0194] [Formula 4]

[0195]

[0196] The value of gDC is also quantized and undergoes entropy coding like the high-pass coefficients.

[0197] Figure 10 A point cloud video decoder according to an embodiment is illustrated.

[0198] Figure 10 The point cloud video decoder illustrated in Figure 1 An example of a point cloud video decoder 10006 is described in Figure 1 The operations of the point cloud video decoder 10006 illustrated in FIG. 10006 are the same as or similar to those of the point cloud video decoder 10006 illustrated in FIG. 10006 . As shown in the figure, the point cloud video decoder can receive a geometry bitstream and an attribute bitstream contained in one or more bitstreams. The point cloud video decoder includes a geometry decoder and an attribute decoder. The geometry decoder performs geometry decoding on the geometry bitstream and outputs a decoded geometry structure. The attribute decoder performs attribute decoding on the attribute bitstream based on the decoded geometry and outputs a decoded attribute. The decoded geometry structure and the decoded attributes are used to reconstruct the point cloud content (decoded point cloud).

[0199] Figure 11 A point cloud video decoder according to an embodiment is illustrated.

[0200] Figure 11 The point cloud video decoder illustrated in Figure 10 An example of a point cloud video decoder is illustrated in , and can be executed as Figures 1 to 9 The decoding operation is the inverse of the encoding operation of the point cloud video encoder illustrated in FIG.

[0201] As reference Figure 1 and Figure 10 As described above, the point cloud video decoder can perform both geometry decoding and attribute decoding. Geometry decoding is performed before attribute decoding.

[0202] According to an embodiment, the point cloud video decoder includes an arithmetic decoder (arithmetic decoding) 11000, an octree synthesizer (synthesized octree) 11001, a surface approximation synthesizer (synthesized surface approximation) 11002 and a geometry reconstruction unit (reconstruction geometry) 11003, an inverse coordinate transformer (inverse transformation coordinates) 11004, an arithmetic decoder (arithmetic decoding) 11005, an inverse quantization unit (inverse quantization) 11006, a RAHT transformer 11007, an LOD generator (generate LOD) 11008, an inverse lifting unit (inverse lifting) 11009 and / or an inverse color transformation unit (inverse transformation color) 11010.

[0203] The arithmetic decoder 11000, the octree synthesizer 11001, the surface approximation synthesizer 11002, the geometric reconstruction unit 11003, and the coordinate inverse transformer 11004 can perform geometric decoding. The geometric decoding according to the embodiment may include direct decoding and triangular soup geometric decoding. Direct decoding and triangular soup geometric decoding are selectively applied. Geometric decoding is not limited to the above example, and is used as a reference. Figures 1 to 9 This is performed by inverse processing of the geometric encoding described.

[0204] The arithmetic decoder 11000 according to the embodiment decodes the received geometry bitstream based on arithmetic coding. The operation of the arithmetic decoder 11000 corresponds to the inverse process of the arithmetic encoder 40004.

[0205] The octree synthesizer 11001 according to the embodiment can generate an octree by acquiring an occupancy code from the decoded geometry bitstream (or information about the geometry structure protected as a result of decoding). Figures 1 to 9 Configure the seizure code in detail.

[0206] When trisoup geometry encoding is applied, the surface approximation synthesizer 11002 according to an embodiment may synthesize a surface based on the decoded geometry and / or the generated octree.

[0207] According to an embodiment, the geometry reconstruction unit 11003 can regenerate the geometry based on the surface and / or the decoded geometry. Figures 1 to 9 As described, direct coding and triangulated geometry coding are selectively applied. Therefore, the geometry reconstruction unit 11003 directly imports and adds position information about the points to which direct coding is applied. When triangulated geometry coding is applied, the geometry reconstruction unit 11003 can reconstruct the geometry by performing the reconstruction operations (e.g., triangulated reconstruction, upsampling, and voxelization) of the geometry reconstruction unit 40005. Details and References Figure 6 The details of the description are the same, so the description thereof is omitted. The reconstructed geometry may include a point cloud image or frame that does not contain attributes.

[0208] The coordinate inverse transformer 11004 according to an embodiment may acquire the position of a point by transforming the coordinates based on the reconstructed geometric structure.

[0209] The arithmetic decoder 11005, the inverse quantization unit 11006, the RAHT transformer 11007, the LOD generation unit 11008, the inverse lifter 11009 and / or the inverse color transform unit 11010 may perform a reference Figure 10 Attribute decoding described. Attribute decoding according to an embodiment includes region adaptive hierarchical transform (RAHT) decoding, interpolation-based hierarchical nearest neighbor prediction (prediction transform) decoding, and interpolation-based hierarchical nearest neighbor prediction decoding with an update / lifting step (lifting transform). The above three decoding schemes can be selectively used, or a combination of one or more decoding schemes can be used. Attribute decoding according to an embodiment is not limited to the above examples.

[0210] The arithmetic decoder 11005 according to the embodiment decodes the attribute bit stream through arithmetic coding.

[0211] The inverse quantization unit 11006 according to an embodiment inversely quantizes the information about the decoded attribute bitstream or attribute protected as a decoding result and outputs the inversely quantized attribute (or attribute value). Inverse quantization can be selectively applied based on the attribute encoding of the point cloud video encoder.

[0212] According to an embodiment, the RAHT transformer 11007, the LOD generation unit 11008, and / or the inverse lifter 11009 may process the reconstructed geometry and the inverse quantized attributes. As described above, the RAHT transformer 11007, the LOD generation unit 11008, and / or the inverse lifter 11009 may selectively perform a decoding operation corresponding to the encoding of the point cloud video encoder.

[0213] The inverse color conversion unit 11010 according to the embodiment performs inverse transform encoding to inversely transform the color value (or texture) included in the decoded attribute. The operation of the inverse color conversion unit 11010 can be selectively performed based on the operation of the color conversion unit 40006 of the point cloud video encoder.

[0214] Although not shown in this figure, Figure 11 The elements of the point cloud video decoder can be implemented by hardware, software, firmware or a combination thereof including one or more processors or integrated circuits configured to communicate with one or more memories included in the point cloud content providing device. The one or more processors can perform the above Figure 11At least one or more of the operations and / or functions of the elements of the point cloud video decoder. In addition, one or more processors can operate or execute a set of software programs and / or instructions to perform Figure 11 The operation and / or functionality of the elements of the point cloud video decoder.

[0215] Figure 12 A transmitting device according to an embodiment is illustrated.

[0216] Figure 12 The sending device shown in Figure 1 The sending device 10000 (or Figure 4 An example of a point cloud video encoder). Figure 12 The sending device illustrated in the example can execute the same Figures 1 to 9 The operations and methods of the point cloud video encoder described herein are the same as or similar to one or more of the operations and methods. The transmitting apparatus according to the embodiment may include a data input unit 12000, a quantization processor 12001, a voxelization processor 12002, an octree occupancy code generator 12003, a surface model processor 12004, an intra / inter encoding processor 12005, an arithmetic encoder 12006, a metadata processor 12007, a color transform processor 12008, an attribute transform processor 12009, an LOD / lifting / RAHT transform processor 12010, an arithmetic encoder 12011 and / or a transmitting processor 12012.

[0217] The data input unit 12000 according to the embodiment receives or acquires point cloud data. The data input unit 12000 can perform the same operation and / or acquisition method as the point cloud video acquisition unit 10001 (or refer to Figure 2 The acquisition process 20000) described above has the same or similar operations and / or acquisition methods.

[0218] The data input unit 12000, the quantization processor 12001, the voxelization processor 12002, the octree occupancy code generator 12003, the surface model processor 12004, the intra / inter coding processor 12005 and the arithmetic encoder 12006 perform geometric coding. Figures 1 to 9 The geometric encoding described is the same or similar, so a detailed description thereof is omitted.

[0219] The quantization processor 12001 according to the embodiment quantizes the geometric structure (e.g., the position value of the point). The operation and / or quantization of the quantization processor 12001 are related to the reference Figure 4 The operation and / or quantization of the quantization unit 40001 described above are the same or similar. Figures 1 to 9 The details of the description are the same.

[0220] The voxelization processor 12002 according to the embodiment performs voxelization on the quantized position value of the point. The voxelization processor 12002 may perform the same as the reference. Figure 4 The operation and / or voxelization process of the quantization unit 40001 described above is the same as or similar to the operation and / or process described above. Figures 1 to 9 The details of the description are the same.

[0221] The octree occupancy code generator 12003 according to the embodiment performs octree encoding on the voxelized position of the point based on the octree structure. The octree occupancy code generator 12003 can generate an occupancy code. The octree occupancy code generator 12003 can perform the same as the reference code. Figure 4 and Figure 6 The operations and / or methods of the point cloud video encoder (or octree analysis unit 40002) described herein are the same or similar to the operations and / or methods described herein. Figures 1 to 9 The details of the description are the same.

[0222] The surface model processor 12004 according to an embodiment may perform triangulation geometry encoding based on the surface model to reconstruct the position of a point in a specific area (or node) based on voxels. Figure 4 The operations and / or methods of the point cloud video encoder (e.g., surface approximation analysis unit 40003) described herein are the same as or similar to the operations and / or methods described herein. Figures 1 to 9 The details of the description are the same.

[0223] The intra / inter encoding processor 12005 according to the embodiment can perform intra / inter encoding on the point cloud data. The intra / inter encoding processor 12005 can perform the same as the reference Figure 7 The same or similar encoding as described for intra / inter coding. Details and references Figure 7 The details of the description are the same. According to an embodiment, the intra / inter encoding processor 12005 may be included in the arithmetic encoder 12006.

[0224] According to an embodiment, arithmetic encoder 12006 performs entropy encoding on an octree and / or an approximate octree of the point cloud data. For example, the encoding scheme includes arithmetic coding. Arithmetic encoder 12006 performs operations and / or methods that are the same as or similar to those of arithmetic encoder 40004.

[0225] The metadata processor 12007 according to an embodiment processes metadata (e.g., set values) about the point cloud data and provides it to necessary processing processes such as geometry coding and / or attribute coding. In addition, the metadata processor 12007 according to an embodiment may generate and / or process signaling information related to geometry coding and / or attribute coding. The signaling information according to an embodiment may be encoded separately from the geometry coding and / or attribute coding. The signaling information according to an embodiment may be interleaved.

[0226] The color conversion processor 12008, the attribute conversion processor 12009, the LOD / lifting / RAHT conversion processor 12010, and the arithmetic encoder 12011 perform attribute encoding. Figures 1 to 9 The attribute codes described are the same or similar, so a detailed description thereof is omitted.

[0227] The color transform processor 12008 according to an embodiment performs color transform encoding to transform the color values ​​included in the attribute. The color transform processor 12008 may perform color transform encoding based on the reconstructed geometry. The reconstructed geometry is consistent with the reference Figures 1 to 9 In addition, it performs the same Figure 4 The operations and / or methods of the color conversion unit 40006 are the same as or similar to those described above, and detailed description thereof is omitted.

[0228] The attribute transformation processor 12009 according to an embodiment performs attribute transformation to transform attributes based on the reconstructed geometry and / or the location where geometry encoding is not performed. Figure 4 The operation and / or method of the attribute transformation unit 40007 described in the embodiment are the same as or similar to the operation and / or method. Detailed description thereof is omitted. The LOD / lifting / RAHT transformation processor 12010 according to the embodiment can encode the transformed attribute by any one of RAHT coding, prediction transformation coding and lifting transformation coding or a combination thereof. The LOD / lifting / RAHT transformation processor 12010 performs the same as the reference. Figure 4 The operations of the RAHT unit 40008, the LOD generation unit 40009 and the lifting transform unit 40010 are the same as or similar to at least one of the operations described above. In addition, the prediction transform coding, the lifting transform coding and the RAHT transform coding are the same as those of the reference Figures 1 to 9 Those described are the same, so detailed descriptions thereof are omitted.

[0229] The arithmetic encoder 12011 according to the embodiment may encode the encoded attribute based on arithmetic coding. The arithmetic encoder 12011 performs the same or similar operations and / or methods as those of the arithmetic encoder 40012.

[0230] The transmitting processor 12012 according to the embodiment may transmit each bitstream containing the encoded geometry and / or the encoded attributes and / or metadata (or metadata information), or transmit a bitstream configured with the encoded geometry and / or the encoded attributes and / or metadata. When the encoded geometry and / or the encoded attributes and / or metadata according to the embodiment are configured as a bitstream, the bitstream may include one or more sub-bitstreams. The bitstream according to the embodiment may include signaling information, which includes a sequence parameter set (SPS) for sequence-level signaling, a geometry parameter set (GPS) for signaling of geometry information encoding, an attribute parameter set (APS) for signaling of attribute information encoding, and a tile parameter set (TPS or tile list) for tile-level signaling and slice data. The slice data may include information about one or more slices. A slice according to the embodiment may include a geometry bitstream Geom0 0 and one or more attribute bitstreams Attr0 0 and Attr1 0 .

[0231] A slice is a sequence of syntax elements that represents all or part of a coded point cloud frame.

[0232] The TPS according to an embodiment may include information about each tile of one or more tiles (for example, height / size information and coordinate information about a bounding box). The geometry bitstream may include a header and a payload. The header of the geometry bitstream according to an embodiment may include a parameter set identifier (geom_parameter_set_id), a tile identifier (geom_tile_id), and a slice identifier (geom_slice_id) included in the GPS, as well as information about the data contained in the payload. As described above, the metadata processor 12007 according to an embodiment may generate and / or process signaling information and send it to the sending processor 12012. According to an embodiment, an element for performing geometry coding and an element for performing attribute coding may share data / information with each other, as indicated by the dotted line. The sending processor 12012 according to an embodiment may perform operations and / or sending methods that are the same as or similar to those of the transmitter 10003. Details and References Figure 1 and Figure 2 The details described are the same, so their description is omitted.

[0233] Figure 13 A receiving device according to an embodiment is illustrated.

[0234] Figure 13 The receiving device illustrated in the example is Figure 1 The receiving device 10004 (or Figure 10 and Figure 11 An example of a point cloud video decoder. Figure 13 The receiving device illustrated in the example can perform the same Figures 1 to 11 One or more of the same or similar operations and methods of the point cloud video decoder described.

[0235] The receiving apparatus according to the embodiment includes a receiver 13000, a receiving processor 13001, an arithmetic decoder 13002, an octtree reconstruction processor based on an occupancy code 13003, a surface model processor (triangle reconstruction, upsampling, voxelization) 13004, an inverse quantization processor 13005, a metadata parser 13006, an arithmetic decoder 13007, an inverse quantization processor 13008, an LOD / lifting / RAHT inverse transform processor 13009, an inverse color transform processor 13010, and / or a renderer 13011. Each element for decoding according to the embodiment may perform an inverse process of an operation of a corresponding element for encoding according to the embodiment.

[0236] The receiver 13000 according to the embodiment receives point cloud data. The receiver 13000 may perform the same Figure 1 The operation and / or receiving method of the receiver 10005 are the same as or similar to the operation and / or receiving method of the receiver 10005. A detailed description thereof is omitted.

[0237] The reception processor 13001 according to an embodiment may obtain a geometry bitstream and / or an attribute bitstream from received data. The reception processor 13001 may be included in the receiver 13000.

[0238] The arithmetic decoder 13002, the octtree reconstruction processor 13003 based on the occupancy code, the surface model processor 13004 and the inverse quantization processor 13005 can perform geometric decoding. Figures 1 to 10 The geometric decoding described is the same or similar, so a detailed description thereof is omitted.

[0239] The arithmetic decoder 13002 according to the embodiment can decode the geometry bitstream based on arithmetic coding. The arithmetic decoder 13002 performs the same or similar operations and / or encoding as those of the arithmetic decoder 11000.

[0240] The octree reconstruction processor 13003 based on the occupancy code according to the embodiment can reconstruct the octree by obtaining the occupancy code from the decoded geometry bitstream (or the information about the geometry structure protected as a result of decoding). The octree reconstruction processor 13003 based on the occupancy code performs the same or similar operations and / or methods as the operations of the octree synthesizer 11001 and / or the octree generation method. When triangle soup geometry coding is applied, the surface model processor 13004 according to the embodiment can perform triangle soup geometry decoding and related geometry reconstruction (e.g., triangle reconstruction, upsampling, voxelization) based on the surface model method. The surface model processor 13004 performs the same or similar operations as the operations of the surface approximation synthesizer 11002 and / or the geometry reconstruction unit 11003.

[0241] According to an embodiment, the inverse quantization processor 13005 may inverse quantize the decoded geometric structure.

[0242] The metadata parser 13006 according to an embodiment can parse metadata contained in the received point cloud data, for example, setting values. The metadata parser 13006 can pass the metadata for geometry decoding and / or attribute decoding. Figure 12 The metadata described are the same, so a detailed description thereof is omitted.

[0243] The arithmetic decoder 13007, the inverse quantization processor 13008, the LOD / lifting / RAHT inverse transform processor 13009 and the color inverse transform processor 13010 perform attribute decoding. Figures 1 to 10 The attribute decoding described is the same or similar, so a detailed description thereof is omitted.

[0244] According to an embodiment, the arithmetic decoder 13007 can decode the attribute bitstream by arithmetic coding. The arithmetic decoder 13007 can decode the attribute bitstream based on the reconstructed geometric structure. The arithmetic decoder 13007 performs the same or similar operations and / or coding as those of the arithmetic decoder 11005.

[0245] According to an embodiment, the inverse quantization processor 13008 may inverse quantize the decoded attribute bitstream. The inverse quantization processor 13008 performs the same or similar operations and / or inverse quantization methods as those of the inverse quantization unit 11006.

[0246] The LOD / lifting / RAHT inverse transform processor 13009 according to an embodiment can process the reconstructed geometry and the inverse quantized attributes. The prediction / lifting / RAHT inverse transform processor 1301 performs one or more of the same or similar operations and / or decoding as the RAHT converter 11007, the LOD generation unit 11008, and / or the inverse lifter 11009. The color inverse transform processor 13010 according to an embodiment performs inverse transform encoding to inversely transform the color values ​​(or textures) included in the decoded attributes. The color inverse transform processor 13010 performs the same or similar operations and / or inverse transform encoding as the inverse color transform unit 11010. The renderer 13011 according to an embodiment can render point cloud data.

[0247] Figure 14 An exemplary structure operatively connectable with a method / apparatus for transmitting and receiving point cloud data according to an embodiment is shown.

[0248] Figure 14 The structure of represents a configuration in which at least one of a server 17600, a robot 17100, an autonomous vehicle 17200, an XR device 17300, a smartphone 17400, a home appliance 17500, and / or a head-mounted display (HMD) 17700 is connected to a cloud network 17100. The robot 17100, the autonomous vehicle 17200, the XR device 17300, the smartphone 17400, or the home appliance 17500 is referred to as a device. In addition, the XR device 17300 may correspond to a point cloud compressed data (PCC) device according to an embodiment, or may be operatively connected to a PCC device.

[0249] The cloud network 17000 may represent a network that constitutes a part of a cloud computing infrastructure or exists in a cloud computing infrastructure. Here, the cloud network 17000 may be configured using a 3G network, a 4G or Long Term Evolution (LTE) network, or a 5G network.

[0250] The server 17600 may be connected to at least one of the robot 17100, the autonomous vehicle 17200, the XR device 17300, the smart phone 17400, the home appliance 17500 and / or the HMD 17700 via the cloud network 17000 and may assist at least a portion of the processing of the connected devices 17100 to 17700.

[0251] HMD 17700 represents one of the implementation types of an XR device and / or a PCC device according to an embodiment. The HMD type device according to an embodiment includes a communication unit, a control unit, a memory, an I / O unit, a sensor unit, and a power supply unit.

[0252] In the following, various embodiments of apparatuses 17100 to 17500 to which the above technologies are applied will be described. According to the above embodiments, Figure 14 the apparatuses 17100 to 17500 illustrated in [reference] can be operably connected / coupled to a point cloud data transmitting apparatus and a receiver.

[0253] <PCC+XR>

[0254] The XR / PCC apparatus 17300 can adopt PCC technology and / or XR (AR+VR) technology, and can be implemented as a head-mounted display (HMD), a head-up display (HUD) provided in a vehicle, a television, a mobile phone, a smartphone, a computer, a wearable device, a household appliance, a digital signage, a vehicle, a stationary robot, or a mobile robot.

[0255] The XR / PCC apparatus 17300 can analyze 3D point cloud data or image data obtained through various sensors or from external apparatuses, and generate position data and attribute data regarding 3D points. Thereby, the XR / PCC apparatus 17300 can obtain information regarding the surrounding space or real objects, and render and output XR objects. For example, the XR / PCC apparatus 17300 can match an XR object including auxiliary information regarding the recognized object with the recognized object, and output the matched XR object.

[0256] <PCC+Autopilot+XR>

[0257] The autonomous driving vehicle 17200 can be implemented as a mobile robot, a vehicle, an unmanned aerial vehicle, etc. by applying PCC technology and XR technology.

[0258] The autonomous driving vehicle 17200 applying XR / PCC technology can represent an autonomous driving vehicle provided with an apparatus for providing an XR image or an autonomous driving vehicle that is a control / interaction target in an XR image. Specifically, the autonomous driving vehicle 17200 that is a control / interaction target in an XR image can be distinguished from the XR apparatus 17300, and can be operably connected to the XR apparatus 1730.

[0259] The autonomous driving vehicle 17200 having an apparatus for providing an XR / PCC image can obtain sensor information from sensors including a camera, and output the generated XR / PCC image based on the obtained sensor information. For example, the autonomous driving vehicle 17200 can have a HUD and output an XR / PCC image thereto, thereby providing an XR / PCC object corresponding to a real object or an object existing on a screen to an occupant.

[0260] When an XR / PCC object is output to the HUD, at least a portion of the XR / PCC object can be output to overlap with the real object at which the occupant's eyes are directed. On the other hand, when an XR / PCC object is output to a display provided inside the autonomous vehicle, at least a portion of the XR / PCC object can be output to overlap with the object on the screen. For example, the autonomous vehicle 17200 can output XR / PCC objects corresponding to objects such as a road, another vehicle, a traffic light, a traffic sign, a two-wheeled vehicle, a pedestrian, and a building.

[0261] Virtual reality (VR) technology, augmented reality (AR) technology, mixed reality (MR) technology, and / or point cloud compression (PCC) technology according to an embodiment are applicable to various devices.

[0262] In other words, VR technology is a display technology that only provides CG images of real-world objects, backgrounds, etc. On the other hand, AR technology refers to a technology that shows a virtually created CG image on an image of a real object. MR technology is similar to the above-mentioned AR technology in that the virtual objects to be shown are mixed and combined with the real world. However, MR technology is different from AR technology in that AR technology clearly distinguishes between real objects and virtual objects created as CG images and uses virtual objects as supplementary objects to real objects, while MR technology regards virtual objects as objects with equivalent characteristics to real objects. More specifically, an example of the application of MR technology is a hologram service.

[0263] Recently, VR, AR, and MR technologies are sometimes referred to as extended reality (XR) technologies, without being clearly distinguished from each other. Therefore, embodiments of the present disclosure are applicable to any of VR, AR, MR, and XR technologies. Encoding / decoding based on PCC, V-PCC, and G-PCC technologies are applicable to such technologies.

[0264] The PCC method / apparatus according to the embodiment may be applied to vehicles providing autonomous driving services.

[0265] Vehicles providing autonomous driving services are connected to the PCC device for wired / wireless communication.

[0266] When a point cloud compressed data (PCC) transmitting / receiving device according to an embodiment is connected to a vehicle for wired / wireless communication, the device can receive / process content data related to AR / VR / PCC services that can be provided together with autonomous driving services and send it to the vehicle. In the case where the PCC transmitting / receiving device is installed on the vehicle, the PCC transmitting / receiving device can receive / process content data related to AR / VR / PCC services based on a user input signal input through a user interface device and provide it to the user. The vehicle or the user interface device according to the embodiment can receive the user input signal. The user input signal according to the embodiment may include a signal indicating an autonomous driving service.

[0267] In addition, the point cloud video encoder of the sender can also perform a spatial segmentation process of spatially segmenting (or dividing) the point cloud data into one or more 3D blocks before encoding the point cloud data. That is, in order to perform the encoding and sending operations of the sending device and the decoding and rendering operations of the receiving device in real time and with low latency, the sending device can spatially segment the point cloud data into multiple regions. In addition, the sending device can encode the spatially segmented regions (or blocks) independently or non-independently, thereby achieving random access and parallel encoding in the three-dimensional space occupied by the point cloud data. In addition, the sending device and the receiving device can perform encoding and decoding independently or non-independently for each spatially segmented region (or block), thereby preventing the accumulation of errors in the encoding process and the decoding process.

[0268] Figure 15 is a diagram illustrating another example of a point cloud transmitting apparatus according to an embodiment including a space divider.

[0269] According to an embodiment, the point cloud transmission device may include a data input unit 51001, a coordinate transformation unit 51002, a quantization processor 51003, a spatial splitter 51004, a signaling processor 51005, a geometry encoder 51006, an attribute encoder 51007, and a transmission processor 51008. According to an embodiment, the coordinate transformation unit 51002, the quantization processor 51003, the spatial splitter 51004, the geometry encoder 51006, and the attribute encoder 51007 may be referred to as a point cloud video encoder.

[0270] The data input unit 51001 can perform Figure 1 Some or all of the operations of the point cloud video acquisition unit 10001, or the point cloud video acquisition unit 10001 may be executed Figure 12 The coordinate transformation unit 51002 can perform some or all operations of the data input unit 12000. Figure 4 In addition, the quantization processor 51003 may perform some or all operations of the coordinate transformation unit 40000. Figure 4Some or all of the operations of the quantization unit 40001, or may perform Figure 12 Some or all operations of the quantization processor 12001.

[0271] The spatial splitter 51004 can spatially split the point cloud data, which has been quantized and output from the quantization processor 51003, into one or more 3D blocks based on bounding boxes and / or sub-bounding boxes. Here, a 3D block can refer to a tile group, a tile, a slice, a coding unit (CU), a prediction unit (PU), or a transform unit (TU). In one embodiment, the signaling information for spatial segmentation is entropy-encoded by the signaling processor 51005 and then transmitted in the form of a bitstream via the transmission processor 51008.

[0272] Figure 16 (a) to Figure 16 (c) of FIG. 1 illustrates an embodiment of dividing the bounding box into one or more tiles. Figure 16 As shown in (a), the point cloud object corresponding to the point cloud data can be represented in the form of a box based on the coordinate system, which is called a bounding box. In other words, the bounding box represents a cube that can contain all points in the point cloud.

[0273] Figure 16 (b) and Figure 16 (c) illustrates the Figure 16 An example in which the bounding box of (a) is divided into tile 1# and tile 2#, and tile 2# is further divided into slice 1# and slice 2#.

[0274] In one embodiment, the point cloud content may be a person, multiple persons, an object, or multiple objects such as an actor. In a larger scope, it may be a map for autonomous driving or a map for indoor navigation of a robot. In this case, the point cloud content may be a large amount of locally connected data. In this case, the point cloud content cannot be encoded / decoded all at once, so tile segmentation may be performed before compressing the point cloud content. For example, room #101 in a building may be segmented into one tile, and room #102 in the building may be segmented into another tile. In order to support fast encoding / decoding by applying parallelization to the segmented tiles, the tiles may be segmented (or split) into slices again. This operation may be referred to as slice segmentation (or splitting).

[0275] That is, depending on the embodiment, a tile may represent a portion of the 3D space occupied by the point cloud data (e.g., a rectangular cube). Depending on the embodiment, a tile may include one or more slices. A tile according to the embodiment may be divided into one or more slices, so that the point cloud video encoder can encode the point cloud data in parallel.

[0276] A slice may represent a unit of data (or bitstream) that can be independently encoded by a point cloud video encoder according to an embodiment and / or a unit of data (or bitstream) that can be independently decoded by a point cloud video decoder. A slice may be a collection of data in the 3D space occupied by point cloud data or a collection of some data among the point cloud data. A slice according to an embodiment may represent a collection or area of ​​points included in a tile according to an embodiment. According to an embodiment, a tile may be divided into one or more slices based on the number of points included in a tile. For example, a tile may be a collection of points divided by the number of points. According to an embodiment, a tile may be divided into one or more slices based on the number of points, and some data may be split or merged in the segmentation process. That is, a slice may be a unit that can be independently encoded within a corresponding tile. In this way, the tile obtained by spatial segmentation is divided into one or more slices for fast and efficient processing.

[0277] A point cloud video encoder according to an embodiment may encode point cloud data on a slice-by-slice or tile-by-tile basis, where a tile includes one or more slices. Furthermore, a point cloud video encoder according to an embodiment may perform different quantization and / or transformations on each tile or each slice.

[0278] The positions of one or more 3D blocks (e.g., slices) spatially partitioned by the spatial partitioner 51004 are output to a geometry encoder 51006, and attribute information (or attributes) are output to an attribute encoder 51007. The positions may be positional information about points included in a partition unit (a frame, a block, a tile, a tile group, or a slice), and are referred to as geometric information.

[0279] The geometry encoder 51006 constructs and encodes (i.e., compresses) an octree based on the positions output from the spatial partitioner 51004 to output a geometry bitstream. The geometry encoder 51006 may reconstruct the octree and / or approximate octree and output it to the attribute encoder 51007. The reconstructed octree may be referred to as reconstructed geometry (recovered geometry).

[0280] The attribute encoder 51007 encodes (ie, compresses) the attributes output from the space divider 51004 based on the reconstructed geometry output from the geometry encoder 51006 and outputs an attribute bitstream.

[0281] Figure 17 is a detailed block diagram illustrating another example of a geometry encoder 51006 and an attribute encoder 51007 according to an embodiment.

[0282] Figure 17The voxelization processor 53001, octree generator 53002, geometric information predictor 53003 and arithmetic encoder 53004 of the geometric encoder 51006 can perform Figure 4 Some or all of the operations of the octree analysis unit 40002, the surface approximation analysis unit 40003, the arithmetic encoder 40004 and the geometric reconstruction unit 40005, or may be performed Figure 12 Some or all of the operations of the voxelization processor 12002, the octree occupancy code generator 12003 and the surface model processor 12004, the intra / inter coding processor 12005 and the arithmetic encoder 12006.

[0283] Figure 17 The attribute encoder 51007 may include a color transformation processor 53005, an attribute transformation processor 53006, an LOD configuration unit 53007, a neighboring point set configuration unit 53008, an attribute information prediction unit 53009, a residual attribute information quantization processor 53010 and an arithmetic encoder 53011.

[0284] In an embodiment, a quantization processor may be further provided between the spatial divider 51004 and the voxelization processor 53001. The quantization processor quantizes the positions of one or more 3D blocks (eg, slices) spatially divided by the spatial divider 51004. In this case, the quantization processor may perform Figure 4 Some or all of the operations of the quantization unit 40001, or performing Figure 12 When a quantization processor is also provided between the spatial divider 51004 and the voxelization processor 53001, some or all operations of the quantization processor 12001 may be omitted or not omitted. Figure 15 Quantization processor 51003.

[0285] The voxelization processor 53001 according to an embodiment performs voxelization based on the position of one or more spatially divided 3D blocks (e.g., slices) or their quantized positions. Voxelization refers to a minimized unit for representing position information in 3D space. A point of a point cloud content (or 3D point cloud video) according to an embodiment may be included in one or more voxels. According to an embodiment, a voxel may include one or more points. In an embodiment, when quantization is performed before voxelization is performed, multiple points may belong to one voxel.

[0286] In this specification, when two or more points are included in one voxel, the two or more points are referred to as overlapping points. That is, in the geometry coding process, overlapping points may be generated by geometry quantization and voxelization.

[0287] The voxelization processor 53001 according to an embodiment may output duplicate points belonging to one voxel to the octree generator 53002 without merging them, or may merge a plurality of points into one point and output the merged point to the octree generator 53002 .

[0288] The octree generator 53002 according to an embodiment generates an octree based on voxels output from the voxelization processor 53001.

[0289] The geometry information predictor 53003 according to an embodiment predicts and compresses geometry information based on the octree generated by the octree generator 53002, and outputs the predicted and compressed information to the arithmetic encoder 53004. In addition, the geometry information predictor 53003 reconstructs geometry based on the position changed by compression, and outputs the reconstructed (or decoded) geometry to the LOD configuration unit 53007 of the attribute encoder 51007. The reconstruction of the geometry information may be performed in a device or component separate from the geometry information predictor 53003. In another embodiment, the reconstructed geometry may also be provided to the attribute transformation processor 53006 of the attribute encoder 51007.

[0290] The color transform processor 53005 of the attribute encoder 51007 corresponds to Figure 4 Color conversion unit 40006 or Figure 12 The color transform processor 12008 of the embodiment. The color transform processor 53005 according to an embodiment performs color transform encoding for transforming the color values ​​(or textures) included in the attributes provided from the data input unit 51001 and / or the spatial divider 51004. For example, the color transform processor 53005 may transform the format of the color information (e.g., from RGB to YCbCr). The operation of the color transform processor 53005 according to an embodiment may be optionally applied according to the color values ​​included in the attributes. In another embodiment, the color transform processor 53005 may perform color transform encoding based on the reconstructed geometry. For details of the geometric reconstruction, refer to Figures 1 to 9 Description.

[0291] According to an embodiment, the attribute transformation processor 53006 may perform attribute transformation of transforming attributes based on a position and / or reconstructed geometry for which geometry encoding has not yet been performed.

[0292] The attribute transformation processor 53006 may be referred to as a recoloring unit.

[0293] The operation of the attribute transformation processor 53006 according to an embodiment may be optionally applied depending on whether duplicate points are merged. According to an embodiment, merging of duplicate points may be performed by the voxelization processor 53001 or the octree generator 53002 of the geometry encoder 51006.

[0294] In this specification, when points belonging to one voxel are merged into one point in the voxelization processor 53001 or the octree generator 53002, the attribute transformation processor 53006 performs attribute transformation.

[0295] Attribute transformation processor 53006 performs the Figure 4 Attribute transformation unit 40007 or Figure 12 The operations and / or methods of the attribute transformation processor 12009 are the same as or similar to the operations and / or methods.

[0296] According to an embodiment, the geometry information reconstructed by the geometry information predictor 53003 and the attribute information output from the attribute transformation processor 53006 are provided to the LOD configuration unit 53007 for attribute compression.

[0297] According to an embodiment, the attribute information output from the attribute transform processor 53006 may be compressed based on the reconstructed geometric information through one or a combination of two or more of RAHT encoding, LOD-based predictive transform encoding, and lifting transform encoding.

[0298] Hereinafter, as an embodiment, it is assumed that attribute compression is performed by one or a combination of LOD-based predictive transform coding and lifting transform coding. Therefore, the description of RAHT coding will be omitted. For details of RAHT transform coding, refer to Figures 1 to 9 Description.

[0299] The LOD configuration unit 53007 according to an embodiment generates a level of detail (LOD).

[0300] LOD represents the level of detail of the point cloud content. As the LOD value decreases, the level of detail of the point cloud content decreases. As the LOD value increases, the level of detail of the point cloud content increases. The points of the reconstructed geometry (i.e., the reconstructed locations) can be categorized according to the LOD.

[0301] In an embodiment, in predictive transform coding and lifting transform coding, points may be divided into LODs and grouped.

[0302] This operation may be referred to as an LOD generation process, and a group with different LODs may be referred to as an LOD l Here, l represents LOD and is an integer starting from 0. LOD0 is the set consisting of points with the largest distance between them. As l increases, the points belonging to LOD l The distance between the points decreases.

[0303] When the LOD value configuration unit 53007 generates LOD lWhen collecting, the neighboring point set configuration unit 53008 according to the embodiment can be based on LOD l The set finds X (>0) nearest neighbor (NN) points in a group with the same or lower LOD (i.e., the distance between nodes is large) and registers them as a neighbor set in the predictor. X is the maximum number of points that can be set as neighbors. X can be input as a user parameter or signaled in the signaling information by the signaling processor 51005 (e.g., the lifting_num_pred_nearest_neighbours field signaled in the APS).

[0304] refer to Figure 9 As an example, find the neighboring points of P3 belonging to LOD1 in LOD0 and LOD1. For example, when the maximum number of points (X) that can be set as neighboring points is 3, the three nearest neighboring nodes of P3 can be P2, P4, and P6. These three nodes are registered as a neighboring set in the predictor of P3. In an embodiment, among the registered nodes, the neighboring node P4 is closest to P3 in terms of distance, then P6 is the next closest node, and P2 is the farthest node among the three nodes. Here, X=3 is merely an embodiment configured to provide an understanding of the present disclosure. The value of X may be different.

[0305] As mentioned above, all points of the point cloud data can have a predictor respectively.

[0306] The attribute information prediction unit 53009 according to the embodiment predicts attribute values ​​from the neighboring points registered in the predictor and obtains residual attribute values ​​of the corresponding points based on the predicted attribute values. The residual attribute values ​​are output to the residual attribute information quantization processor 53010.

[0307] Next, LOD generation and neighboring point search will be described in detail.

[0308] As described above, for attribute compression, the attribute encoder generates an LOD based on the points of the reconstructed geometry and searches for the nearest neighboring points of the point to be encoded based on the generated LOD. According to an embodiment, even the attribute decoder of the receiving device generates an LOD and searches for the nearest neighboring points of the point to be decoded based on the generated LOD.

[0309] According to an embodiment, the LOD configuration unit 53007 may configure one or more LODs using one or more LOD generation methods (or LOD configuration methods).

[0310] According to an embodiment, the LOD generation method used in the LOD configuration unit 53007 may be input as a user parameter, or may be signaled in signaling information via the signaling processor 51005. For example, the LOD generation method may be signaled in the APS.

[0311] According to an embodiment, the LOD generation method may be divided into an octree-based LOD generation method, a distance-based LOD generation method, and a sampling-based LOD generation method.

[0312] Figure 18 is a diagram illustrating an example of generating LOD based on an octree according to an embodiment.

[0313] According to an embodiment, when LOD is generated based on an octree, each depth level of the octree may be matched with each LOD, such as Figure 18 As illustrated in . That is, the octree-based LOD generation method is a method for generating LOD using the following characteristics: the details of the point cloud data gradually increase as the depth level in the octree structure increases (i.e., from the root to the leaf). Depending on the embodiment, the octree-based LOD configuration can be performed from the root node to the leaf node or vice versa from the leaf node to the root node.

[0314] The distance-based LOD generation method according to the embodiment is a method of arranging points according to a Morton code and generating LOD based on the distance between the points.

[0315] The sampling-based LOD generation method according to the embodiment is a method of arranging points according to the Morton code and classifying each k-th point as a lower LOD that can be a candidate for a neighboring point set. That is, during sampling, each k-th point can be selected and classified as belonging to LOD0 to LOD l-1 The lower candidate set of , and the remaining points can be registered in the LOD l That is, the selected points become a candidate group that can be selected to be registered in the LOD l The set of neighboring points of a point in the set. k can be different depending on the content of the point cloud. Here, the point can be a point of the captured point cloud or a point of the reconstructed geometry.

[0316] In this disclosure, based on LOD l Collections and LOD0 to LOD l-1 The corresponding lower candidate set will be called the retained set or LOD retained set. LOD0 is the set consisting of points with the largest distance between them. As l increases, the number of points belonging to LOD l The distance between the points decreases.

[0317] According to the implementation, LOD lThe points in the set are also arranged based on the Morton code order. l-1 The points included in each of the sets are also arranged based on the Morton code order.

[0318] Figure 19 is a diagram illustrating an example of arranging points of a point cloud in a Morton code order according to an embodiment.

[0319] That is, a Morton code is generated for each point in the point cloud based on the x, y, and z position values ​​of each point. When the Morton codes of the points of the point cloud are generated through this process, the points of the point cloud may be arranged in a Morton code order. According to an embodiment, the points of the point cloud may be arranged in ascending order of the Morton codes. The order of the points arranged in ascending order of the Morton codes may be referred to as a Morton order.

[0320] The LOD configuration unit 53007 according to an embodiment may generate LOD by performing sampling on points arranged in a Morton order.

[0321] The LOD configuration unit 53007 generates an LOD by applying at least one of an octree-based LOD generation method, a distance-based LOD generation method, or a sampling-based LOD generation method. l Set, the neighboring point set configuration unit 53008 can be based on LOD l The set is used to search for X (>0) nearest neighbor (NN) points in a group with the same or lower LOD (ie, the distance between nodes is large), and the X NN points are registered in the predictor as a neighbor point set.

[0322] In this case, since it takes a lot of time to search all points in order to configure a neighboring point set, an embodiment is provided for searching neighboring points within a neighboring point search range that includes only some points. The search range refers to the number of points and can be 128, 256, or another value. Depending on the embodiment, information about the search range can be set in the neighboring point set configuration unit 53008 or can be input as a user parameter. Alternatively, information about the search range can be signaled in signaling information via the signaling processor 51005 (for example, the lifting_search_range field signaled in the APS).

[0323] Figure 20 : is a diagram illustrating an example of searching for a neighboring point in a search range based on LOD according to an embodiment. The arrows illustrated in the figure indicate a Morton order according to an embodiment.

[0324] exist Figure 20 In an embodiment, the index list includes the LOD set to which the point to be coded belongs (ie, LOD l ) and the reserved list includes LOD-basedl At least one lower LOD set of the set (e.g., LOD0 to LOD l-1 ).

[0325] According to an embodiment, the points of the index list are arranged in ascending order based on the size of the Morton code, and the points of the reserved list are also arranged in ascending order based on the size of the Morton code. Therefore, the first point in the Morton order of the points in the index list and the reserved list has the smallest Morton code size.

[0326] According to the embodiment, the neighboring point set configuration unit 53008 may be configured in the LOD0 to LOD l-1 Points of a collection and / or belonging to LOD l Among the points in the set, a point having a Morton code closest to the Morton code of the point Px among the points that are sequentially preceding the point Px (i.e., points having a Morton code less than or equal to the Morton code of the point Px) is searched to generate a point belonging to the LOD l The neighboring point set of the point Px (ie, the point to be encoded or the current point) is set. In this disclosure, the searched point will be referred to as Pi or the center point.

[0327] According to an embodiment, when searching for a central point Pi, the neighboring point set configuration unit 53008 may search for a point Pi having a Morton code closest to that of point Px among all points preceding point Px, or may search for a point Pi having a Morton code closest to that of point Px among the points within the search range. In the present disclosure, the search range may be configured by the neighboring point set configuration unit 53008 or may be input as a user parameter. In addition, information about the search range may be signaled in signaling information via the signaling processor 51005. The present disclosure provides an embodiment for searching for the central point Pi in a reserved list when the number of LODs is multiple and for searching for the central point Pi in an index list when the number of LODs is 1. For example, when the number of LODs is 1, the search range may be determined based on the position of the current point in the list arranged by Morton code.

[0328] The neighboring point set configuration unit 53008 according to the embodiment compares the point Px with the points located before the searched (or selected) center point Pi (i.e., before Figure 20 to the left of the center point in ) and after the searched (or selected) center point Pi (i.e., Figure 20The distance value between the points belonging to the neighboring point search range (to the right of the center point in ). The neighboring point set configuration unit 53008 can register X (for example, 3) NN points as a neighboring point set. In an embodiment, the neighboring point search range is the number of points. The neighboring point search range according to an embodiment may include one or more points located before (i.e., in front of) and / or after (i.e., after) the center point Pi in the Morton order. X is the maximum number of points that can be registered as neighboring points.

[0329] According to an embodiment, information about the neighboring point search range and information about the maximum number X of points that can be registered as neighboring points can be configured by the neighboring point set configuration unit 53008, or can be input as a user parameter, or signaled in the signaling information through the signaling processor 51005 (for example, the lifting_search_range field and the lifting_num_pred_nearest_neighbours field signaled in the APS). According to an embodiment, the actual search range can be a value obtained by multiplying the value of the lifting_search_range field by 2 and then adding the resulting value to the center point (i.e., (the value of the lifting_search_range field × 2) + the center point), a value obtained by adding the center value to the value of the lifting_search_range field (i.e., the value of the lifting_search_range field + the center point), or the value of the lifting_search_range field. The present disclosure provides an embodiment for searching for neighboring points of a point Px in a reserved list when the number of LODs is multiple and searching for neighboring points of a point Px in an index list when the number of LODs is 1.

[0330] For example, if the number of LODs is 2 or more and the information about the search range (e.g., lifting_search_range field) is 128, the actual search range includes the center point Pi in the retention list arranged by the Morton code, the 128 points before the center point Pi, and the 128 points after the center point Pi. As another example, if the number of LODs is 1 and the information about the search range is 128, the actual search range includes the center point Pi in the index list arranged by the Morton code and the 128 points before the center point Pi. As another example, if the number of LODs is 1 and the information about the search range is 128, the actual search range includes the 128 points before the current point Px in the index list arranged by the Morton code.

[0331] According to an embodiment, the neighboring point set configuring unit 53008 may compare the distance values ​​between the points within the actual search range and the point Px, and register X NN points as the neighboring point set of the point Px.

[0332] On the other hand, attribute characteristics of content may appear different depending on an object from which the content was captured, a capturing scheme, or a device used to capture the content.

[0333] Figure 21 (a) and Figure 21 (b) is a diagram illustrating an example of point cloud content according to an embodiment.

[0334] Figure 21 (a) is an example of dense point cloud data obtained by capturing an object with a 3D scanner and the attribute correlation of the object can be high. Figure 21 (b) is an example of sparse point cloud data obtained by capturing a wide area with a LiDAR device, and the attribute correlation between points may be low. An example of dense point cloud data may be a still image, and an example of sparse point cloud data may be an image captured by a drone or an autonomous vehicle.

[0335] In this way, the attribute characteristics of the content may appear different depending on the object from which the content was captured, the capture scheme, or the device used to capture the content. If a neighboring point search is performed to configure a neighboring point set when encoding the attributes without considering the different attribute characteristics, the distances between the corresponding points and the registered neighboring points may not all be adjacent. For example, when X (for example, 3) NN points are registered as a neighboring point set after comparing the distance values ​​between point Px and the points belonging to the neighboring point search range before and after the center point Pi, at least one of the X registered neighboring points may be far away from point Px. In other words, at least one of the X registered neighboring points may not be the actual neighboring point of point Px.

[0336] This phenomenon is more likely to occur in sparse point cloud data than in dense point cloud data. Figure 21 As illustrated in (a) of , if there is an object in the content that has color continuity / similarity and is densely captured, and the captured area is small, then even if the points are a little far from the neighboring points, there may be content with high attribute correlation between the points. On the contrary, if Figure 21 As illustrated in (b) of FIG, if a relatively wide area is sparsely captured by the LiDAR device, content with color correlation may exist only when the distance to the neighboring point is within a certain range. Figure 21 When encoding the attributes of sparse point cloud data as exemplified in (b), some neighboring points registered by neighboring point search may not be the actual neighboring points of the point to be encoded.

[0337] When generating LOD, the probability of this phenomenon is high when the points are arranged in Morton order. That is, although the Morton order quickly arranges adjacent points, due to the zigzag scanning characteristics of the Morton code, jump segments (i.e., Figure 19 In this case, some of the neighboring points registered by searching for neighboring points within the search range may not be the actual NN points of the point to be coded.

[0338] Figure 22 (a) and Figure 22 (b) is a diagram illustrating an example of an average distance, a minimum distance, and a maximum distance of points belonging to a neighboring point set according to an embodiment.

[0339] Figure 22 (a) illustrates an example of the 100th frame of the content called FORD, and Figure 22 (b) illustrates an example of the first frame of content called QNX.

[0340] Figure 22 (a) and Figure 22 (b) is an exemplary embodiment to help those skilled in the art understand. Figure 22 (a) and Figure 22 (b) may be different frames of the same content or specific frames of different content.

[0341] For the sake of convenience, Figure 22 The frame illustrated in (a) is referred to as the first frame, and Figure 22 The frame illustrated in (b) is referred to as the second frame.

[0342] exist Figure 22 In the first frame of (a), the total number of points is 80265, only one NN point is registered among 4 points, two NN points are registered among 60 points, and three NN points are registered among 80201 points. That is, the maximum number of points that can be registered as NN points is 3, but it can be 1 or 2 depending on the position of the point in the frame.

[0343] exist Figure 22 In the second frame of (b), the total number of points is 31279, only one NN point is registered in 3496 points, two NN points are registered in 219 points, and three NN points are registered in 27564 points.

[0344] exist Figure 22 The first frame of (a) and Figure 22 In the second frame of (b), NN1 is the NN point, NN2 is the next NN point, and NN3 is the third NN point. Figure 22In the first frame of (a), the maximum distance of NN2 is 1407616 and the maximum distance of NN3 is 1892992, while Figure 22 In the second frame of (b), the maximum distance of NN2 is 116007040 and the maximum distance of NN3 is 310887696.

[0345] That is, it can be understood that there is a considerable difference in distance depending on the content or frame. Therefore, some of the registered neighboring points may not be actual neighboring points.

[0346] In this way, if the distance difference between the to-be-encoded point and the registered neighboring points increases, the attribute difference between the two points may be large. In addition, when prediction is performed based on a neighboring point set including these neighboring points and a residual attribute value is obtained, the residual attribute value may increase, resulting in an increase in the bitstream size.

[0347] In other words, the characteristics of the content or frame may have an impact on the configuration of the neighboring point set used for attribute prediction, and this may affect the compression efficiency of the attribute.

[0348] Therefore, in the present disclosure, when configuring neighboring point sets to encode attributes of point cloud content, neighboring points are selected by considering the attribute correlation between points of the content, so that meaningless points are not selected as neighboring point sets. Consequently, since the residual attribute value is reduced and the bitstream size is thereby reduced, the compression efficiency of the attribute is improved. In other words, the present disclosure improves the compression efficiency of the attribute by limiting the points that can be selected as the neighboring point set by considering the attribute correlation between points of the content.

[0349] The present disclosure provides an embodiment of applying a maximum distance of neighboring points (also referred to as a maximum NN distance) when configuring a neighboring point set in an attribute encoding process of point cloud content in order to consider attribute correlations between points of the content.

[0350] The present disclosure provides an embodiment for configuring a neighboring point set by applying a search range and / or a maximum NN distance when encoding attributes of point cloud content.

[0351] For example, as a result of comparing the distance values ​​between point Px and the points that fall within the neighboring point search range before and after the center point Pi, points that are farther than the maximum NN distance among the X (e.g., 3) NN points are not registered in the neighboring point set of point Px. In other words, as a result of comparing the distance values ​​between point Px and the points that fall within the neighboring point search range before and after the center point Pi, only points within the maximum NN distance among the X (e.g., 3) NN points are registered in the neighboring point set of point Px. For example, if one of the three points with the closest distance is farther than the maximum NN distance, the farther point can be excluded, and only the remaining two points can be registered in the neighboring point set of point Px.

[0352] According to an embodiment, the neighboring point set configuration unit 53008 can apply a maximum NN distance based on the attribute characteristics of the content to achieve optimal compression efficiency for the attribute regardless of the attribute characteristics of the content. Here, the maximum NN distance can be calculated differently depending on the LOD generation method (octree-based, distance-based, or sampling-based LOD generation method). In other words, the maximum NN distance can be applied differently for each LOD.

[0353] In the present disclosure, the maximum NN distance is used interchangeably with the maximum distance of neighboring points or the maximum neighboring point distance. In other words, in the present disclosure, the maximum NN distance, the maximum distance of neighboring points, and the maximum neighboring point distance have the same meaning.

[0354] In the present disclosure, the maximum NN distance may be obtained by multiplying a basic neighboring point distance (referred to as a basic distance or a reference distance) by NN_range, as illustrated in Equation 5 below.

[0355] [Formula 5]

[0356] Maximum neighbor distance = basic neighbor distance × NN_range

[0357] In Equation 5, NN_range is a range within which neighboring points can be selected, and may be referred to as a maximum neighboring point range, a neighboring point range, or a NN range.

[0358] The neighboring point set configuration unit 53008 according to the embodiment can set NN_range automatically or manually according to the characteristics of the content, or set NN_range by inputting as a user parameter. In addition, information about NN_range can be signaled in the signaling information through the signaling processor 51005. The signaling information including information about NN_range can be at least one of SPS, APS, tile parameter set or attribute slice header. The attribute decoder of the receiving device can generate a neighboring point set based on the signaling information. In this case, the neighboring points used in the attribute prediction of the attribute encoding can be restored and applied to the attribute decoding.

[0359] According to an embodiment, the neighboring point set configuration unit 53008 can calculate / configure the basic neighboring point distance by combining one or more of an octree-based method, a distance-based method, a sampling-based method, a Morton code-based average difference method, and an average distance difference-based method for each LOD. According to an embodiment, the method for calculating / configuring the basic neighboring point distance can be signaled in signaling information (e.g., APS) by the signaling processor 51005.

[0360] Next, an embodiment of acquiring a basic distance of neighboring points and a maximum distance of neighboring points when generating LOD based on an octree will be described.

[0361] like Figure 18 As illustrated in , when the LOD configuration unit 53007 generates an octree-based LOD, each depth level of the octree can be matched with each LOD.

[0362] According to an embodiment, the neighboring point set configuration unit 53008 may obtain the maximum distance of neighboring points based on the octree-based LOD.

[0363] According to an embodiment, when the LOD configuration unit 53007 generates an octree-based LOD, spatial scalability can be supported. With spatial scalability, when the source point cloud is dense, a lower resolution point cloud can be accessed, like a thumbnail with lower decoder complexity and / or smaller bandwidth. For example, the spatial scalability function of the geometry can be provided by a process that only encodes or decodes occupied bits up to a selected depth level by adjusting the depth level of the octree during encoding / decoding of the geometry. In addition, even during encoding / decoding of attributes, the spatial scalability function of the attribute can be provided by a process that generates an LOD from a selected depth level of the octree and configures the points for which the encoding / decoding of the attribute will be performed.

[0364] According to an embodiment, for spatial scalability, the LOD configuration unit 53007 may generate an octree-based LOD, and the neighbor set configuration unit 53008 may perform a neighbor search based on the generated octree-based LOD.

[0365] When the neighboring point set configuration unit 53008 according to an embodiment searches for neighboring points in a previous LOD based on points belonging to the current LOD generated based on the octree, the maximum neighboring point distance can be obtained by multiplying the basic neighboring point distance (referred to as the basic distance or reference distance) by NN_range (i.e., basic neighboring point distance × NN_range).

[0366] According to an embodiment, a maximum distance (ie, a diagonal distance) of a node in a specific octree-generated LOD may be defined as a basic distance (or a basic neighboring point distance).

[0367] According to an embodiment, when a LOD l Set) to search for the previous LOD (for example, LOD0 to LOD in the retention list l-1 ), the diagonal distance of the higher node (parent node) of the octree node of the current LOD can be the basic distance for obtaining the maximum neighboring point distance.

[0368] According to an embodiment, a basic distance that is a diagonal distance (ie, a maximum distance) of a node at a specific LOD may be obtained as in Equation 6 below.

[0369] [Formula 6]

[0370]

[0371] According to another embodiment, a basic distance that is a diagonal distance (ie, a maximum distance) of a node at a specific LOD may be obtained based on L2 as shown in Equation 7 below.

[0372] [Formula 7]

[0373]

[0374] Typically, when calculating the distance between two points, the Manhattan distance calculation method or the Euclidean distance calculation method is used. Manhattan distance will be referred to as L1 distance and Euclidean distance will be referred to as L2 distance. Euclidean space can be defined using Euclidean distance, and the norm corresponding to this distance will be referred to as the Euclidean norm or L2 norm. The norm is a method (function) that measures the length or size of a vector.

[0375] Figure 23 (a) illustrates an example of obtaining the diagonal distance of the octree node of LOD0. According to an embodiment, when Figure 23 When the example of (a) is applied to Equation 6, the basic distance becomes And when Figure 23 When the example of (a) is applied to Equation 7, the basic distance becomes 3.

[0376] Figure 23 (b) illustrates an example of calculating the diagonal distance of the octree node of LOD1. According to an embodiment, when Figure 23 When the example (b) is applied to Equation 6, the basic distance becomes And when Figure 23 When the example of (b) is applied to Equation 7, the basic distance becomes 12.

[0377] Figure 24 (a) and Figure 24 (b) illustrates an example of a range NN_range that may be selected as a neighboring point at each LOD. More specifically, Figure 24 (a) illustrates an example when NN_range is 1 at LOD0, and Figure 24(b) illustrates an example when NN_range is 3 at LOD0. According to an embodiment, NN_range may be set automatically or manually according to the characteristics of the content, or may be set by input as a user parameter. Information about NN_range may be signaled in signaling information. The signaling information including information about NN_range may be at least one of an SPS, an APS, a tile parameter set, or an attribute slice header. According to an embodiment, the range NN_range that may be set to a neighboring point may be set to an arbitrary value (e.g., a multiple of a basic distance) regardless of the octree node range.

[0378] According to an embodiment, when Figure 24 When example (a) is applied to Equations 5 and 6, the maximum neighbor distance becomes And when Figure 24 When example (b) is applied to Equations 5 and 6, the maximum neighbor distance becomes

[0379] According to an embodiment, when Figure 24 When the example of (a) is applied to Equation 5 and Equation 7, the maximum neighboring point distance becomes 3 (=3×1), and when Figure 24 When the example of (b) is applied to Equation 5 and Equation 7, the maximum neighboring point distance becomes 9 (=3×3).

[0380] Next, an embodiment of acquiring a basic neighboring point distance and a maximum neighboring point distance when generating a distance-based LOD will be described.

[0381] According to an embodiment, when configuring a distance-based LOD, a basic neighbor distance may be set to be used for the current LOD (ie, LOD l ) is configured at the distance of the point. That is, the basic distance of each LOD can be set to the distance applied to the LOD generation at the L level of the LOD (dist2 L ).

[0382] Figure 25 An example of a basic neighboring point distance belonging to each LOD according to an embodiment is illustrated.

[0383] Therefore, when configuring distance-based LOD, you can do so by placing the distance-based base neighbor at distance dist2 L Multiply by NN_range to get the maximum neighbor distance.

[0384] According to an embodiment, NN_range can be automatically or manually set by the neighboring point set configuration unit 53008 according to the characteristics of the content, or can be set by input as a user parameter. Information about NN_range can be signaled in signaling information. The signaling information including information about NN_range can be at least one of SPS, APS, tile parameter set, or attribute slice header. According to an embodiment, the NN_range that can be set to a neighboring point can be set to an arbitrary value (for example, a multiple of the basic distance). According to an embodiment, the NN_Range can be adjusted according to the method of calculating the distance to the neighboring point.

[0385] Next, an embodiment of acquiring a basic neighbor distance and a maximum neighbor distance when generating a sampling-based LOD will be described.

[0386] According to an embodiment, when generating a sampling-based (i.e., decimation-based) LOD, the basic distance may be determined according to the sampling rate. According to an embodiment, in the sampling-based LOD configuration method, a Morton code is generated based on the position value of the point, the points are arranged according to the Morton code, and then the points that do not correspond to the kth point according to the arrangement order are registered in the current LOD. Therefore, each LOD may have a different k and may be represented as k L .

[0387] According to an embodiment, when LOD is generated by applying a sampling method to points arranged based on Morton codes, the kth consecutive points at each LOD are L The average distance of the points can be set as the basic neighboring point distance at each LOD. That is, the kth consecutive points at the current LOD arranged based on the Morton code L The average distance of the points can be set as the basic neighboring point distance of the current LOD. According to another embodiment, when generating the LOD by applying a sampling method to points arranged based on Morton codes, the depth level of the octree can be estimated from the current LOD level, and the diagonal length of the node of the estimated octree depth level can be set as the basic neighboring point distance. That is, the basic neighboring point distance can be obtained by applying the above formula 6 or formula 7.

[0388] Therefore, when configuring sample-based LOD, the maximum neighbor distance can be obtained by multiplying the sample-based basic neighbor distance by NN_range.

[0389] According to an embodiment, NN_range can be automatically or manually set by the neighboring point set configuration unit 53008 according to the characteristics of the content, or can be set by input as a user parameter. Information about NN_range can be signaled in signaling information. The signaling information including information about NN_range can be at least one of SPS, APS, tile parameter set, or attribute slice header. According to an embodiment, the NN_range that can be set to a neighboring point can be set to an arbitrary value (for example, a multiple of the basic distance). According to an embodiment, the NN_Range can be adjusted according to the method of calculating the distance to the neighboring point.

[0390] In the present disclosure, a method other than the above-described method of acquiring the basic neighboring point distance may be used to obtain the basic neighboring point distance.

[0391] For example, when configuring LOD, the basic neighbor distance can be configured by calculating the average difference of the Morton codes of the sampling points. In addition, the maximum neighbor distance can be obtained by multiplying the configured basic neighbor distance by NN_range.

[0392] As another example, regardless of whether a distance-based approach or a sampling-based approach is used, the basic neighbor distance can be configured by calculating the average distance difference of the currently configured LOD. In addition, the maximum neighbor distance can be obtained by multiplying the configured basic neighbor distance by NN_range.

[0393] As another example, the basic neighbor distance of each LOD may be received as a user parameter and then applied when obtaining the maximum neighbor distance. Thereafter, information about the received basic neighbor distance may be signaled in the signaling information.

[0394] As described above, the basic neighboring point distances can be obtained by applying the optimal basic neighboring point distance calculation method according to the LOD configuration method. In another embodiment, the method for calculating the basic neighboring point distances can be applied by selecting any desired method and signaling the selected method in signaling information, so that the method can be used to restore the points in the decoder of the receiving device. Depending on the embodiment, a method for calculating the maximum neighboring point distance that is suitable for the properties of the point cloud content and can maximize compression performance can be used.

[0395] The above-mentioned LOD configuration method, basic neighbor distance acquisition method, NN_range acquisition method and maximum neighbor distance acquisition method can be applied in the same or similar manner when configuring LOD or neighbor point sets for predicting transformation or lifting transformation.

[0396] According to an embodiment, if the LOD1 set is generated as described above and the maximum neighboring point distance is determined, the neighboring point set configuration unit 53008 searches for X (e.g., 3) NN points among the points within the search range in the group having the same or lower LOD (i.e., the distance between the nodes is large) based on the LOD1 set. Then, the neighboring point set configuration unit 53008 can register only the NN points within the maximum neighboring point distance among the X (e.g., 3) NN points as the neighboring point set. Therefore, the number of NN points registered as the neighboring point set is equal to or less than X. In other words, the NN points that are not within the maximum neighboring point distance among the X NN points are not registered as the neighboring point set and are excluded from the neighboring point set. For example, if two of the three NN points are not within the maximum neighboring point distance, these two NN points are excluded and only the other NN point (i.e., one NN point) is registered as the neighboring point set.

[0397] According to another embodiment, if the LOD1 set is generated as described above and the maximum neighboring point distance is determined, the neighboring point set configuration unit 53008 can search for X (e.g., 3) NN points within the maximum neighboring point distance among the points within the search range in the group with the same or lower LOD (i.e., the distance between the nodes is large) based on the LOD1 set, and register these X NN points as the neighboring point set.

[0398] That is, the number of NN points that can be registered as a neighboring point set may vary depending on the timing (or location) at which the maximum neighbor distance is applied. In other words, the number of NN points that can be registered as a neighboring point set may vary depending on whether the maximum neighbor distance is applied after searching for X NN points through a neighbor search or when calculating the distance between points. X is the maximum number of neighbors that can be set and can be input as a user parameter or signaled in signaling information by the signaling processor 51005 (e.g., the lifting_num_pred_nearest_neighbours field signaled in the APS).

[0399] Figure 26 : is a diagram illustrating an example of searching for neighboring points by applying a search range and a maximum neighboring point distance according to an embodiment. The arrows illustrated in the diagram indicate a Morton order according to an embodiment.

[0400] exist Figure 26 In an embodiment, the index list includes the LOD set to which the point to be coded belongs (ie, LOD l ) and the reserved list includes LOD-based l At least one lower LOD set of the set (e.g., LOD0 to LOD l-1 ).

[0401] According to an embodiment, the points in the index list and the points in the reserved list are arranged in ascending order based on the size of the Morton code. Therefore, the first point in the points in the index list and the reserved list arranged in Morton order has the smallest Morton code size.

[0402] According to the embodiment, the neighboring point set configuration unit 53008 may be configured in the LOD0 to LOD l-1 Among the points of the set and / or the points belonging to the LOD1 set, a center point Pi having a Morton code closest to the Morton code of the point Px among the points sequentially preceding the point Px (i.e., points having a Morton code less than or equal to the Morton code of the point Px) is searched for, so as to register the points belonging to the LOD l The set of neighboring points of the point Px (ie, the point to be encoded or the current point).

[0403] According to an embodiment, when searching for a central point Pi, the neighboring point set configuration unit 53008 may search for a point Pi having a Morton code closest to that of point Px among all points preceding point Px, or may search for a point Pi having a Morton code closest to that of point Px among the points within the search range. In the present disclosure, the search range may be configured by the neighboring point set configuration unit 53008 or may be input as a user parameter. In addition, information about the search range may be signaled in signaling information via the signaling processor 51005. The present disclosure provides an embodiment for searching for the central point Pi in a reserved list when the number of LODs is multiple and for searching for the central point Pi in an index list when the number of LODs is 1. For example, when the number of LODs is 1, the search range may be determined based on the position of the current point in the list arranged by Morton code.

[0404] The neighboring point set configuration unit 53008 according to the embodiment compares the point Px with the points located before the searched (or selected) center point Pi (i.e., before Figure 26 to the left of the center point in ) and after the searched (or selected) center point Pi (i.e., Figure 26 The distance value between the points belonging to the neighboring point search range (to the right of the center point in ). The neighboring point set configuration unit 53008 can select X (for example, 3) NN points and register only the points within the maximum neighboring point distance at the LOD to which the point Px belongs among the selected X points as the neighboring point set of the point Px. In an embodiment, the neighboring point search range is the number of points. The neighboring point search range according to the embodiment may include one or more points located before (i.e., in front of) and / or after (i.e., after) the center point Pi in the Morton order. X is the maximum number of points that can be registered as neighboring points.

[0405] According to an embodiment, information about the neighboring point search range and information about the maximum number X of points that can be registered as neighboring points can be configured by the neighboring point set configuration unit 53008, or can be input as a user parameter, or signaled in the signaling information through the signaling processor 51005 (for example, the lifting_search_range field and the lifting_num_pred_nearest_neighbours field signaled in the APS). According to an embodiment, the actual search range can be a value obtained by multiplying the value of the lifting_search_range field by 2 and then adding the resulting value to the center point (i.e., (the value of the lifting_search_range field × 2) + the center point), a value obtained by adding the center value to the value of the lifting_search_range field (i.e., the value of the lifting_search_range field + the center point), or the value of the lifting_search_range field. The present disclosure provides an embodiment for searching for neighboring points of a point Px in a reserved list when the number of LODs is multiple and searching for neighboring points of a point Px in an index list when the number of LODs is 1.

[0406] For example, if the number of LODs is 2 or more and the information about the search range (e.g., lifting_search_range field) is 128, the actual search range includes the center point Pi in the retention list arranged by the Morton code, the 128 points before the center point Pi, and the 128 points after the center point Pi. As another example, if the number of LODs is 1 and the information about the search range is 128, the actual search range includes the center point Pi in the index list arranged by the Morton code and the 128 points before the center point Pi. As another example, if the number of LODs is 1 and the information about the search range is 128, the actual search range includes the 128 points before the current point Px in the index list arranged by the Morton code.

[0407] According to an embodiment, the neighboring point set configuration unit 53008 may compare the distance values ​​between the points in the actual search range and the point Px to search for X NN points, and register only the points within the maximum neighboring point distance at the LOD to which the point Px belongs among the X points as the neighboring point set of the point Px. In other words, the neighboring points registered as the neighboring point set of the point Px are limited to the points within the maximum neighboring point distance at the LOD to which the point Px belongs among the X points.

[0408] As described above, the present disclosure can provide an effect of enhancing compression efficiency of attributes by limiting points that can be selected as a neighboring point set in consideration of attribute characteristics (correlations) between points of point cloud content.

[0409] Figure 27 1 is a diagram illustrating another example of searching for neighboring points by applying a search range and a maximum neighboring point distance according to an embodiment. The arrows illustrated in the diagram indicate a Morton order according to an embodiment.

[0410] because Figure 27 The example is the same as , except that the maximum neighbor distance is applied Figure 26 The examples are similar, so the repeated description thereof will be omitted here. Figure 27 For any omitted or undescribed parts, refer to Figure 26 Description.

[0411] According to an embodiment, the points of the index list and the points of the reserved list are arranged in ascending order based on the size of the Morton code.

[0412] According to the embodiment, the neighboring point set configuration unit 53008 may be configured in the LOD0 to LOD l-1 Among the points of the set and / or the points belonging to the LOD1 set, a center point Pi having a Morton code closest to the Morton code of the point Px among the points sequentially preceding the point Px (i.e., points having a Morton code less than or equal to the Morton code of the point Px) is searched for, so as to register the points belonging to the LOD l The set of neighboring points of the point Px (ie, the point to be encoded or the current point).

[0413] The neighboring point set configuration unit 53008 according to the embodiment compares the point Px with the points located before the searched (or selected) center point Pi (i.e., before Figure 27 to the left of the center point in ) and after the searched (or selected) center point Pi (i.e., Figure 27 The neighboring point set configuration unit 53008 may select X (e.g., 3) NN points within the maximum neighboring point distance of the LOD to which point Px belongs and register these X points as the neighboring point set of point Px.

[0414] The present disclosure provides an embodiment of searching for neighboring points of point Px in an actual search range of a reserved list when the number of LODs is plural, and searching for neighboring points of point Px in an actual search range of an index list when the number of LODs is 1.

[0415] For example, if the number of LODs is 2 or more and the information about the search range (e.g., lifting_search_range field) is 128, the actual search range includes the center point Pi in the retention list arranged by the Morton code, the 128 points before the center point Pi, and the 128 points after the center point Pi. As another example, if the number of LODs is 1 and the information about the search range is 128, the actual search range includes the center point Pi in the index list arranged by the Morton code and the 128 points before the center point Pi. As another example, if the number of LODs is 1 and the information about the search range is 128, the actual search range includes the 128 points before the current point Px in the index list arranged by the Morton code.

[0416] When comparing the distance values ​​between the points in the actual search range and the point Px, the neighboring point set configuration unit 53008 according to an embodiment selects X (e.g., 3) NN points within the maximum neighboring point distance at the LOD to which the point Px belongs from among the points in the actual search range, and registers these X points as the neighboring point set of the point Px. That is, the neighboring points registered as the neighboring point set of the point Px are limited to the points within the maximum neighboring point distance at the LOD to which the point Px belongs among the X points.

[0417] As described above, the present disclosure can provide an effect of enhancing compression efficiency of attributes by selecting a neighboring point set in consideration of attribute characteristics (correlations) between points of point cloud content.

[0418] According to the above embodiment, when a neighboring point set is registered in each predictor of a point to be encoded in the neighboring point set configuration unit 53008, the attribute information prediction unit 53009 predicts the attribute value of the corresponding point from one or more neighboring points registered in the predictor. As described above, when configuring the neighboring point set, by applying the maximum neighboring point distance, the number of neighboring points included in the neighboring point set registered in each predictor is equal to or less than X (for example, 3). According to an embodiment, the predictor of a point can register a 1 / 2 distance (=weight) based on the distance value to each neighboring point with the help of the registered neighboring point set. For example, with (P2 P4 P6) as the neighboring point set, the predictor of the P3 node calculates the weight based on the distance value to each neighboring point. According to an embodiment, the weight of the neighboring point can be (1 / √(P2-P3) 2 ,1 / √(P4-P3) 2 ,1 / √(P6-P3) 2 ).

[0419] According to an embodiment, when a neighboring point set is registered in the predictor, the neighboring point set configuration unit 53008 or the attribute information prediction unit 53009 may normalize the weight of each neighboring point by the total weight of the neighboring points included in the neighboring point set.

[0420] For example, the weights of all neighboring points in the neighbor set of node P3 are summed (total_weight = 1 / √(P2-P3) 2 +1 / √(P4-P3) 2 +1 / √(P6-P3) 2 ), and the weight of each neighboring point is divided by the sum of the weights Thus, the weight of each neighboring point is normalized.

[0421] Then, the attribute information prediction unit 5301 may predict the attribute value through the predictor.

[0422] According to an embodiment, the average of the values ​​obtained by multiplying the attributes (e.g., color, reflectivity, etc.) of the neighboring points registered in the predictor by a weight (or normalized weight) can be set as the prediction result (i.e., predicted attribute value). Alternatively, the attribute of a specific point can be set as the prediction result (i.e., predicted attribute value). According to an embodiment, the predicted attribute value can be referred to as predicted attribute information. In addition, the predicted attribute value (or predicted attribute information) of the point can be subtracted from the attribute value (i.e., original attribute value) of the point to obtain a residual attribute value (or residual attribute information or residual).

[0423] According to an embodiment, a compression result value may be pre-calculated by applying various prediction modes (or predictor indexes), and then a prediction mode (ie, predictor index) generating a minimum bitstream may be selected from among the prediction modes.

[0424] Next, the process of selecting a prediction mode will be described in detail.

[0425] In this specification, the prediction mode has the same meaning as the predictor index (Preindex) and may be broadly referred to as a prediction method.

[0426] In an embodiment, the attribute information prediction unit 53009 may perform a process of finding the most appropriate prediction mode for each point and setting the found prediction mode in the predictor of the corresponding point.

[0427] According to an embodiment, a prediction mode in which a predicted attribute value is calculated by weighted averaging (i.e., an average obtained by multiplying the attribute of the neighboring points set in the predictor of each point by a weight calculated based on the distance to each neighboring point) will be referred to as prediction mode 0. In addition, a prediction mode in which the attribute of the first neighboring point is set as the predicted attribute value will be referred to as prediction mode 1, a prediction mode in which the attribute of the second neighboring point is set as the predicted attribute value will be referred to as prediction mode 2, and a prediction mode in which the attribute of the third neighboring point is set as the predicted attribute value will be referred to as prediction mode 3. In other words, a value of the prediction mode (or predictor index) equal to 0 may indicate that the attribute value is predicted by weighted averaging, and a value equal to 1 may indicate that the attribute value is predicted by the first neighboring node (i.e., neighboring point). A value equal to 2 may indicate that the attribute value is predicted by the second neighboring node, and a value equal to 3 may indicate that the attribute value is predicted by the third neighboring node.

[0428] According to an embodiment, the residual attribute value in prediction mode 0, the residual attribute value in prediction mode 1, the residual attribute value in prediction mode 2, and the residual attribute value in prediction mode 3 may be calculated, and a score or a double score may be calculated based on each residual attribute value. Then, the prediction mode with the lowest calculated score may be selected and set as the prediction mode for the corresponding point.

[0429] According to an embodiment, when a preset condition is met, a process of searching for the most appropriate prediction mode among multiple prediction modes and setting it as the prediction mode for the corresponding point may be performed. Therefore, when the preset condition is not met, a fixed (or predefined) prediction mode (e.g., prediction mode 0) in which the prediction attribute value is calculated by weighted average may be set as the prediction mode for the point without performing the process of searching for the most appropriate prediction mode. In an embodiment, this process is performed for each point.

[0430] According to an embodiment, a preset condition may be satisfied for a specific point when the difference in attribute elements (e.g., R, G, B) between neighboring points registered in the predictor of the point is greater than or equal to a preset threshold (e.g., lifting_adaptive_prediction_threshold), or when the difference in attribute elements (e.g., R, G, B) between neighboring points registered in the predictor of the point is calculated and the sum of the maximum difference in the elements is greater than or equal to the preset threshold. For example, assume that point P3 is a specific point and points P2, P4, and P6 are registered as neighboring points of point P3. Also, assume that when the differences in R, G, and B values ​​between points P2 and P4, the differences in R, G, and B values ​​between points P2 and P6, and the differences in R, G, and B values ​​between points P4 and P6 are calculated, the maximum difference in R values ​​is obtained between points P2 and P4, the maximum difference in G values ​​is obtained between points P4 and P6, and the maximum difference in B values ​​is obtained between points P2 and P6. In addition, it is assumed that among the maximum difference in R value (between P2 and P4), the maximum difference in G value (between P4 and P6), and the maximum difference in B value (between P2 and P6), the difference in R value between points P2 and P4 is the largest.

[0431] Under these assumptions, when the R value difference between points P2 and P4 is greater than or equal to a preset threshold, or when the sum of the R value difference between points P2 and P4, the G value difference between points P4 and P6, and the B value difference between points P2 and P6 is greater than or equal to the preset threshold, a process of searching for the most appropriate prediction mode among a plurality of candidate prediction modes may be performed. In addition, a prediction mode (e.g., predIndex) may be signaled only when the R value difference between points P2 and P4 is greater than or equal to the preset threshold, or when the sum of the R value difference between points P2 and P4, the G value difference between points P4 and P6, and the B value difference between points P2 and P6 is greater than or equal to the preset threshold.

[0432] According to another embodiment, when the maximum difference between the values ​​of the attribute (e.g., reflectivity) of the neighboring points registered in the predictor of a specific point is greater than or equal to a preset threshold (e.g., lifting_adaptive_prediction_threshold), the preset condition may be satisfied for the point. In addition, it is assumed that the reflectivity difference between points P2 and P4, the reflectivity difference between points P2 and P6, and the reflectivity difference between points P4 and P6 is the largest.

[0433] Under this assumption, when the reflectivity difference between points P2 and P4 is greater than or equal to a preset threshold, a process of searching for the most appropriate prediction mode among a plurality of candidate prediction modes may be performed. In addition, a prediction mode (e.g., predIndex) may be signaled only when the reflectivity difference between points P2 and P4 is greater than or equal to a preset threshold.

[0434] According to an embodiment, the selected prediction mode (e.g., predIndex) of the corresponding point may be signaled in the attribute slice data. In this case, the sending party sends the residual attribute value obtained based on the selected prediction mode, and the receiving party obtains the predicted attribute value of the corresponding point based on the signaled prediction mode, and adds the predicted attribute value and the received residual attribute value to restore the attribute value of the corresponding point.

[0435] In another embodiment, when the prediction mode is not signaled, the sender may calculate the predicted attribute value based on the prediction mode set as the default mode (e.g., prediction mode 0), and calculate and send the residual attribute value based on the difference between the original attribute value and the predicted attribute value. The receiver may calculate the predicted attribute value based on the prediction mode set as the default mode (e.g., prediction mode 0), and restore the attribute value by adding the predicted attribute value to the received residual attribute value.

[0436] According to an embodiment, the threshold value may be signaled in the signaling information or directly input through the signaling processor 51005 (eg, the lifting_adaptive_prediction_threshold field signaled in the APS).

[0437] According to an embodiment, when a preset condition is satisfied for a specific point as described above, a predictor candidate may be generated. The predictor candidate is referred to as a prediction mode or a predictor index.

[0438] According to an embodiment, prediction modes 1 to 3 may be included in the predictor candidate. According to an embodiment, prediction mode 0 may be included or may not be included in the predictor candidate. According to an embodiment, at least one prediction mode not mentioned above may also be included in the predictor candidate.

[0439] The prediction mode set for each point through the above-mentioned processing and the residual attribute value in the set prediction mode are output to the residual attribute information quantization processor 53010.

[0440] According to an embodiment, the residual property information quantization processor 53010 may apply zero run length encoding to the input residual property value.

[0441] According to an embodiment of the present disclosure, quantization and zero-run-length encoding may be performed on the residual property value.

[0442] According to an embodiment, the arithmetic encoder 53011 applies arithmetic coding to the residual property value and the prediction mode output from the residual property information quantization processor 53010, and outputs the result as a property bitstream.

[0443] The geometry bit stream compressed and output by the geometry encoder 51006 and the attribute bit stream compressed and output by the attribute encoder 51007 are output to the transmission processor 51008.

[0444] According to the embodiment, the sending processor 51008 can perform Figure 12 The operations and / or sending methods of the sending processor 12012 are the same or similar to the operations and / or sending methods, and perform the same as Figure 1 The operation and / or transmission method of the transmitter 10003 is the same as or similar to the operation and / or transmission method. Figure 1 or Figure 12 Description.

[0445] According to an embodiment, the transmission processor 51008 can separately transmit the geometry bit stream output from the geometry encoder 51006, the attribute bit stream output from the attribute encoder 51007, and the signaling bit stream output from the signaling processor 51005, or can multiplex the bit streams into one bit stream to be transmitted.

[0446] The transmission processor 51008 according to an embodiment may encapsulate the bitstream in a file or a segment (eg, a streaming segment) and then transmit the encapsulated bitstream through various networks such as a broadcast network and / or a broadband network.

[0447] According to an embodiment, the signaling processor 51005 may generate and / or process signaling information and output it in the form of a bitstream to the transmission processor 51008. The signaling information generated and / or processed by the signaling processor 51005 will be provided to the geometry encoder 51006, the attribute encoder 51007, and / or the transmission processor 51008 for geometry encoding, attribute encoding, and transmission processing. Alternatively, the signaling processor 51005 may receive signaling information generated by the geometry encoder 51006, the attribute encoder 51007, and / or the transmission processor 51008.

[0448] In the present disclosure, signaling information may be signaled and sent in units of parameter sets (sequence parameter set (SPS), geometry parameter set (GPS), attribute parameter set (APS), tile parameter set (TPS), etc.). In addition, it may be signaled and sent based on coding units of each image such as slices or tiles. In the present disclosure, signaling information may include metadata (e.g., set values) related to point cloud data and may be provided to the geometry encoder 51006, the attribute encoder 51007, and / or the sending processor 51008 for geometry encoding, attribute encoding, and sending processing. Depending on the application, signaling information may also be defined on the system side such as a file format, dynamic adaptive streaming over HTTP (DASH), or MPEG Media Transport (MMT), or on the wired interface side such as a High-Definition Multimedia Interface (HDMI), DisplayPort, Video Electronics Standards Association (VESA), or CTA.

[0449] The method / apparatus according to the embodiment may signal relevant information to add / perform the operation of the embodiment. The signaling information according to the embodiment may be used in a sending device and / or a receiving device.

[0450] According to an embodiment of the present disclosure, information about the maximum number of predictors to be used for attribute prediction (lifting_max_num_direct_predictors), threshold information for enabling adaptive prediction of attributes (lifting_adaptive_prediction_threshold), option information based on neighbor selection, etc. can be signaled in at least one of a sequence parameter set, an attribute parameter set, a tile parameter set, or an attribute slice header. In addition, according to an embodiment, predictor index information (predIndex) indicating a prediction mode corresponding to a predictor candidate selected from a plurality of predictor candidates can be signaled in attribute slice data.

[0451] According to an embodiment, the neighbor selection related option information may include information related to NN_range (e.g., nearest_neighbor_max_range). According to an embodiment, the neighbor selection related option information may further include at least one of information about the maximum number of neighbors that can be set (e.g., lifting_num_pred_nearest_neighbours), information related to the search range (e.g., lifting_search_range), information about the LOD configuration method, and / or information related to the basic neighbor distance.

[0452] Similar to the point cloud video encoder of the sending device, the point cloud video decoder of the receiving device performs the same or similar LOD generation. l Collection, LOD-based l The decoder then decodes the received prediction mode and predicts the attribute value of the point based on the decoded prediction mode. Furthermore, after the received residual attribute value is decoded, the decoded residual attribute value can be added to the predicted attribute value to restore the attribute value of the point.

[0453] Figure 28 FIG. 4 is a diagram illustrating another exemplary point cloud receiving apparatus according to an embodiment.

[0454] According to an embodiment, a point cloud receiving apparatus may include a receiving processor 61001, a signaling processor 61002, a geometry decoder 61003, an attribute decoder 61004, and a post-processor 61005. Depending on the embodiment, the geometry decoder 61003 and the attribute decoder 61004 may be referred to as a point cloud video decoder. Depending on the embodiment, the point cloud video decoder may be referred to as a PCC decoder, a PCC decoding unit, a point cloud decoder, a point cloud decoding unit, etc.

[0455] The receiving processor 61001 according to an embodiment may receive a single bitstream, or may receive a geometry bitstream, an attribute bitstream, and a signaling bitstream separately. Upon receiving a file and / or segment, the receiving processor 61001 according to an embodiment may decapsulate the received file and / or segment and output the decapsulated file and / or segment as a bitstream.

[0456] When a single bitstream is received (or decapsulated), the receiving processor 61001 according to an embodiment can demultiplex the geometry bitstream, the attribute bitstream, and / or the signaling bitstream from the single bitstream. The receiving processor 61001 can output the demultiplexed signaling bitstream to the signaling processor 61002, output the geometry bitstream to the geometry decoder 61003, and output the attribute bitstream to the attribute decoder 61004.

[0457] When the geometry bitstream, attribute bitstream and / or signaling bitstream are received (or decapsulated) respectively, the receiving processor 61001 according to the embodiment can transmit the signaling bitstream to the signaling processor 61002, the geometry bitstream to the geometry decoder 61003, and the attribute bitstream to the attribute decoder 61004.

[0458] The signaling processor 61002 can parse signaling information (e.g., information contained in SPS, GPS, APS, TPS, metadata, etc.) from the input signaling bit stream, process the parsed information, and provide the processed information to the geometry decoder 61003, the attribute decoder 61004, and the post-processor 61005. In another embodiment, the signaling information contained in the geometry slice header and / or the attribute slice header can also be parsed by the signaling processor 61002 before the corresponding slice data is decoded. That is, when the point cloud data is as shown in FIG. Figure 16 As shown, when the sender is divided into tiles and / or slices, the TPS includes the number of slices included in each tile, so the point cloud video decoder according to the embodiment can check the number of slices and quickly parse the information for parallel decoding.

[0459] Therefore, the point cloud video decoder according to the present disclosure can quickly parse the bitstream containing point cloud data when it receives an SPS with a reduced data volume. The receiving device can decode the tiles after receiving them and can decode each slice based on the GPS and APS included in each tile. This maximizes decoding efficiency.

[0460] That is, the geometry decoder 61003 can perform a decode on the compressed geometry bitstream based on the signaling information (e.g., geometry-related parameters). Figure 15 The geometry is reconstructed by performing the inverse process of the operation of the geometry encoder 51006. The geometry recovered (or reconstructed) by the geometry decoder 61003 is provided to the attribute decoder 61004. The attribute decoder 61004 can perform the following operations on the compressed attribute bitstream based on the signaling information (e.g., attribute related parameters) and the reconstructed geometry. Figure 15 According to an embodiment, when point cloud data is as Figure 16 As shown in , when the sender is divided into tiles and / or slices, the geometry decoder 61003 and the attribute decoder 61004 perform geometry decoding and attribute decoding on a tile-by-tile and / or slice-by-slice basis.

[0461] Figure 29 is a detailed block diagram illustrating another example of a geometry decoder 61003 and an attribute decoder 61004 according to an embodiment.

[0462] Figure 29 The arithmetic decoder 63001, the octree reconstruction unit 63002, the geometric information prediction unit 63003, the inverse quantization processor 63004 and the coordinate inverse transformation unit 63005 included in the geometric decoder 61003 can perform Figure 11Some or all of the operations of the arithmetic decoder 11000, the octree synthesizer 11001, the surface approximation synthesis unit 11002, the geometry reconstruction unit 11003 and the coordinate inverse transformer 11004 may be performed, or Figure 13 The position restored by the geometry decoder 61003 is output to the post-processor 61005.

[0463] According to an embodiment, when information about the maximum number of predictors to be used for attribute prediction (lifting_max_num_direct_predictors), threshold information for enabling adaptive prediction of attributes (lifting_adaptive_prediction_threshold), neighboring point selection related information, etc. are signaled in at least one of a sequence parameter set (SPS), an attribute parameter set (APS), a tile parameter set (TPS), or an attribute slice header, they can be acquired by the signaling processor 61002 and provided to the attribute decoder 61004, or can be directly acquired by the attribute decoder 61004.

[0464] According to an embodiment, the attribute decoder 61004 may include an arithmetic decoder 63006, an LOD configuration unit 63007, a neighboring point set configuration unit 63008, an attribute information prediction unit 63009, a residual attribute information inverse quantization processor 63010 and an inverse color transform processor 63011.

[0465] The arithmetic decoder 63006 according to the embodiment can perform arithmetic decoding on the input attribute bit stream. The arithmetic decoder 63006 can decode the attribute bit stream based on the reconstructed geometry. The arithmetic decoder 63006 performs the same Figure 11 The arithmetic decoder 11005 or Figure 13 The operations and / or decoding of the arithmetic decoder 13007 are the same or similar operations and / or decoding.

[0466] According to an embodiment, the attribute bitstream output from the arithmetic decoder 63006 may be decoded based on the reconstructed geometric information through one or a combination of two or more of RAHT decoding, LOD-based prediction transform decoding, and lifting transform decoding.

[0467] This has been described as an embodiment in which the transmitting device performs attribute compression using one or a combination of LOD-based predictive transform coding and lifting transform coding. Therefore, an embodiment in which the receiving device performs attribute decoding using one or a combination of LOD-based predictive transform decoding and lifting transform decoding will be described. The description of RAHT decoding by the receiving device will be omitted.

[0468] According to an embodiment, the attribute bitstream arithmetically decoded by the arithmetic decoder 63006 is provided to the LOD configuration unit 63007. According to an embodiment, the attribute bitstream provided from the arithmetic decoder 63006 to the LOD configuration unit 63007 may contain a prediction mode and a residual attribute value.

[0469] The LOD configuration unit 63007 according to the embodiment generates one or more LODs in the same or similar manner as the LOD configuration unit 53007 of the transmitting device, and outputs the generated one or more LODs to the neighboring point set configuration unit 63008.

[0470] According to an embodiment, the LOD configuration unit 63007 may configure one or more LODs using one or more LOD generation methods (or LOD configuration methods). According to an embodiment, the LOD generation method used in the LOD configuration unit 63007 may be provided by the signaling processor 61002. For example, the LOD generation method may be signaled in the APS of the signaling information. According to an embodiment, the LOD generation method may be categorized into an octree-based LOD generation method, a distance-based LOD generation method, and a sampling-based LOD generation method.

[0471] According to an embodiment, a group with different LODs is referred to as LOD l Here, l represents LOD, which is an integer starting from 0. LOD0 is the set consisting of points with the largest distance between them. As l increases, the LOD l The distance between the points decreases.

[0472] According to an embodiment, the prediction mode and residual property value encoded by the transmitting device may be provided for each LOD or only for a leaf node.

[0473] In one embodiment, when the LOD configuration unit 63007 generates the LOD l When the neighboring point set configuration unit 63008 is set, the neighboring point set configuration unit 63008 can be based on the LOD l The neighboring points that are equal to or less than X (for example, 3) points are searched for in a group having the same or lower LOD (ie, the distance between nodes is large), and the searched neighboring points are registered in the predictor as a neighboring point set.

[0474] According to an embodiment, the neighbor point set configuration unit 63008 configures the neighbor point set by applying a search range and / or a maximum neighbor point distance based on signaling information.

[0475] According to an embodiment, the neighbor set configuration unit 63008 can obtain the maximum neighbor distance by multiplying the basic neighbor distance by NN_range. NN_range is a range within which neighbor points can be selected and will be referred to as the maximum neighbor range, neighbor range, or NN range.

[0476] The neighboring point set configuration unit 63008 according to an embodiment may automatically or manually set NN_range according to the characteristics of the content, or may be received through the signaling processor 61002. For example, information about NN_range may be signaled in at least one of an SPS, an APS, a tile parameter set, or an attribute slice header of the signaling information.

[0477] According to an embodiment, the neighboring point set configuration unit 63008 can calculate / configure the basic neighboring point distance by combining one or more of an octree-based method, a distance-based method, a sampling-based method, a Morton code average difference method, and an average distance difference-based method for each LOD. According to an embodiment, the neighboring point set configuration unit 63008 can receive information about the basic neighboring point distance through the signaling processor 61002, and the information about the basic neighboring point distance can be signaled in the APS of the signaling information.

[0478] Since a detailed description of calculating / configuring the basic neighboring point distance by combining one or more of an octree-based method, a distance-based method, a sampling-based method, a Morton code average difference method, and an average distance difference-based method for each LOD according to an embodiment has been given in detail in the above-mentioned process of encoding the attributes of the transmitting device, its details will be omitted here.

[0479] According to an embodiment, if the maximum neighboring point distance is determined as described above, the neighboring point set configuration unit 63008 is configured based on the LOD l Search X (eg, 3) NN points among the points within the search range in a group with the same or lower LOD (ie, the distance between nodes is large), as Figure 26Then, the neighboring point set configuration unit 63008 may register only the NN points within the maximum neighboring point distance among the X (e.g., 3) NN points as the neighboring point set. Therefore, the number of NN points registered as the neighboring point set is equal to or less than X. In other words, the NN points that are not within the maximum neighboring point distance among the X NN points are not registered as the neighboring point set and are excluded from the neighboring point set. For example, if two of the three NN points are not within the maximum neighboring point distance, the two NN points are excluded and only the other NN point (i.e., one NN point) is registered as the neighboring point set.

[0480] According to another embodiment, if the maximum neighbor distance is determined as described above, then Figure 27 , the neighboring point set configuration unit 53008 can search for X (e.g., 3) NN points within the maximum neighboring point distance among the points within the search range in the group with the same or lower LOD (i.e., the distance between nodes is large) based on the LOD1 set, and register these X NN points as the neighboring point set.

[0481] That is, the number of NN points that can be registered as a neighbor point set can vary depending on the timing (or position) at which the maximum neighbor point distance is applied. Figure 27 , the number of NN points registered as the neighboring point set may differ depending on whether the maximum neighboring point distance is applied after searching for X NN points through the neighboring point search or when calculating the distance between points.

[0482] refer to Figure 26 For example, the neighboring point set configuration unit 63008 may compare the distance values ​​between the points within the actual search range and the point Px to search for X NN points, and register only the points within the maximum neighboring point distance at the LOD to which the point Px belongs among the X points as the neighboring point set of the point Px. That is, the neighboring points registered as the neighboring point set of the point Px are limited to the points within the maximum neighboring point distance at the LOD to which the point Px belongs among the X points.

[0483] refer to Figure 27 For example, when comparing the distance values ​​between the points within the actual search range and the point Px, the neighboring point set configuration unit 63008 selects X (e.g., 3) NN points within the maximum neighboring point distance at the LOD to which the point Px belongs from among the points within the actual search range, and registers the selected points as the neighboring point set of the point Px. That is, the neighboring points registered as the neighboring point set of the point Px are limited to the points within the maximum neighboring point distance at the LOD to which the point Px belongs.

[0484] exist Figure 26 and Figure 27In the lifting_search_range field, the actual search range may be a value obtained by multiplying the value of the lifting_search_range field by 2 and then adding the resulting value to the center point (i.e., (the value of the lifting_search_range field × 2) + the center point), a value obtained by adding the center value to the value of the lifting_search_range field (i.e., the value of the lifting_search_range field + the center point), or the value of the lifting_search_range field. In an embodiment, the neighboring point set configuration unit 63008 searches for neighboring points of the point Px in the reserved list when the number of LODs is multiple, and searches for neighboring points of the point Px in the index list when the number of LODs is 1.

[0485] For example, if the number of LODs is 2 or more and the information about the search range (e.g., lifting_search_range field) is 128, the actual search range includes the center point Pi in the retention list arranged by the Morton code, the 128 points before the center point Pi, and the 128 points after the center point Pi. As another example, if the number of LODs is 1 and the information about the search range is 128, the actual search range includes the center point Pi in the index list arranged by the Morton code and the 128 points before the center point Pi. As another example, if the number of LODs is 1 and the information about the search range is 128, the actual search range includes the 128 points before the current point Px in the index list arranged by the Morton code.

[0486] For example, assume that the neighboring point set configuration unit 63008 selects points P2 P4 P6 as neighboring points of point P3 (i.e., node) belonging to LOD1, and registers the selected points as a neighboring point set in the predictor of P3 (see Figure 9 ).

[0487] According to an embodiment, when a neighboring point set is registered in each predictor of a to-be-decoded point in the neighboring point set configuration unit 63008, the attribute information prediction unit 63009 predicts the attribute value of the corresponding point from one or more neighboring points registered in each predictor. According to an embodiment, the attribute information prediction unit 63009 performs processing to predict the attribute value of the point based on the prediction mode of the point. This attribute prediction processing is performed for all or at least some points of the reconstructed geometry.

[0488] The prediction mode of a specific point according to an embodiment may be one of prediction mode 0 to prediction mode 3.

[0489] According to an embodiment, prediction mode 0 is a mode for calculating a predicted attribute value by weighted averaging, prediction mode 1 is a mode for determining an attribute of a first neighboring point as a predicted attribute value, prediction mode 2 is a mode for determining an attribute of a second neighboring point as a predicted attribute value, and prediction mode 3 is a mode for determining an attribute of a third neighboring point as a predicted attribute value.

[0490] According to an embodiment, when the maximum difference between the attribute values ​​of neighboring points registered in the predictor of a point is less than a preset threshold, the transmitting side's attribute encoder sets prediction mode 0 as the prediction mode for the point. When the maximum difference is greater than or equal to the preset threshold, the attribute encoder applies the RDO method to multiple candidate prediction modes and sets one of the candidate prediction modes as the prediction mode for the point. In an embodiment, this process is performed for each point.

[0491] According to an embodiment, the prediction mode (predIndex) of the point selected by applying the RDO method may be signaled in the attribute slice data. Accordingly, the prediction mode of the point may be obtained from the attribute slice data.

[0492] According to an embodiment, the attribute information prediction unit 63009 may predict the attribute value of each point based on the prediction mode of each point set as described above.

[0493] For example, when it is assumed that the prediction mode of point P3 is prediction mode 0, the average value of the values ​​obtained by multiplying the attributes of points P2, P4, and P6, which are neighboring points registered in the predictor of point P3, by weights (or normalized weights) can be calculated. The calculated average value can be determined as the predicted attribute value of the point.

[0494] As another example, when it is assumed that the prediction mode of the point P3 is prediction mode 1, the attribute value of the point P4 which is a neighboring point registered in the predictor of the point P3 may be determined as the predicted attribute value of the point P3.

[0495] As another example, when it is assumed that the prediction mode of the point P3 is prediction mode 2, the attribute value of the point P6 which is a neighboring point registered in the predictor of the point P3 may be determined as the predicted attribute value of the point P3.

[0496] As another example, when it is assumed that the prediction mode of the point P3 is prediction mode 3, the attribute value of the point P2 which is a neighboring point registered in the predictor of the point P3 may be determined as the predicted attribute value of the point P3.

[0497] Once the attribute information prediction unit 63009 obtains the predicted attribute value of the point based on the prediction mode of the point, the residual attribute information inverse quantization processor 63010 restores the attribute value of the point by adding the predicted attribute value of the point predicted by the prediction unit 63009 to the residual attribute value of the received point, and then performs inverse quantization as the inverse process of the quantization process of the sending device.

[0498] In an embodiment, when the sender applies zero-run-length encoding to the residual property value of a point, the residual property information inverse quantization processor 63010 performs zero-run-length decoding on the residual property value of the point and then performs inverse quantization.

[0499] The attribute value restored by the residual attribute information inverse quantization processor 63010 is output to the inverse color transformation processor 63011.

[0500] The inverse color transform processor 63011 performs inverse transform encoding on the inverse transform of the color value (or texture) included in the restored attribute value, and then outputs the attribute to the post-processor 61005. The inverse color transform processor 63011 performs the same Figure 11 The inverse color conversion unit 11010 or Figure 13 The operation and / or inverse transform encoding of the color inverse transform processor 13010 is the same as or similar to the operation and / or inverse transform encoding.

[0501] The post-processor 61005 can reconstruct the point cloud data by matching the position restored and output by the geometry decoder 61003 with the attribute restored and output by the attribute decoder 61004. In addition, when the reconstructed point cloud data is in units of tiles and / or slices, the post-processor 61005 can perform the inverse processing of the space segmentation of the sender based on the signaling information. For example, when Figure 16 (b) and Figure 16 As shown in (c) Figure 16 When the bounding box shown in (a) is divided into tiles and slices, the tiles and / or slices can be combined based on signaling information to restore the image as shown in FIG. Figure 16 The bounding box shown in (a).

[0502] Figure 30 An example of a bitstream structure for transmitted / received point cloud data according to an embodiment is illustrated.

[0503] Relevant information may be signaled to add / execute the above-mentioned embodiments. The signaling information according to the embodiments may be used in a point cloud video encoder at a transmitting end or a point cloud video decoder at a receiving end.

[0504] The point cloud video encoder according to the embodiment can generate a point cloud video by encoding the geometric information and attribute information as described above. Figure 32In addition, signaling information about the point cloud data may be generated and processed by at least one of a geometry encoder, an attribute encoder, or a signaling processor of the point cloud video encoder and may be included in the bitstream.

[0505] The signaling information according to the embodiment may be received / obtained by at least one of a geometry decoder, an attribute decoder, and a signaling processor of a point cloud video decoder.

[0506] A bitstream according to an embodiment may be divided into a geometry bitstream, an attribute bitstream, and a signaling bitstream to be transmitted / received, or may be combined into one bitstream and transmitted / received.

[0507] When the geometry bitstream, attribute bitstream and signaling bitstream according to the embodiment are configured as one bitstream, the bitstream may include one or more sub-bitstreams. The bitstream according to the embodiment may include a sequence parameter set (SPS) for sequence-level signaling, a geometry parameter set (GPS) for signaling of geometry information encoding, one or more attribute parameter sets (APS) (APS0, APS1) for signaling of attribute information encoding, a tile parameter set (TPS) for tile-level signaling, and one or more slices (slice 0 to slice n). That is, the bitstream of point cloud data according to the embodiment may include one or more tiles, and each tile may be a group of slices including one or more slices (slice 0 to slice n). The TPS according to the embodiment may contain information about each of the one or more tiles (for example, coordinate value information and height / size information about the bounding box). Each slice may include a geometry bitstream (Geom0) and one or more attribute bitstreams (Attr0 and Attr1). For example, the first slice (slice 0) may include a geometry bitstream (Geom0 0 ) and one or more attribute bitstreams (Attr 0 、Attr1 0 ).

[0508] The geometry bitstream (or geometry slice) in each slice may be composed of a geometry slice header (geom_slice_header) and geometry slice data (geom_slice_data). According to an embodiment, the geom_slice_header may include identification information (geom_parameter_set_id) of the parameter set included in the GPS, a tile identifier (geom_tile_id), and a slice identifier (geom_slice_id), as well as information (geomBoxOrigin, geom_box_log2_scale, geom_max_node_size_log2, geom_num_points) about the data contained in the geometry slice data (geom_slice_data). geomBoxOrigin is geometry box origin information indicating the origin of the box of the geometry slice data, geom_box_log2_scale is information indicating the logarithmic scale of the geometry slice data, geom_max_node_size_log2 is information indicating the size of the root geometry octree node, and geom_num_points is information related to the number of points of the geometry slice data. According to an embodiment, geom_slice_data may include geometric information (or geometric data) about point cloud data in a corresponding slice.

[0509] Each attribute bitstream (or attribute slice) in each slice can be composed of an attribute slice header (attr_slice_header) and attribute slice data (attr_slice_data). Depending on the embodiment, the attr_slice_header may include information about the corresponding attribute slice data. The attribute slice data may contain attribute information (or attribute data or attribute values) about the point cloud data in the corresponding slice. When there are multiple attribute bitstreams in a slice, each bitstream may contain different attribute information. For example, one attribute bitstream may contain attribute information corresponding to color, while another attribute stream may contain attribute information corresponding to reflectivity.

[0510] Figure 31 An exemplary bitstream structure of point cloud data according to an embodiment is shown.

[0511] Figure 32 The connection relationship between components in a bit stream of point cloud data according to an embodiment is illustrated.

[0512] Figure 31 and Figure 32 The bitstream structure of the point cloud data exemplified in can be represented by Figure 30 The bitstream structure of point cloud data is shown in FIG.

[0513] According to an embodiment, the SPS may include an identifier (seq_parameter_set_id) for identifying the SPS, and the GPS may include an identifier (geom_parameter_set_id) for identifying the GPS and an identifier (seq_parameter_set_id) indicating the active SPS to which the GPS belongs. The APS may include an identifier (attr_parameter_set_id) for identifying the APS and an identifier (seq_parameter_set_id) indicating the active SPS to which the APS belongs.

[0514] According to an embodiment, a geometry bitstream (or geometry slice) may include a geometry slice header and geometry slice data. The geometry slice header may include an identifier (geom_parameter_set_id) of an active GPS to be referenced by the corresponding geometry slice. In addition, the geometry slice header may also include an identifier (geom_slice_id) for identifying the corresponding geometry slice and / or an identifier (geom_tile_id) for identifying the corresponding tile. The geometry slice data may include geometry information belonging to the corresponding slice.

[0515] According to an embodiment, an attribute bitstream (or attribute slice) may include an attribute slice header and attribute slice data. The attribute slice header may include an identifier (attr_parameter_set_id) of the active APS to be referenced by the corresponding attribute slice and an identifier (geom_slice_id) for identifying a geometric slice related to the attribute slice. The attribute slice data may include attribute information belonging to the corresponding slice.

[0516] That is, the geometry slice references the GPS, which references the SPS. Additionally, the SPS lists the available attributes, assigns identifiers to each attribute, and identifies the decoding method. Based on the identifiers, the attribute slices are mapped to output attributes. The attribute slices depend on the previously (decoded) geometry slice and the APS. The APS references the SPS.

[0517] Depending on the implementation, parameters necessary for encoding point cloud data may be newly defined in the parameter set of the point cloud data and / or the corresponding slice header. For example, when encoding attribute information, parameters may be added to the APS. When performing tile-based encoding, parameters may be added to the tile and / or slice header.

[0518] like Figure 30 、 Figure 31 and Figure 32As shown in , the bitstream of point cloud data provides tiles or slices, so that the point cloud data can be segmented and processed by region. Depending on the embodiment, the corresponding regions of the bitstream can have different importance. Therefore, when the point cloud data is segmented into tiles, different filters (encoding methods) and different filter units can be applied to each tile. When the point cloud data is segmented into slices, different filters and different filter units can be applied to each slice.

[0519] When point cloud data is segmented and compressed, the transmitting apparatus and the receiving apparatus according to the embodiment may transmit and receive a bit stream in a high-level syntax structure for selectively transmitting attribute information in the segmented regions.

[0520] The transmitting device according to the embodiment may be configured as follows: Figure 30 、 Figure 31 and Figure 32 Point cloud data is transmitted using the bitstream structure shown in . Therefore, a method can be provided for applying different encoding operations and using a high-quality encoding method for important areas. Furthermore, efficient encoding and transmission can be supported based on the characteristics of the point cloud data, and attribute values ​​can be provided according to user requirements.

[0521] The receiving device according to the embodiment can be configured as follows: Figure 30 、 Figure 31 and Figure 32 The point cloud data is received using the bitstream structure shown in [1]. Therefore, different filtering (decoding) methods can be applied to individual regions (regions divided into tiles or slices) rather than applying a complex decoding (filtering) method to the entire point cloud data. This provides users with better image quality in important areas and ensures appropriate system latency.

[0522] As described above, tiles or slices are provided to process point cloud data by segmenting the point cloud data into regions. When segmenting the point cloud data into regions, an option can be set to generate a different set of neighboring points for each region. This allows for a selection method with low complexity and slightly lower reliability, or a selection method with high complexity and high reliability.

[0523] According to an embodiment, neighbor point selection-related option information of a sequence required in a process of encoding / decoding attribute information may be signaled in an SPS and / or an APS.

[0524] According to an embodiment, if there are tiles or slices with different attribute characteristics in the same sequence, the neighbor point selection related option information of the sequence may be signaled in the TPS and / or attribute slice header of each slice.

[0525] According to an embodiment, when point cloud data is divided into regions, the attribute characteristics of a specific region may be different from the attribute characteristics of a sequence, and thus may be configured differently through different maximum neighbor range configuration functions.

[0526] Therefore, when the point cloud data is divided into tiles, a different maximum neighboring point range can be applied to each tile. In addition, when the point cloud data is divided into slices, a different maximum neighboring point range can be applied to each slice.

[0527] According to an embodiment, at least one of the SPS, APS, TPS, or attribute slice header of each slice may include neighbor selection related option information. According to an embodiment, the neighbor selection related option information may include information about the maximum neighbor range (e.g., nearest_neighbor_max_range field).

[0528] According to an embodiment, the neighbor selection related option information may also include information about the maximum number of points that can be set as neighboring points (e.g., the lifting_num_pred_nearest_neighbours field), information about the search range (e.g., the lifting_search_range field), and at least one of information about the LOD configuration method or information about the basic neighbor distance.

[0529] A field, which is a term used in the syntax of the present disclosure described later, may have the same meaning as a parameter or an element.

[0530] Figure 33 An embodiment of the syntax structure of a sequence parameter set (SPS) (seq_parameter_set_rbsp()) according to the present disclosure is shown. The SPS may include sequence information about a point cloud data bitstream. In particular, in this example, the SPS includes neighbor point selection related option information.

[0531] The SPS according to an embodiment may include a profile_idc field, a profile_compatibility_flags field, a level_idc field, a sps_bound_box_present_flag field, a sps_source_scale_factor field, a sps_seq_parameter_set_id field, a sps_num_attribute_sets field, and a sps_extension_present_flag field.

[0532] The profile_idc field indicates the profile to which the bitstream conforms.

[0533] The profile_compatibility_flags field equal to 1 may indicate that the bitstream conforms to the profile indicated by profile_idc.

[0534] The level_idc field indicates the level to which the bitstream conforms.

[0535] The sps_bounding_box_present_flag field indicates whether source bounding box information is signaled in the SPS. The source bounding box information may include offset and size information about the source bounding box. For example, an sps_bounding_box_present_flag field equal to 1 indicates that source bounding box information is signaled in the SPS. An sps_bounding_box_present_flag field equal to 0 indicates that source bounding box information is not signaled. The sps_source_scale_factor field indicates the scaling factor of the source point cloud.

[0536] The sps_seq_parameter_set_id field provides an identifier of the SPS for reference by other syntax elements.

[0537] The sps_num_attribute_sets field indicates the number of coded attributes in the bitstream.

[0538] The sps_extension_present_flag field specifies whether the sps_extension_data syntax structure is present in the SPS syntax structure. For example, a sps_extension_present_flag field equal to 1 specifies that the sps_extension_data syntax structure is present in the SPS syntax structure. A sps_extension_present_flag field equal to 0 specifies that the syntax structure is not present. When not present, the value of the sps_extension_present_flag field is inferred to be 0.

[0539] When the sps_bounding_box_present_flag field is equal to 1, the SPS according to an embodiment may further include a sps_bounding_box_offset_x field, a sps_bounding_box_offset_y field, a sps_bounding_box_offset_z field, a sps_bounding_box_scale_factor field, a sps_bounding_box_size_width field, a sps_bounding_box_size_height field, and a sps_bounding_box_size_depth field.

[0540] The sps_bounding_box_offset_x field indicates the x offset of the source bounding box in Cartesian coordinates. When the x offset of the source bounding box does not exist, the value of sps_bounding_box_offset_x is 0.

[0541] The sps_bounding_box_offset_y field indicates the y offset of the source bounding box in Cartesian coordinates. When the y offset of the source bounding box does not exist, the value of sps_bounding_box_offset_y is 0.

[0542] The sps_bounding_box_offset_z field indicates the z offset of the source bounding box in Cartesian coordinates. When the z offset of the source bounding box does not exist, the value of sps_bounding_box_offset_z is 0.

[0543] The sps_bounding_box_scale_factor field indicates the scaling factor of the source bounding box in Cartesian coordinates. When the scaling factor of the source bounding box does not exist, the value of sps_bounding_box_scale_factor may be 1.

[0544] The sps_bounding_box_size_width field indicates the width of the source bounding box in Cartesian coordinates. When the width of the source bounding box does not exist, the value of the sps_bounding_box_size_width field may be 1.

[0545] The sps_bounding_box_size_height field indicates the height of the source bounding box in Cartesian coordinates. When the height of the source bounding box does not exist, the value of the sps_bounding_box_size_height field may be 1.

[0546] The sps_bounding_box_size_depth field indicates the depth of the source bounding box in Cartesian coordinates. When the depth of the source bounding box does not exist, the value of the sps_bounding_box_size_depth field may be 1.

[0547] An SPS according to an embodiment includes an iteration statement that is repeated as many times as the value of the sps_num_attribute_sets field. In an embodiment, i is initialized to 0 and incremented by 1 each time the iteration statement is executed. The iteration statement is repeated until the value of i becomes equal to the value of the sps_num_attribute_sets field. The iteration statement may include an attribute_dimension[i] field, an attribute_instance_id[i] field, an attribute_bitdepth[i] field, an attribute_cicp_colour_primaries[i] field, an attribute_cicp_transfer_characteristics[i] field, an attribute_cicp_matrix_coeffs[i] field, an attribute_cicp_video_full_range_flag[i] field, and a known_attribute_label_flag[i] field.

[0548] The attribute_dimension[i] field specifies the number of components of the i-th attribute.

[0549] The attribute_instance_id[i] field specifies the instance ID of the i-th attribute.

[0550] The attribute_bitdepth[i] field specifies the bit depth of the i-th attribute signal.

[0551] The attribute_cicp_colour_primaries[i] field indicates the chromaticity coordinates of the color attribute source primaries of the i-th attribute.

[0552] The attribute_cicp_transfer_characteristics[i] field indicates either a reference electro-optical transfer characteristic function of the color attribute as a function of source input linear optical intensity with a nominal real value range of 0 to 1, or the inverse of the reference electro-optical transfer characteristic function as a function of output linear optical intensity.

[0553] The attribute_cicp_matrix_coeffs[i] field describes the matrix coefficients used in deriving the luma and chroma signals from the green, blue, and red or Y, Z, and X primaries.

[0554] The attribute_cicp_video_full_range_flag[i] field indicates the black level and range of luminance and chrominance signals derived from E′Y, E′PB, and E′PR or E′R, E′G, and E′B real-valued component signals.

[0555] The known_attribute_label_flag[i] field specifies whether the known_attribute_label field or the attribute_label_four_bytes field is signaled for the i-th attribute. For example, a value of the known_attribute_label_flag[i] field equal to 0 specifies that the known_attribute_label field is signaled for the i-th attribute. A known_attribute_label_flag[i] field equal to 1 specifies that the attribute_label_four_bytes field is signaled for the i-th attribute.

[0556] The known_attribute_label[i] field specifies the attribute type. For example, a known_attribute_label[i] field equal to 0 specifies that the i-th attribute is color. A known_attribute_label[i] field equal to 1 specifies that the i-th attribute is reflectivity. A known_attribute_label[i] field equal to 2 specifies that the i-th attribute is a frame index.

[0557] The attribute_label_four_bytes field indicates a known attribute type with a 4-byte code.

[0558] In this example, the attribute_label_four_bytes field indicates color when equal to 0 and indicates reflectivity when equal to 1.

[0559] According to an embodiment, when the sps_extension_present_flag field is equal to 1, the SPS may further include a sps_extension_data_flag field.

[0560] The sps_extension_data_flag field can have any value.

[0561] According to an embodiment, the SPS may further include neighboring point selection related option information. According to an embodiment, the neighboring point selection related option information may include information related to NN_range.

[0562] According to an embodiment, the neighboring point selection related option information may be included in an iteration statement that is repeated as many times as the value of the above-mentioned sps_num_attribute_sets field.

[0563] That is, the iteration statement may further include a nearest_neighbour_max_range[i] field and a nearest_neighbour_min_range[i] field.

[0564] The nearest_neighbour_max_range[i] field may indicate the maximum neighboring point range to be applied when the i-th attribute of the corresponding sequence is compressed. Depending on the embodiment, the value of the nearest_neighbour_max_range[i] field may be used as the value of NN_range in Equation 5. Depending on the embodiment, the nearest_neighbour_max_range[i] field may be used to limit the distance of the points registered as neighboring points. For example, if the LOD is generated based on an octree, the value of the nearest_neighbour_max_range[i] field may be the number of octree nodes around the point.

[0565] The nearest_neighbour_min_range[i] field may indicate the minimum neighbor range when the i-th attribute of the sequence is compressed.

[0566] Figure 34 An embodiment of a syntax structure of a geometry parameter set (GPS) (geometry_parameter_set()) according to the present disclosure is shown. The GPS according to an embodiment may include information on a method of encoding geometric information about point cloud data included in one or more slices.

[0567] According to an embodiment, the GPS may include a gps_geom_parameter_set_id field, a gps_seq_parameter_set_id field, a gps_box_present_flag field, a unique_geometry_points_flag field, a neighborhood_context_restriction_flag field, an inferred_direct_coding_mode_enabled_flag field, a bitwise_occupancy_coding_flag field, an adjacent_child_contextualization_enabled_flag field, a log2_neighbour_avail_boundary field, a log2_intra_pred_max_node_size field, a log2_trisoup_node_size field, and a gps_extension_present_flag field.

[0568] The gps_geom_parameter_set_id field provides an identifier of the GPS for reference by other syntax elements.

[0569] The gps_seq_parameter_set_id field specifies the value of sps_seq_parameter_set_id for the active SPS.

[0570] The gps_box_present_flag field specifies whether additional bounding box information is provided in the geometry slice header with reference to the current GPS. For example, a gps_box_present_flag field equal to 1 may specify that additional bounding box information is provided in the geometry header with reference to the current GPS. Therefore, when the gps_box_present_flag field is equal to 1, the GPS may also include a gps_gsh_box_log2_scale_present_flag field.

[0571] The gps_gsh_box_log2_scale_present_flag field specifies whether the gps_gsh_box_log2_scale field is signaled in each geometry slice header that references the current GPS. For example, a gps_gsh_box_log2_scale_present_flag field equal to 1 may specify that the gps_gsh_box_log2_scale field is signaled in each geometry slice header that references the current GPS. As another example, a gps_gsh_box_log2_scale_present_flag field equal to 0 may specify that the gps_gsh_box_log2_scale field is not signaled in each geometry slice header and that a common scaling for all slices is signaled in the gps_gsh_box_log2_scale field of the current GPS.

[0572] When the gps_gsh_box_log2_scale_present_flag field is equal to 0, GPS may also include a gps_gsh_box_log2_scale field.

[0573] The gps_gsh_box_log2_scale field indicates the common scaling factor for the bounding box origin of all slices referenced to the current GPS.

[0574] The unique_geometry_points_flag field indicates whether all output points have unique locations. For example, a unique_geometry_points_flag field equal to 1 indicates that all output points have unique locations. A unique_geometry_points_flag field equal to 0 indicates that two or more of the output points can have the same location in all slices referenced to the current GPS.

[0575] The neighbor_context_restriction_flag field indicates the context used for octree occupancy coding. For example, a neighbor_context_restriction_flag field equal to 0 indicates that octree occupancy coding uses the context determined from the six neighboring parent nodes. A neighbor_context_restriction_flag field equal to 1 indicates that octree occupancy coding uses only the context determined from the sibling nodes.

[0576] The inferred_direct_coding_mode_enabled_flag field indicates whether the direct_mode_flag field is present in the geometry node syntax. For example, an inferred_direct_coding_mode_enabled_flag field equal to 1 indicates that the direct_mode_flag field may be present in the geometry node syntax. For example, an inferred_direct_coding_mode_enabled_flag field equal to 0 indicates that the direct_mode_flag field is not present in the geometry node syntax.

[0577] The bitwise_occupancy_coding_flag field indicates whether the bitwise contextualization of the syntax element occupancy map is used to encode the geometry node occupancy. For example, a bitwise_occupancy_coding_flag field equal to 1 indicates that the bitwise contextualization of the syntax element ocupancy_map is used to encode the geometry node occupancy. For example, a bitwise_occupancy_coding_flag field equal to 0 indicates that the dictionary-coded syntax element occupancy_byte is used to encode the geometry node occupancy.

[0578] The adjacent_child_contextualization_enabled_flag field indicates whether the adjacent children of the adjacent octree node are used for bitwise occupancy contextualization. For example, an adjacent_child_contextualization_enabled_flag field equal to 1 indicates that the adjacent children of the adjacent octree node are used for bitwise occupancy contextualization. For example, an adjacent_child_contextualization_enabled_flag field equal to 0 indicates that the children of the adjacent octree node are not used for occupancy contextualization.

[0579] The log2_neighbour_avail_boundary field specifies the value of the variable NeighbAvailBoundary used in the decoding process as follows:

[0580] NeighbAvailBoundary=2 log2_neighbour_avail_boundary .

[0581] For example, when the neighborhood_context_restriction_flag field is equal to 1, NeighbAvailabilityMask may be set to be equal to 1. For example, when the neighborhood_context_restriction_flag field is equal to 0, NeighbAvailabilityMask may be set to be equal to 1. <log2_neighbour_avail_boundary。

[0582] The log2_intra_pred_max_node_size field specifies the size of octree nodes that are eligible for intra prediction.

[0583] The log2_trisoup_node_size field specifies the variable TrisoupNodeSize as the size of the triangle nodes as follows:

[0584] TrisoupNodeSize=1< <log2_trisoup_node_size。

[0585] The gps_extension_present_flag field specifies whether the gps_extension_data syntax structure is present in the GPS syntax structure. For example, a gps_extension_present_flag equal to 1 specifies that the gps_extension_data syntax structure is present in the GPS syntax. For example, a gps_extension_present_flag equal to 0 specifies that the syntax structure is not present in the GPS syntax.

[0586] When the value of the gps_extension_present_flag field is equal to 1, the GPS according to an embodiment may further include a gps_extension_data_flag field.

[0587] The gps_extension_data_flag field can have any value. Its presence and value do not affect the conformance of the decoder to the profile.

[0588] Figure 35 An embodiment of the syntax structure of an attribute parameter set (APS) (attribute_parameter_set()) according to the present disclosure is shown. An APS according to an embodiment may include information about a method for encoding attribute information in point cloud data contained in one or more slices. According to an embodiment, an APS may include information about neighbor point selection related options.

[0589] The APS according to an embodiment may include an aps_attr_parameter_set_id field, an aps_seq_parameter_set_id field, an attr_coding_type field, an aps_attr_initial_qp field, an aps_attr_chroma_qp_offset field, an aps_slice_qp_delta_present_flag field, and an aps_flag field.

[0590] The aps_attr_parameter_set_id field provides an identifier of the APS for reference by other syntax elements.

[0591] The aps_seq_parameter_set_id field indicates the value of sps_seq_parameter_set_id of the active SPS.

[0592] The attr_coding_type field indicates the coding type of the attribute.

[0593] In this example, the attr_coding_type field equal to 0 indicates prediction weight lifting (or LOD with prediction transform) as the encoding type. The attr_coding_type field equal to 1 indicates RAHT as the encoding type. The attr_coding_type field equal to 2 indicates fixed weight lifting (or LOD with lifting transform).

[0594] The aps_attr_initial_qp field specifies the initial value of the variable SliceQp for each slice of the referenced APS. The initial value of SliceQp is modified at the attribute slice layer when a non-zero value of slice_qp_delta_luma or slice_qp_delta_luma is decoded.

[0595] The aps_attr_chroma_qp_offset field indicates the offset of the initial quantization parameter signaled by the syntax aps_attr_initial_qp.

[0596] The aps_slice_qp_delta_present_flag field indicates whether the ash_attr_qp_delta_luma and ash_attr_qp_delta_chroma syntax elements are present in the attribute slice header (ASH). For example, an aps_slice_qp_delta_present_flag field equal to 1 indicates that the ash_attr_qp_delta_luma and ash_attr_qp_delta_chroma syntax elements are present in the ASH. For example, an aps_slice_qp_delta_present_flag field equal to 0 indicates that the ash_attr_qp_delta_luma and ash_attr_qp_delta_chroma syntax elements are not present in the ASH.

[0597] When the value of the attr_coding_type field is 0 or 2, that is, when the encoding type is prediction weight lifting (or LOD with prediction transform) or fixed weight lifting (or LOD with lifting transform), the APS according to the embodiment may further include a lifting_num_pred_nearest_neighbours field, a lifting_max_num_direct_predictors field, a lifting_search_range field, a lifting_lod_regular_sampling_enabled_flag field, and a lifting_num_detail_levels_minus1 field.

[0598] The lifting_num_pred_nearest_neighbours field specifies the maximum number of nearest neighbors (ie, X values) to be used for prediction.

[0599] The lifting_max_num_direct_predictors field specifies the maximum number of predictors that will be used for direct prediction. The value of the variable MaxNumPredictors used in the decoding process is as follows:

[0600] MaxNumPredictors=lifting_max_num_direct_predictors field+1

[0601] The lifting_search_range field specifies the search range used to determine the nearest neighbor.

[0602] The lifting_num_detail_levels_minus1 field specifies the number of levels of detail that the attribute encodes.

[0603] The lifting_lod_regular_sampling_enabled_flag field indicates whether the level of detail (LOD) is constructed by using a regular sampling strategy. For example, lifting_lod_regular_sampling_enabled_flag equal to 1 indicates that the level of detail (LOD) is constructed by using a regular sampling strategy. Lifting_lod_regular_sampling_enabled_flag equal to 0 indicates that a distance-based sampling strategy is used instead.

[0604] The APS according to an embodiment includes an iteration statement that is repeated as many times as the value of the lifting_num_detail_levels_minus1 field. In an embodiment, the index (idx) is initialized to 0 and incremented by 1 each time the iteration statement is executed, and the iteration statement is repeated until the index (idx) is greater than the value of the lifting_num_detail_levels_minus1 field. When the value of the lifting_lod_decimation_enabled_flag field is true (e.g., 1), the iteration statement may include the lifting_sampling_period[idx] field, and when the value of the lifting_lod_decimation_enabled_flag field is false (e.g., 0), the iteration statement may include the lifting_sampling_distance_squared[idx] field.

[0605] The lifting_sampling_period[idx] field specifies the sampling period of the level of detail idx.

[0606] The lifting_sampling_distance_squared[idx] field specifies the square of the sampling distance of the level of detail idx.

[0607] When the value of the attr_coding_type field is 0, that is, when the encoding type is prediction weight lifting (or LOD with prediction transform), the APS according to an embodiment may further include a lifting_adaptive_prediction_threshold field and a lifting_intra_lod_prediction_num_layers field.

[0608] The lifting_adaptive_prediction_threshold field indicates the threshold for enabling adaptive prediction.

[0609] The lifting_intra_lod_prediction_num_layers field indicates the number of LOD layers that can reference decoded points in the same LOD layer to generate a predicted value for the target point. For example, a lifting_intra_lod_prediction_num_layers field equal to num_detail_levels_minus1 plus 1 indicates that for all LOD layers, the target point can reference decoded points in the same LOD layer. For example, a lifting_intra_lod_prediction_num_layers field equal to 0 indicates that for any LOD layer, the target point cannot reference decoded points in the same LOD layer.

[0610] The aps_extension_present_flag field indicates whether the aps_extension_data syntax structure is present in the APS syntax structure. For example, an aps_extension_present_flag field equal to 1 indicates that the aps_extension_data syntax structure is present in the APS syntax structure. For example, an aps_extension_present_flag field equal to 0 indicates that the syntax structure is not present in the APS syntax structure.

[0611] When a value of the aps_extension_present_flag field is 1, the APS according to an embodiment may further include an aps_extension_data_flag field.

[0612] The aps_extension_data_flag field can have any value. Its presence and value do not affect the conformance of the decoder to the profile.

[0613] The APS according to an embodiment may further include neighbor point selection related option information.

[0614] When the value of the attr_coding_type field is 0 or 2, that is, when the encoding type is prediction weight lifting or LOD with prediction transformation or fixed weight lifting or LOD with lifting transformation, the APS according to an embodiment may further include a nearest_neighbour_max_range field, a nearest_neighbour_min_rang field and a different_nn_range_in_tile_flag field.

[0615] The nearest_neighbour_max_range field may indicate the maximum neighboring point range. Depending on the embodiment, the nearest_neighbour_max_range field may be used to limit the distance of a point registered as a neighboring point. Depending on the embodiment, the value of the nearest_neighbour_max_range field may be used as the value of NN_range in Equation 5. For example, if the LOD is generated based on an octree, the value of the nearest_neighbour_max_range field may be the number of octree nodes surrounding the point.

[0616] When the attribute is compressed, the nearest_neighbour_min_range field may indicate the minimum neighbor range.

[0617] The different_nn_range_in_tile_flag field may indicate whether the corresponding sequence uses different maximum / minimum neighbor ranges for tiles divided from the sequence.

[0618] According to an embodiment, information about the maximum number that can be set as neighboring points (eg, a lifting_num_pred_nearest_neighbours field) and information about a search range (eg, a lifting_search_range field) may also be included in the neighboring point selection related option information.

[0619] According to an embodiment, the neighboring point selection related option information may further include at least one of information on an LOD configuration method and / or information on a basic neighboring point distance.

[0620] Figure 36This document illustrates another embodiment of the syntax structure (attribute_parameter_set()) of an attribute parameter set (APS) according to an embodiment. The APS according to an embodiment may include information regarding a method for encoding attribute information for point cloud data included in one or more slices. In particular, the document illustrates an example of including information related to neighboring point selection options.

[0621] In addition to the options related to neighbor point selection, Figure 36 The syntax structure of APS (attribute_parameter_set()) is the same as Figure 35 The syntax structure of APS (attribute_parameter_set()) is the same or similar. Figure 36 For any omitted or undescribed parts of the description, refer to Figure 35 .

[0622] The aps_attr_parameter_set_id field indicates the ID of the APS for reference by other syntax elements.

[0623] The aps_seq_parameter_set_id field indicates the value of sps_seq_parameter_set_id of the active SPS.

[0624] The attr_coding_type field indicates the coding type used for the attribute.

[0625] In an embodiment, if the value of the attr_coding_type field is 0, the encoding type may indicate prediction weight lifting or LOD with prediction transformation, and if the value of the attr_coding_type field is 1, the encoding type may indicate RAHT. If the value of the attr_coding_type field is 2, the encoding type may indicate fixed weight lifting or LOD with lifting transformation.

[0626] When the value of the attr_coding_type field is 0 or 2, that is, when the encoding type is prediction weight lifting or LOD with prediction transformation or fixed weight lifting or LOD with lifting transformation, the APS according to the embodiment may further include a lifting_num_pred_nearest_neighbours field, a lifting_max_num_direct_predictors field, a lifting_search_range field, a lifting_lod_regular_sampling_enabled_flag field and a lifting_num_detail_levels_minus1 field.

[0627] The lifting_num_pred_nearest_neighbours field indicates the maximum number of NNs (ie, X values) to be used for prediction.

[0628] The lifting_max_num_direct_predictors field indicates the maximum number of predictors to be used for direct prediction. A value of the MaxNumPredictors variable used in the point cloud data decoding process according to an embodiment may be expressed as follows.

[0629] MaxNumPredictors=lifting_max_num_direct_predictors field+1

[0630] The lifting_search_range field indicates the search range for determining the NN.

[0631] The lifting_lod_regular_sampling_enabled_flag field indicates whether a regular sampling strategy is used to construct LOD. For example, a value of the lifting_lod_regular_sampling_enabled_flag field equal to 1 indicates that a regular sampling strategy is used to construct LOD, and a value of the lifting_lod_regular_sampling_enabled_flag field equal to 0 indicates that a distance-based sampling strategy is used instead.

[0632] The lifting_num_detail_levels_minus1 field specifies the number of LODs used for attribute encoding.

[0633] An APS according to an embodiment includes an iteration statement that repeats as many times as the value of the lifting_num_detail_levels_minus1 field. In an embodiment, the index idx is initialized to 0 and incremented by 1 each time the iteration statement is executed. The iteration statement is repeated until the index idx is greater than the value of the lifting_num_detail_levels_minus1 field. When the value of the lifting_lod_decimation_enabled_flag field is true (e.g., 1), the iteration statement may include the lifting_sampling_period[idx] field, and when the value of the lifting_lod_decimation_enabled_flag field is false (e.g., 0), the iteration statement may include the lifting_sampling_distance_squared[idx] field.

[0634] The lifting_sampling_period[idx] field indicates the sampling period of LOD idx.

[0635] The lifting_sampling_distance_squared[idx] field specifies the square of the sampling distance of LOD idx.

[0636] When the value of the attr_coding_type field is 0, that is, when the encoding type is prediction weight lifting or LOD with prediction transformation, the APS according to the embodiment may further include a lifting_adaptive_prediction_threshold field and a lifting_intra_lod_prediction_num_layers field.

[0637] The lifting_adaptive_prediction_threshold field indicates the threshold for enabling adaptive prediction.

[0638] The lifting_intra_lod_prediction_num_layers field indicates the number of LOD layers that decoded points in the same LOD layer can refer to to generate the prediction value of the target point.

[0639] The APS according to an embodiment may further include neighbor point selection related option information.

[0640] When the value of the attr_coding_type field is 0 or 2, that is, when the encoding type is prediction weight lifting or LOD with prediction transformation or fixed weight lifting or LOD with lifting transformation, the APS according to an embodiment may further include a different_nn_range_in_tile_flag field and a different_nn_range_per_lod_flag field.

[0641] The different_nn_range_in_tile_flag field may indicate whether the corresponding sequence uses different maximum / minimum neighbor ranges for tiles divided from the sequence.

[0642] The different_nn_range_per_lod_flag field may indicate whether to use different maximum / minimum neighbor ranges for each LOD.

[0643] For example, when the value of the different_nn_range_per_lod_flag field is false, the APS may further include a nearest_neighbour_max_range field and a nearest_neighbour_min_range field.

[0644] The nearest_neighbour_max_range field may indicate the maximum neighboring point range. According to an embodiment, the nearest_neighbour_max_range field may be used to limit the distance of a point registered as a neighboring point. According to an embodiment, the value of the nearest_neighbour_max_range field may be used as the value of NN_range in Equation 5.

[0645] The nearest_neighbour_min_range field may indicate the minimum neighbor range.

[0646] For example, if the value of the different_nn_range_per_lod_flag field is true, the APS also includes an iteration statement that repeats as many times as the value of the lifting_num_detail_levels_minus1 field. In an embodiment, the index idx is initialized to 0 and incremented by 1 each time the iteration statement is executed. The iteration statement is repeated until the index idx is greater than the value of the lifting_num_detail_levels_minus1 field. The iteration statement may include the nearest_neighbour_max_range[idx] field and the nearest_neighbour_min_range[idx] field.

[0647] The nearest_neighbour_max_range[idx] field may indicate the maximum neighboring point range of LOD idx. Depending on the embodiment, the nearest_neighbour_max_range[idx] field may be used to limit the distance of points registered as neighbors of LOD idx. Depending on the embodiment, the value of the nearest_neighbour_max_range[idx] field may be used as the value of NN_range of LOD idx.

[0648] The nearest_neighbour_min_range[idx] field may indicate the minimum neighbor range of LOD idx.

[0649] According to an embodiment, information about the maximum number that can be set as neighboring points (eg, a lifting_num_pred_nearest_neighbours field) and information about a search range (eg, a lifting_search_range field) may also be included in the neighboring point selection related option information.

[0650] According to an embodiment, the neighboring point selection related option information may further include at least one of information on an LOD configuration method and / or information on a basic neighboring point distance.

[0651] Figure 37 An embodiment of the syntax structure of a tile parameter set (TPS) (tile_parameter_set()) according to the present disclosure is shown. Depending on the embodiment, the TPS may be referred to as a tile manifest. The TPS according to the embodiment includes information related to each tile. In particular, in this example, the TPS includes neighbor selection-related option information.

[0652] The TPS according to an embodiment includes a num_tiles field.

[0653] The num_tiles field indicates the number of tiles signaled for the bitstream. When not present, num_tiles is inferred to be 0.

[0654] The TPS according to an embodiment includes an iteration statement that repeats as many times as the value of the num_tiles field. In an embodiment, i is initialized to 0 and incremented by 1 each time the iteration statement is executed. The iteration statement is repeated until the value of i becomes equal to the value of the num_tiles field. The iteration statement may include a tile_bounding_box_offset_x[i] field, a tile_bounding_box_offset_y[i] field, a tile_bounding_box_offset_z[i] field, a tile_bounding_box_size_width[i] field, a tile_bounding_box_size_height[i] field, and a tile_bounding_box_size_depth[i] field.

[0655] The tile_bounding_box_offset_x[i] field indicates the x offset of the i-th tile in Cartesian coordinates.

[0656] The tile_bounding_box_offset_y[i] field indicates the y offset of the i-th tile in Cartesian coordinates.

[0657] The tile_bounding_box_offset_z[i] field indicates the z offset of the i-th tile in Cartesian coordinates.

[0658] The tile_bounding_box_size_width[i] field indicates the width of the i-th tile in Cartesian coordinates.

[0659] The tile_bounding_box_size_height[i] field indicates the height of the i-th tile in Cartesian coordinates.

[0660] The tile_bounding_box_size_depth[i] field indicates the depth of the i-th tile in Cartesian coordinates.

[0661] The TPS according to an embodiment may further include neighbor point selection related option information.

[0662] According to an embodiment, neighbor point selection related option information may be included in an iteration statement repeated as many times as the value of the following num_tiles field.

[0663] In an embodiment, if the value of the different_nn_range_in_tile_flag field is true, the iteration statement may further include the nearest_neighbour_max_range[i] field, the nearest_neighbour_min_range[i] field, and the different_nn_range_in_slice_flag[i] field. In an embodiment, the different_nn_range_in_tile_flag field is signaled in the APS.

[0664] The nearest_neighbour_max_range[i] field may indicate the maximum neighboring point range of the i-th tile. Depending on the embodiment, the nearest_neighbour_max_range[i] field may be used to limit the distance of points registered as neighboring points in the i-th tile. Depending on the embodiment, the value of the nearest_neighbour_max_range[i] field may be used as the value of NN_range for the i-th tile.

[0665] The nearest_neighbour_min_range field[i] may indicate the minimum neighbor range of the i-th tile.

[0666] The different_nn_range_in_slice_flag[i] field may indicate whether the corresponding tile uses different maximum / minimum neighboring point ranges for slices divided from the tile.

[0667] If a value of the different_nn_range_in_slice_flag[i] field is true, the TPS may further include a nearest_neighbour_offset_range_in_slice_flag[i] field.

[0668] The nearest_neighbour_offset_range_in_slice_flag[i] field may indicate whether the maximum / minimum neighbor range defined in the slice is marked as a range offset from the maximum / minimum neighbor range defined in the tile or as an absolute value.

[0669] Figure 38 An embodiment of the syntax structure of a geometry slice bitstream() according to the present disclosure is shown.

[0670] A geometry slice bitstream (geometry_slice_bitstream()) according to an embodiment may include a geometry slice header (geometry_slice_header()) and geometry slice data (geometry_slice_data()).

[0671] Figure 39 An embodiment of a syntax structure of a geometry slice header (geometry_slice_header()) according to the present disclosure is shown.

[0672] According to an embodiment, a bitstream transmitted by a transmitting device (or a bitstream received by a receiving device) may include one or more slices. Each slice may include a geometry slice and an attribute slice. The geometry slice includes a geometry slice header (GSH). The attribute slice includes an attribute slice header (ASH).

[0673] The geometry slice header (geometry_slice_header()) according to an embodiment may include a gsh_geom_parameter_set_id field, a gsh_tile_id field, a gsh_slice_id field, a gsh_max_node_size_log2 field, a gsh_num_points field, and a byte_alignment() field.

[0674] When the value of the gps_box_present_flag field included in GPS is "true" (e.g., 1) and the value of the gps_gsh_box_log2_scale_present_flag field is "true" (e.g., 1), the geometry slice header (geometry_slice_header()) according to the embodiment may also include a gsh_box_log2_scale field, a gsh_box_origin_x field, a gsh_box_origin_y field, and a gsh_box_origin_z field.

[0675] The gsh_geom_parameter_set_id field specifies the value of the gps_geom_parameter_set_id of the active GPS.

[0676] The gsh_tile_id field specifies the value of the tile id referenced by GSH.

[0677] The gsh_slice_id field specifies the id of the slice referenced by other syntax elements.

[0678] The gsh_box_log2_scale field specifies the scaling factor used for the bounding box origin of the slice.

[0679] The gsh_box_origin_x field specifies the x value of the bounding box origin scaled by the value of the gsh_box_log2_scale field.

[0680] The gsh_box_origin_y field specifies the y value of the bounding box origin scaled by the value of the gsh_box_log2_scale field.

[0681] The gsh_box_origin_z field specifies the z value of the bounding box origin scaled by the value of the gsh_box_log2_scale field.

[0682] The gsh_max_node_size_log2 field specifies the size of the root geometry octree node.

[0683] The gsh_points_number field specifies the number of code points in the slice.

[0684] Figure 40 An embodiment of a syntax structure of geometry slice data (geometry_slice_data()) according to the present disclosure is shown. The geometry slice data (geometry_slice_data()) according to the embodiment may carry a geometry bitstream belonging to a corresponding slice.

[0685] According to an embodiment, geometry_slice_data() may include a first iteration statement that is repeated as many times as the value of MaxGeometryOctreeDepth. In an embodiment, each time the iteration statement is executed, the depth is initialized to 0 and incremented by 1, and the first iteration statement is repeated until the depth becomes equal to MaxGeometryOctreeDepth. The first iteration statement may include a second iteration statement that is repeated as many times as the value of NumNodesAtDepth. In an embodiment, nodeidx is initialized to 0 and incremented by 1 each time the iteration statement is executed. The second iteration statement is repeated until nodeidx becomes equal to NumNodesAtDepth. The second iteration statement may include xN=NodeX[depth][nodeIdx], yN=NodeY[depth][nodeIdx], zN=NodeZ[depth][nodeIdx] and geometry_node(depth, nodeIdx, xN, yN, zN). MaxGeometryOctreeDepth indicates the maximum depth of the geometry octree, and NumNodesAtDepth[depth] indicates the number of nodes to be decoded at the corresponding depth. The variables NodeX[depth][nodeIdx], NodeY[depth][nodeIdx], and NodeZ[depth][nodeIdx] indicate the x, y, z coordinates of the nodeIdx-th node in decoding order at a given depth. The geometry bitstream for the node at depth is sent via geometry_node(depth, nodeIdx, xN, yN, zN).

[0686] When the value of the log2_trisoup_node_size field is greater than 0, the geometry slice data (geometry_slice_data()) according to the embodiment may further include geometry_trisoup_data(). That is, when the size of the triangle node is greater than 0, the geometry bitstream subjected to trisoup geometry encoding is transmitted through geometry_trisoup_data().

[0687] Figure 41 An embodiment of the syntax structure of attribute_slice_bitstream() according to the present disclosure is shown.

[0688] An attribute slice bitstream (attribute_slice_bitstream()) according to an embodiment may include an attribute slice header (attribute_slice_header()) and attribute slice data (attribute_slice_data()).

[0689] Figure 42 An embodiment of the syntax structure of an attribute slice header (attribute_slice_header()) according to the present disclosure is shown. The attribute slice header according to the embodiment includes signaling information of the corresponding attribute slice. In particular, in this example, the attribute slice header includes neighbor point selection related option information.

[0690] The attribute slice header (attribute_slice_header()) according to an embodiment may include an ash_attr_parameter_set_id field, an ash_attr_sps_attr_idx field, and an ash_attr_geom_slice_id field.

[0691] When a value of the aps_slice_qp_delta_present_flag field of the APS is "true" (for example, 1), the attribute slice header (attribute_slice_header()) according to an embodiment may further include an ash_qp_delta_luma field and an ash_qp_delta_chroma field.

[0692] The ash_attr_parameter_set_id field indicates the aps_attr_parameter_set_id field of the currently active APS (e.g., Figure 35 or Figure 36 The value of the aps_attr_parameter_set_id field included in the APS described in.

[0693] The ash_attr_sps_attr_idx field identifies an attribute set in the current active SPS. The value of the ash_attr_sps_attr_idx field ranges from 0 to the sps_num_attribute_sets fields included in the current active SPS.

[0694] The ash_attr_geom_slice_id field indicates the value of the gsh_slice_id field in the current geometry slice header.

[0695] The ash_qp_delta_luma field specifies the luma delta quantization parameter (qp) derived from the initial slice qp in the active attribute parameter set.

[0696] The ash_qp_delta_chroma field indicates the chroma delta qp derived from the initial slice qp in the active attribute parameter set.

[0697] According to an embodiment, the attribute slice header (attribute_slice_header()) may further include the following neighbor selection related option information.

[0698] In an embodiment, if a value of the different_nn_range_in_slice_flag field is true and a value of the nearest_neighbour_offset_range_in_slice_flag field is false, the attribute slice header may further include a nearest_neighbour_absolute_max_range field and a nearest_neighbour_absolute_min_range field.

[0699] When the attributes of the corresponding slice are compressed, the nearest_neighbour_absolute_max_range field may indicate the maximum neighbor range. Depending on the embodiment, the nearest_neighbour_absolute_max_range field may be used to limit the distance of points registered as neighbor points in the slice. Depending on the embodiment, the value of the nearest_neighbour_absolute_max_range field may be used as the value of the NN_range of the slice.

[0700] The nearest_neighbour_absolute_min_range field may indicate the minimum neighbor range when attributes of a slice are compressed.

[0701] In an embodiment, the different_nn_range_in_slice_flag field and the nearest_neighbour_offset_range_in_slice_flag field are signaled in the TPS.

[0702] In an embodiment, if a value of the different_nn_range_in_slice_flag field is true and a value of the nearest_neighbour_offset_range_in_slice_flag field is true, the attribute slice header may further include a nearest_neighbour_max_range_offset field and a nearest_neighbour_min_range_offset field.

[0703] The nearest_neighbour_max_range_offset field may indicate the maximum neighbor range offset when the attributes of a slice are compressed. In an embodiment, the reference for the neighbor maximum range offset is the maximum neighbor range of the tile to which the slice belongs. Depending on the embodiment, the nearest_neighbour_max_range_offset field may be used to limit the distance of points registered as neighbors in a slice. Depending on the embodiment, the value of the nearest_neighbour_max_range_offset field may be used as an offset to the value of the NN_range of the slice.

[0704] The nearest_neighbour_min_range_offset field may indicate the minimum neighbor range offset when the attributes of the slice are compressed. In an embodiment, a reference for the minimum neighbor range offset is the minimum neighbor range of the tile to which the slice belongs.

[0705] Figure 43 Another embodiment of the syntax structure of an attribute slice header (attribute_slice_header()) according to the present disclosure is illustrated. The attribute slice header according to the embodiment includes signaling information of the corresponding attribute slice. In particular, an example of including neighbor point selection related option information is illustrated.

[0706] In addition to the options for neighbor selection, Figure 43 The syntax of the attribute slice header is the same as Figure 42 The syntax structure of the attribute slice header is the same or similar. Figure 43 For any omitted or undescribed parts of the description, refer to Figure 42 .

[0707] The ash_attr_parameter_set_id field indicates the aps_attr_parameter_set_id field of the currently active APS (e.g., refer to Figure 35 or Figure 36The value of the aps_attr_parameter_set_id field) included in the described APS.

[0708] The ash_attr_sps_attr_idx field identifies an attribute set in the current active SPS. The value of the ash_attr_sps_attr_idx field ranges from 0 to the sps_num_attribute_sets fields included in the current active SPS.

[0709] The ash_attr_geom_slice_id field indicates the value of the gsh_slice_id field in the current geometry slice header.

[0710] According to an embodiment, the attribute slice header (attribute_slice_header()) may further include the following neighbor selection related option information.

[0711] In an embodiment, if the value of the different_nn_range_in_slice_flag field is true, the attribute slice header may further include a different_nn_range_per_lod_flag field.

[0712] The different_nn_range_per_lod_flag field may indicate whether to use the maximum / minimum neighboring point range differently for each LOD.

[0713] For example, if a value of the different_nn_range_per_lod_flag field is false and a value of the nearest_neighbour_offset_range_in_slice_flag field is false, the attribute slice header may further include a nearest_neighbour_absolute_max_range field and a nearest_neighbour_absolute_min_range field.

[0714] When the attributes of the corresponding slice are compressed, the nearest_neighbour_absolute_max_range field may indicate the maximum neighbor range. Depending on the embodiment, the nearest_neighbour_absolute_max_range field may be used to limit the distance of points registered as neighbor points in the slice. Depending on the embodiment, the value of the nearest_neighbour_absolute_max_range field may be used as the value of the NN_range of the slice.

[0715] The nearest_neighbour_absolute_min_range field may indicate the minimum neighbor range when the attributes of the slice are compressed.

[0716] For example, if a value of the different_nn_range_per_lod_flag field is false and a value of the nearest_neighbour_offset_range_in_slice_flag field is true, the attribute slice header may further include a nearest_neighbour_max_range_offset field and a nearest_neighbour_min_range_offset field.

[0717] The nearest_neighbour_max_range_offset field may indicate the maximum neighbor range offset when the attributes of a slice are compressed. In an embodiment, the reference for the maximum neighbor range offset is the maximum neighbor range of the tile to which the slice belongs. Depending on the embodiment, the nearest_neighbour_absolute_max_range field may be used to limit the distance of points registered as neighbors in a slice. Depending on the embodiment, the value of the nearest_neighbour_absolute_max_range field may be used as an offset to the value of the NN_range of the slice.

[0718] The nearest_neighbour_min_range_offset field may indicate the minimum neighbor range offset when the attributes of the slice are compressed. In an embodiment, the reference of the minimum neighbor range offset is the minimum neighbor range of the tile to which the slice belongs.

[0719] For example, if the value of the different_nn_range_per_lod_flag field is true and the value of the nearest_neighbour_offset_range_in_slice_flag field is false, the attribute slice header also includes an iteration statement that repeats as many times as the value of the lifting_num_detail_levels_minus1 field. In an embodiment, the index (idx) is initialized to 0 and incremented by 1 each time the iteration statement is executed. The iteration statement is repeated until the index idx is greater than the value of the lifting_num_detail_levels_minus1 field. The iteration statement may include a nearest_neighbour_absolute_max_range[idx] field and a nearest_neighbour_absolute_min_range[idx] field.

[0720] In an embodiment, the lifting_num_detail_levels_minus1 field indicates the number of LODs used for attribute encoding and is signaled in the APS.

[0721] The nearest_neighbour_absolute_max_range[idx] field may indicate the maximum neighboring point range of LOD idx when the attributes of the slice are compressed. Depending on the embodiment, the nearest_neighbour_absolute_max_range[idx] field may be used to limit the distance of points registered as neighbors of LOD idx in the slice. Depending on the embodiment, the value of the nearest_neighbour_absolute_max_range[idx] field may be used as the value of NN_range of LOD idx of the corresponding slice.

[0722] The nearest_neighbour_absolute_min_range[idx] field may indicate the minimum neighbor range of LOD idx when attributes of a slice are compressed.

[0723] For example, if the value of the different_nn_range_per_lod_flag field is true and the value of the nearest_neighbour_offset_range_in_slice_flag field is true, the attribute slice header also includes an iteration statement that repeats as many times as the value of the lifting_num_detail_levels_minus1 field. In an embodiment, the index (idx) is initialized to 0 and incremented by 1 each time the iteration statement is executed. The iteration statement is repeated until the index idx is greater than the value of the lifting_num_detail_levels_minus1 field. The iteration statement may include a nearest_neighbour_max_range_offset[idx] field and a nearest_neighbour_min_range_offset[idx] field.

[0724] In an embodiment, the lifting_num_detail_levels_minus1 field indicates the number of LODs used for attribute encoding and is signaled in the APS.

[0725] The nearest_neighbour_max_range_offset[idx] field may indicate the maximum neighbor range offset of LOD idx when the attributes of the slice are compressed. According to an embodiment, the reference for the maximum neighbor range offset is the maximum neighbor range of the tile to which the slice belongs. According to an embodiment, the nearest_neighbour_max_range_offset[idx] field may be used to limit the distance of points registered as neighbors of LOD idx in the slice. According to an embodiment, the value of the nearest_neighbour_max_range_offset[idx] field may be used as an offset to the value of NN_range of LOD idx of the slice.

[0726] The nearest_neighbour_min_range_offset[idx] field may specify the minimum neighbor range offset of the LOD idx when the attributes of the slice are compressed. In an embodiment, the reference of the minimum neighbor range offset is the minimum neighbor range of the tile to which the slice belongs.

[0727] Figure 44 An embodiment of a syntax structure of attribute slice data (attribute_slice_data()) according to the present disclosure is shown. Attribute slice data (attribute_slice_data()) according to an embodiment may carry an attribute bitstream belonging to a corresponding slice.

[0728] exist Figure 44 In the attribute slice data (attribute_slice_data()), dimension=attribute_dimension[ash_attr_sps_attr_idx] represents the attribute dimension (attribute_dimension) of the attribute set identified by the ash_attr_sps_attr_idx field in the corresponding attribute slice header. Attribute_dimension refers to the number of components that make up the attribute. The attribute according to the embodiment represents reflectivity, color, etc. Therefore, the number of components is different for each attribute. For example, the attribute corresponding to color can have three color components (e.g., RGB). Therefore, the attribute corresponding to reflectivity can be a one-dimensional attribute, and the attribute corresponding to color can be a three-dimensional attribute.

[0729] Attributes according to an embodiment may be attribute-encoded dimension by dimension.

[0730] For example, the attribute corresponding to reflectivity and the attribute corresponding to color can be attribute-encoded separately. According to an embodiment, the attributes can be attribute-encoded together regardless of the dimension. For example, the attribute corresponding to reflectivity and the attribute corresponding to color can be attribute-encoded together.

[0731] exist Figure 44 In , zerorun specifies the number of zeros before the residual.

[0732] exist Figure 44 In

[0045] , i represents the i-th value of an attribute. According to an embodiment, the attr_coding_type field and the lifting_adaptive_prediction_threshold field are signaled in the APS.

[0733] Figure 44 MaxNumPredictors is a variable used in the point cloud data decoding process and can be acquired based on the value of the lifting_adaptive_prediction_threshold field signaled in the APS as follows.

[0734] MaxNumPredictors=lifting_max_num_direct_predictors field+1

[0735] Here, the lifting_max_num_direct_predictors field indicates the maximum number of predictors to be used for direct prediction.

[0736] According to an embodiment, predIndex[i] specifies a predictor index (or prediction mode) to decode the i-th point value of the attribute. The value of predIndex[i] ranges from 0 to the value of the lifting_max_num_direct_predictors field.

[0737] The variable MaxPredDiff[i] according to an embodiment may be calculated as follows.

[0738]

[0739] for(j=0;j <k;j++){

[0740]

[0741] }

[0742] MaxPredDiff[i]=maxValue-minValue;

[0743] Here, let k i is the set of k nearest neighboring points of the current point i, and let is their decoded / reconstructed attribute value. The number of nearest neighbor points k i Should be in the range of 1 to lifting_num_pred_nearest_neighbours.According to an embodiment, the decoding / reconstruction property values ​​of the neighbors are derived according to the prediction lifting decoding process.

[0744] The lifting_num_pred_nearest_neighbours field is signaled in the APS and indicates the maximum number of nearest neighbors to be used for prediction.

[0745] Figure 45 is a flowchart of a method of transmitting point cloud data according to an embodiment.

[0746] According to an embodiment, the point cloud data sending method may include: step 71001 of encoding the geometry contained in the point cloud data, step 71002 of encoding the attributes contained in the point cloud data based on the input and / or reconstructed geometry, and step 71003 of sending a bit stream including the encoded geometry, the encoded attributes and signaling information.

[0747] Steps 71001 and 71002 of encoding the geometry and attributes contained in the point cloud data may be performed Figure 1 Point cloud video encoder 10002, Figure 2 Encoding processing 20001, Figure 4Point cloud video encoder, Figure 12 Point cloud video encoder, Figure 14 Point cloud coding processing, Figure 15 Point cloud video encoder or Figure 17 Some or all of the operations of the geometry encoder and attribute encoder.

[0748] In an embodiment, the step 71002 of encoding the attributes may include generating the LOD by applying at least one of an octree-based LOD generation method, a distance-based LOD generation method, or a sampling-based LOD generation method. l Collection, based on LOD l The set is set to search for X (>0) NN points in a group with the same or lower LOD (ie, the distance between nodes is large) and these X NN points are registered in the predictor as a neighbor point set.

[0749] According to an embodiment, the encoding attribute step 71002 may configure the neighbor set by applying a search range and / or a maximum neighbor distance.

[0750] According to an embodiment, the step 71002 of encoding attributes may obtain the maximum neighbor distance by multiplying the basic neighbor distance by NN_range. NN_range is a range within which neighbors can be selected and is also referred to as the maximum neighbor range, neighbor range, or NN range.

[0751] Search range, basic neighbor distance and NN_range with reference Figures 15 to 27 Those described are the same, so detailed descriptions thereof are omitted here.

[0752] According to an embodiment, the step 71002 of encoding attributes comprises encoding the attributes based on the LOD l Search X (eg, 3) NN points among the points within the search range in a group with the same or lower LOD (ie, the distance between nodes is large), as Figure 26 Then, only the NN points within the maximum neighboring point distance among X (for example, 3) NN points can be registered as the neighboring point set. Figure 26 As an example, step 71002 of encoding attributes includes comparing the distance values ​​between points within the actual search range and point Px to search for X NN points, and registering only the points within the maximum neighboring point distance at the LOD to which point Px belongs among the X points as the neighboring point set of point Px. In other words, the neighboring points registered as the neighboring point set of point Px are limited to the points within the maximum neighboring point distance at the LOD to which point Px belongs among the X points.

[0753] According to another embodiment, the step 71002 of encoding the attributes may include: Figure 27 In the example shown in , among the points in the search range in the group having the same or lower LOD (i.e., the distance between nodes is large) based on the LOD1 set, X (e.g., 3) NN points within the maximum neighboring point distance are searched, and these X (e.g., 3) NN points are registered as the neighboring point set. Figure 27 As an example, step 71002 of encoding attributes includes comparing the distance values ​​between the points within the actual search range and the point Px to search for X (e.g., 3) NN points within the maximum neighboring point distance at the LOD to which the point Px belongs from among the points within the actual search range, and registering these X NN points as the neighboring point set of the point Px. That is, the neighboring points registered as the neighboring point set of the point Px are limited to the points within the maximum neighboring point distance at the LOD to which the point Px belongs.

[0754] According to an embodiment, step 71002 of encoding attributes includes obtaining a predicted attribute value of each point by applying one of prediction modes 0 to 3 when one or more neighboring points are registered in the predictor of each point, and obtaining a residual attribute value of the point based on the original attribute value and the predicted attribute value of each point.

[0755] According to an embodiment, prediction mode 0 is a mode for calculating a predicted attribute value by weighted averaging, prediction mode 1 is a mode for determining an attribute of a first neighboring point as a predicted attribute value, prediction mode 2 is a mode for determining an attribute of a second neighboring point as a predicted attribute value, and prediction mode 3 is a mode for determining an attribute of a third neighboring point as a predicted attribute value.

[0756] According to an embodiment, in step 71002 of encoding attributes, if the maximum difference between the attribute values ​​of neighboring points registered in the predictor of the corresponding point is less than a preset threshold, prediction mode 0 is configured as the prediction mode for the point. If the maximum difference is equal to or greater than the preset threshold, the RDO method is applied to multiple candidate prediction modes, and one of the candidate prediction modes is configured as the prediction mode for the point. In an embodiment, this process is performed for each point.

[0757] According to an embodiment, the prediction mode applied to each point may be transmitted in the attribute slice data.

[0758] According to an embodiment, step 71002 may apply quantization and zero-run-length encoding to the residual property value.

[0759] In steps 71001 and 71002 according to an embodiment, encoding may be performed based on a slice or a tile including one or more slices.

[0760] Step 71003 can be performed by Figure 1 Transmitter 10003, Figure 2 Sending process 20002, Figure 12 The sending processor 12012 or Figure 15 The sending processor 51008 executes.

[0761] Figure 46 is a flowchart of a method of receiving point cloud data according to an embodiment.

[0762] According to an embodiment, the point cloud data receiving method may include step 81001 of receiving encoded geometry, encoded attributes and signaling information, step 81002 of decoding the geometry based on the signaling information, step 81003 of decoding the attributes based on the signaling information and the decoded / reconstructed geometry, and step 81004 of rendering the point cloud data restored based on the decoded geometry and the decoded attributes.

[0763] According to an embodiment, step 81001 may be performed by Figure 1 Receiver 10005, Figure 2 Sending process 20002 or decoding process 20003, Figure 13 The receiver 13000 or the receiving processor 13001 or Figure 20 The receiving processor 61001 executes.

[0764] In steps 81002 and 81003 according to an embodiment, decoding may be performed based on a slice or a tile including one or more slices.

[0765] According to the embodiment, step 81002 may be performed Figure 1 Point cloud video decoder 10006, Figure 2 Decoding process 20003, Figure 11 Point cloud video decoder, Figure 13 Point cloud video decoder, Figure 20 The geometry decoder or Figure 21 Some or all operations of the geometry decoder.

[0766] According to the embodiment, step 81003 may be performed Figure 1 Point cloud video decoder 10006, Figure 2 Decoding process 20003, Figure 11 Point cloud video decoder, Figure 13 Point cloud video decoder, Figure 28 Attribute decoder or Figure 29 Some or all operations of the attribute decoder.

[0767] According to an embodiment, signaling information (e.g., at least one of an SPS, an APS, a TPS, or an attribute slice header) may include neighbor selection-related option information. According to an embodiment, the neighbor selection-related option information may include information about NN_range (e.g., nearest_neighbor_max_range field). According to an embodiment, the neighbor selection-related option information may also include at least one of information about the maximum number of points that can be set as neighboring points (e.g., lifting_num_pred_nearest_neighbours field), information about a search range (e.g., lifting_search_range field), information about an LOD configuration method, or information about a basic neighbor distance.

[0768] In an embodiment, the step 81003 of decoding the attribute may include generating the LOD by applying at least one of an octree-based LOD generation method, a distance-based LOD generation method, or a sampling-based LOD generation method. l Collection, based on LOD l Search for X (>0) NN points in a group with the same or lower LOD (i.e., the distance between nodes is large) and register these X NN points as a neighbor point set in the predictor. In an embodiment, the LOD generation method is signaled in signaling information (e.g., APS).

[0769] According to an embodiment, the decoding attribute step 81003 may configure the neighbor set by applying a search range and / or a maximum neighbor distance.

[0770] According to an embodiment, the decoding attribute step 81003 may obtain the maximum neighbor distance by multiplying the basic neighbor distance by NN_range. NN_range is a range in which neighbors can be selected and is referred to as the maximum neighbor range, neighbor range, or NN range.

[0771] Search range, basic neighbor distance and NN_range with reference Figures 15 to 29 Those described are the same, so detailed descriptions thereof are omitted here.

[0772] According to an embodiment, the step 81003 of decoding the attributes comprises performing a l Search X (eg, 3) NN points among the points within the search range in a group with the same or lower LOD (ie, the distance between nodes is large), as Figure 26 Then, only the NN points within the maximum neighboring point distance among X (for example, 3) NN points can be registered as the neighboring point set. Figure 26As an example, step 81003 of decoding the attributes includes comparing the distance values ​​between the points within the actual search range and point Px to search for X NN points, and registering only the points within the maximum neighboring point distance at the LOD to which point Px belongs among the X points as the neighboring point set of point Px. In other words, the neighboring points registered as the neighboring point set of point Px are limited to the points within the maximum neighboring point distance at the LOD to which point Px belongs among the X points.

[0773] According to another embodiment, the step 81003 of decoding the attribute may include: Figure 27 In the example shown in , among the points in the search range in the group having the same or lower LOD (i.e., the distance between nodes is large) based on the LOD1 set, X (e.g., 3) NN points within the maximum neighboring point distance are searched, and these X (e.g., 3) NN points are registered as the neighboring point set. Figure 27 As an example, step 81003 of decoding the attributes includes comparing the distance values ​​between the points within the actual search range and the point Px to search for X (e.g., 3) NN points within the maximum neighboring point distance at the LOD to which the point Px belongs from the points within the actual search range, and registering these X NN points as the neighboring point set of the point Px. That is, the neighboring points registered as the neighboring point set of the point Px are limited to the points within the maximum neighboring point distance at the LOD to which the point Px belongs.

[0774] According to an embodiment, step 81003 of decoding the attribute may include obtaining a predicted attribute value of the corresponding point by applying one of prediction modes 0 to 3 when one or more neighboring points are registered in a predictor of a specific point.

[0775] According to an embodiment, prediction mode 0 is a mode for calculating a predicted attribute value by weighted averaging, prediction mode 1 is a mode for determining an attribute of a first neighboring point as a predicted attribute value, prediction mode 2 is a mode for determining an attribute of a second neighboring point as a predicted attribute value, and prediction mode 3 is a mode for determining an attribute of a third neighboring point as a predicted attribute value.

[0776] According to an embodiment, the prediction mode of a specific point may be configured as a default, and predictor index information (predIndex) indicating the prediction mode may be signaled in attribute slice data.

[0777] According to an embodiment, when the predictor index information of the point is not signaled in the attribute slice data, step 81003 may predict the attribute value of the to-be-decoded point based on prediction mode 0.

[0778] According to an embodiment, when the predictor index information of the point is signaled in the attribute slice data, step 81003 may predict the attribute value of the to-be-decoded point based on the prediction mode signaled in the attribute slice data.

[0779] According to an embodiment, step 81003 may restore the attribute value of the point by adding the predicted attribute value of the point to the received residual attribute value of the point. In an embodiment, the attribute value of each point may be restored by performing this process on each point.

[0780] According to an embodiment, step 81003 may perform zero-run-length decoding on the received residual property value before restoring the property value when the received residual property value is zero-run-length encoded, which is an inverse process of the zero-run-length encoding at the sending side.

[0781] In step 81004 of rendering point cloud data according to an embodiment, the point cloud data may be rendered using various rendering methods. For example, the points of the point cloud content may be rendered on vertices having a certain thickness, a cube of a certain minimum size centered at the vertex position, or a circle centered at the vertex position. All or part of the rendered point cloud content is provided to the user via a display (e.g., a VR / AR display, a general display, etc.).

[0782] According to an embodiment, step 81004 may be performed by Figure 1 Renderer 10007, Figure 2 Rendering process 20004 or Figure 13 Renderer 13011 executes.

[0783] As described above, according to the present disclosure, during attribute encoding of point cloud content, when configuring a neighboring point set by considering the attribute correlation between points of the content, the selection of meaningless neighboring points is eliminated, and meaningful neighboring points can be selected. Consequently, since the residual attribute value is reduced and the bitstream size is thereby reduced, the compression efficiency of the attribute is improved. In other words, in the present disclosure, a method of limiting the points that can be selected as the neighboring point set by considering the attribute correlation between points of the content is used to improve the compression efficiency of the attribute.

[0784] Each of the above parts, modules or units can be software, processors or hardware parts that execute the continuous process stored in the memory (or storage unit). Each step described in the above embodiments can be performed by a processor, software or hardware part. Each module / block / unit described in the above embodiments can be operated as a processor, software or hardware. In addition, the method proposed in the embodiment can be executed as code. The code can be written on a processor-readable storage medium and, therefore, read by a processor provided by the device.

[0785] In this specification, when a part "includes" or "comprising" an element, it means that the part also includes or comprises another element unless otherwise mentioned. In addition, the term "...module (or unit)" disclosed in the specification means a unit for processing at least one function or operation, and can be implemented by hardware, software, or a combination of hardware and software.

[0786] Although the embodiments are described with reference to each of the drawings for the sake of simplicity, new embodiments can be designed by combining the embodiments illustrated in the drawings. If a person skilled in the art designs a computer-readable recording medium having a program for executing the embodiments mentioned in the above description recorded thereon, the recording medium may fall within the scope of the appended claims and their equivalents.

[0787] The apparatus and method may not be limited to the configuration and method of the above-described embodiments. The above-described embodiments may be configured by selectively combining all or part of each other to enable various modifications.

[0788] Although the preferred embodiment has been described with reference to the accompanying drawings, it will be appreciated by those skilled in the art that various modifications and variations may be made in the embodiments without departing from the spirit or scope of the present disclosure as described in the appended claims. Such modifications should not be understood independently of the technical ideas or viewpoints of the embodiments.

[0789] The various elements of the device of the embodiment can be implemented by hardware, software, firmware or a combination thereof. The various elements in the embodiment can be implemented by a single chip (e.g., a single hardware circuit). According to the embodiment, the components according to the embodiment can be implemented as separate chips respectively. According to the embodiment, at least one or more components of the device according to the embodiment can include one or more processors capable of executing one or more programs. The one or more programs can execute any one or more of the operations / methods according to the embodiment, or include instructions for executing them. The executable instructions for executing the method / operation of the device according to the embodiment can be stored in a non-transient CRM or other computer program product configured to be executed by one or more processors, or can be stored in a transient CRM or other computer program product configured to be executed by one or more processors. In addition, the memory according to the embodiment can be used as a concept covering not only volatile memory (e.g., RAM) but also non-volatile memory, flash memory and PROM. In addition, it can also be implemented in the form of a carrier wave such as sent over the Internet. In addition, the processor-readable recording medium can be distributed to computer systems connected by a network so that the processor-readable code can be stored and executed in a distributed manner.

[0790] In this document, the terms " / " and "," should be interpreted as indicating "and / or". For example, the expression "A / B" may mean "A and / or B". In addition, "A, B" may mean "A and / or B". In addition, "A / B / C" may mean "at least one of A, B, and / or C". "A, B, C" may mean "at least one of A, B, and / or C".

[0791] In addition, in this document, the term "or" should be interpreted as "and / or." For example, the expression "A or B" may mean 1) only A, 2) only B, and / or 3) both A and B. In other words, the term "or" in this document should be interpreted as "additionally or alternatively."

[0792] The various elements of the embodiments may be implemented by hardware, software, firmware, or a combination thereof. The various elements of the embodiments may be implemented by a single chip such as a single hardware circuit. Depending on the embodiment, the elements may be selectively executed by separate chips. Depending on the embodiment, at least one of the elements of the embodiments may be executed in one or more processors including instructions for executing the operations according to the embodiments.

[0793] Terms such as first and second may be used to describe various elements of an embodiment. However, the various components according to the embodiment should not be limited by the above terms. These terms are simply used to distinguish one element from another. For example, a first user input signal may be referred to as a second user input signal. Similarly, a second user input signal may be referred to as a first user input signal. The use of these terms should be interpreted as not departing from the scope of the various embodiments. Both the first user input signal and the second user input signal are user input signals, but are not intended to refer to the same user input signal unless the context clearly indicates otherwise.

[0794] The terms used to describe the embodiments are used only 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 forms "a", "an", and "the" include plural referents unless the context clearly dictates otherwise. The expression "and / or" is used to include all possible combinations of terms. Terms such as "including" or "having" are intended to indicate the presence of figures, numbers, steps, elements, and / or parts, but should be understood as not excluding the possibility of additional presence of figures, numbers, steps, elements, and / or parts. As used herein, conditional expressions such as "if" and "when" are not limited to optional cases, but are intended to be interpreted as performing relevant operations or interpreting rel...

Claims

1. A method for transmitting point cloud data, comprising the following steps: Get point cloud data; encoding geometric information including positions of points of the point cloud data; generating a level of detail (LOD) based on the geometric information, and selecting one or more neighboring points of each point to be attribute-encoded based on the LOD, wherein a higher LOD in the LOD includes points that belong to a lower LOD in the LOD and points that do not belong to the lower LOD, wherein the one or more neighboring points of each selected point are within a maximum neighbor distance, and wherein the maximum neighbor distance is obtained by multiplying a base distance by a maximum neighbor range, wherein the base distance is a diagonal distance of a node at each LOD, and wherein the maximum neighbor range is the number of neighbor nodes around each point; encoding attribute information of each point based on the one or more selected neighboring points of each point; and The encoded geometric information, the encoded attribute information and the signaling information are sent.

2. A method for receiving point cloud data, the method comprising the following steps: Receive geometric information, attribute information and signaling information; decoding the geometric information based on the signaling information; generating a level of detail (LOD) based on the geometric information; selecting one or more neighboring points for each point to be attribute-decoded based on the LODs, wherein a higher LOD in the LODs includes points that belong to a lower LOD in the LODs and points that do not belong to the lower LODs, wherein the one or more neighboring points of each selected point are within a maximum neighbor distance, and wherein the maximum neighbor distance is obtained by multiplying a base distance by a maximum neighbor range, wherein the base distance is a diagonal distance of a node at each LOD, and wherein the maximum neighbor range is the number of neighbor nodes around each point; decoding the attribute information of each point based on the one or more selected neighboring points of each point and the signaling information; and Rendering includes the decoded point cloud data of the geometric information and the decoded attribute information.

Images