WO2021045603A1 - Dispositif de transmission de données de nuage de points, procédé de transmission de données de nuage de points, dispositif de réception de données de nuage de points et procédé de réception de données de nuage de points - Google Patents

Dispositif de transmission de données de nuage de points, procédé de transmission de données de nuage de points, dispositif de réception de données de nuage de points et procédé de réception de données de nuage de points Download PDF

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WO2021045603A1
WO2021045603A1 PCT/KR2020/012072 KR2020012072W WO2021045603A1 WO 2021045603 A1 WO2021045603 A1 WO 2021045603A1 KR 2020012072 W KR2020012072 W KR 2020012072W WO 2021045603 A1 WO2021045603 A1 WO 2021045603A1
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attribute
point
point cloud
prediction mode
information
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PCT/KR2020/012072
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English (en)
Korean (ko)
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허혜정
오세진
박유선
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엘지전자 주식회사
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T9/00—Image coding
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
    • H04N13/00—Stereoscopic video systems; Multi-view video systems; Details thereof
    • H04N13/10—Processing, recording or transmission of stereoscopic or multi-view image signals
    • H04N13/106—Processing image signals
    • H04N13/161—Encoding, multiplexing or demultiplexing different image signal components
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
    • H04N19/00—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals
    • H04N19/10—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using adaptive coding
    • H04N19/134—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using adaptive coding characterised by the element, parameter or criterion affecting or controlling the adaptive coding
    • H04N19/157—Assigned coding mode, i.e. the coding mode being predefined or preselected to be further used for selection of another element or parameter
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
    • H04N19/00—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals
    • H04N19/10—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using adaptive coding
    • H04N19/169—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using adaptive coding characterised by the coding unit, i.e. the structural portion or semantic portion of the video signal being the object or the subject of the adaptive coding
    • H04N19/184—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using adaptive coding characterised by the coding unit, i.e. the structural portion or semantic portion of the video signal being the object or the subject of the adaptive coding the unit being bits, e.g. of the compressed video stream
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
    • H04N19/00—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals
    • H04N19/90—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using coding techniques not provided for in groups H04N19/10-H04N19/85, e.g. fractals
    • H04N19/93—Run-length coding

Definitions

  • Embodiments relate to a method and apparatus for processing point cloud content.
  • the point cloud content is content expressed as a point cloud, which is a set of points (points) belonging to a coordinate system representing a three-dimensional space.
  • Point cloud content can express media consisting of three dimensions, VR (Virtual Reality, Virtual Reality), AR (Augmented Reality, Augmented Reality), MR (Mixed Reality, Mixed Reality), XR (Extended Reality), and autonomous driving. It is used to provide various services such as services. However, tens of thousands to hundreds of thousands of point data are required to represent point cloud content. Therefore, a method for efficiently processing a vast amount of point data is required.
  • the technical problem according to the embodiments is to provide a point cloud data transmission apparatus, a transmission method, a point cloud data reception apparatus, and a reception method for efficiently transmitting and receiving a point cloud in order to solve the above-described problems.
  • a technical problem according to embodiments is to provide a point cloud data transmission apparatus, a transmission method, a point cloud data reception apparatus, and a reception method for solving latency and encoding/decoding complexity.
  • the technical problem according to embodiments is to improve the encoding technology of the attribute information of geometry-based point cloud compression (G-PCC) to improve the compression performance of the point cloud data transmission. It is to provide an apparatus, a transmission method, a point cloud data receiving apparatus, and a receiving method.
  • G-PCC geometry-based point cloud compression
  • a technical problem according to embodiments is to provide a point cloud data transmission device, a transmission method, a point cloud data reception device, and a reception method for increasing compression efficiency while supporting parallel processing of attribute information of G-PCC.
  • a technical problem according to embodiments is a point cloud data transmission apparatus for increasing compression efficiency of attribute information by changing a prediction mode and a bitstream configuration of a quantized residual attribute value in a process of encoding and decoding attribute information of G-PCC , A transmission method, a point cloud data receiving apparatus and a receiving method.
  • the point cloud data transmission method includes the steps of acquiring point cloud data, encoding geometry information of the point cloud data, and the point cloud based on the geometry information. Encoding attribute information of data, and transmitting a bitstream including the encoded geometry information, the encoded attribute information, and signaling information.
  • the encoding of the attribute information includes selecting a prediction mode of each point to perform attribute encoding, and a predicted attribute value of each point and an original attribute value of each point predicted based on the selected prediction mode of each point.
  • the method includes obtaining a residual attribute value of each point by a difference between and.
  • the encoding of the attribute information includes separating a prediction mode and a residual attribute value from each point, and a prediction mode bitstream including a prediction mode separated from each point and a residual attribute value separated from each point.
  • the method further includes configuring a residual attribute bitstream to be included.
  • the encoding of the attribute information may further include applying and encoding zero run-length coding to a prediction mode bitstream including a prediction mode separated from each point.
  • the signaling information includes information for identifying whether a prediction mode and a residual attribute value are separated, and information for identifying whether zero run-length coding is applied to the prediction mode bitstream.
  • the prediction mode is one of first to fourth prediction modes, and the first prediction mode is a mode in which an average value of a value obtained by multiplying the attributes of the registered neighboring points of the corresponding point by a weight is determined as the predicted attribute value of the corresponding point.
  • the second prediction mode is a mode in which the attribute of the first nearest neighbor point among the registered neighbor points is determined as the predicted attribute value of the corresponding point based on a distance
  • the third prediction mode is the registered neighbor Among the points, the attribute of the second nearest neighboring point is determined as the predicted attribute value of the corresponding point based on the distance to the corresponding point
  • the fourth prediction mode is the third among the registered neighboring points based on the distance and the corresponding point.
  • an attribute of a nearby neighboring point is determined as a predicted attribute value of the corresponding point.
  • the point cloud data transmission apparatus includes an acquisition unit that obtains point cloud data, a geometry encoder that encodes geometry information of the point cloud data, and an attribute that encodes attribute information of the point cloud data based on the geometry information. It may include an encoder and a transmission unit for transmitting a bitstream including the encoded geometry information, the encoded attribute information, and signaling information.
  • the attribute encoder selects a prediction mode of each point to perform attribute encoding, and the difference between the predicted attribute value of each point predicted based on the selected prediction mode of each point and the original attribute value of each point. According to an embodiment, the residual attribute value of a point is calculated.
  • the attribute encoder separates a prediction mode and a residual attribute value from each point, a prediction mode bitstream including a prediction mode separated from each point, and a residual attribute bitstream including a residual attribute value separated from each point And encoding by applying zero run-length coding to a prediction mode bitstream including a prediction mode separated from each point.
  • the signaling information includes information for identifying whether a prediction mode and a residual attribute value are separated, and information for identifying whether zero run-length coding is applied to the prediction mode bitstream.
  • the method for receiving point cloud data includes receiving a bitstream including geometry information, attribute information, and signaling information, decoding the geometry information based on the signaling information, the signaling information and the geometry
  • the method includes decoding the attribute information based on the information, and rendering the reconstructed point cloud data based on the decoded geometry information and the decoded attribute information.
  • the signaling information includes information for identifying whether a prediction mode and a residual attribute value are separated, and information for identifying whether zero run-length coding is applied to a prediction mode bitstream including the prediction mode. Let's take an example.
  • the decoding of the attribute information if it is determined that the prediction mode and the residual attribute value are separated based on the signaling information, and when it is determined that zero run-length coding is applied to the prediction mode bitstream, zero run-length coding is applied to the prediction mode bitstream. According to an embodiment, it further includes performing run-length decoding.
  • the attribute value of each point is predicted based on the prediction mode of each received and decoded point, and the residual attribute value of each received and decoded point is added to the predicted attribute value of each point. According to an embodiment, it includes the step of restoring the attribute value of the point.
  • the prediction mode is one of first to fourth prediction modes, and the first prediction mode is a mode in which an average value of a value obtained by multiplying the attributes of the registered neighboring points of the corresponding point by a weight is determined as the predicted attribute value of the corresponding point.
  • the second prediction mode is a mode in which the attribute of the first nearest neighbor point among the registered neighbor points is determined as the predicted attribute value of the corresponding point based on a distance
  • the third prediction mode is the registered neighbor Among the points, the attribute of the second nearest neighboring point is determined as the predicted attribute value of the corresponding point based on the distance to the corresponding point
  • the fourth prediction mode is the third among the registered neighboring points based on the distance and the corresponding point.
  • an attribute of a nearby neighboring point is determined as a predicted attribute value of the corresponding point.
  • the point cloud data receiving apparatus includes a receiving unit that receives a bitstream including geometry information, attribute information, and signaling information, a geometry decoder that decodes the geometry information based on the signaling information, the signaling information and the An attribute decoder that decodes the attribute information based on geometry information, and a renderer that renders the reconstructed point cloud data based on the decoded geometry information and the decoded attribute information.
  • the signaling information includes information for identifying whether a prediction mode and a residual attribute value are separated, and information for identifying whether zero run-length coding is applied to a prediction mode bitstream including the prediction mode. Let's take an example.
  • the attribute decoder determines that the prediction mode and the residual attribute value are separated based on the signaling information, and when it is determined that zero run-length coding is applied to the prediction mode bitstream, zero run-length decoding is performed on the prediction mode bitstream. It is assumed to perform as an embodiment.
  • the attribute decoder predicts the attribute value of each point based on the prediction mode of each received and decoded point, adds the residual attribute value of each received and decoded point to the predicted attribute value of each point, and adds the attribute value of each point. It is assumed that the restoration is performed in an embodiment.
  • the prediction mode is one of first to fourth prediction modes, and the first prediction mode is a mode in which an average value of a value obtained by multiplying the attributes of the registered neighboring points of the corresponding point by a weight is determined as the predicted attribute value of the corresponding point.
  • the second prediction mode is a mode in which the attribute of the first nearest neighbor point among the registered neighbor points is determined as the predicted attribute value of the corresponding point based on a distance
  • the third prediction mode is the registered neighbor Among the points, the attribute of the second nearest neighboring point is determined as the predicted attribute value of the corresponding point based on the distance to the corresponding point
  • the fourth prediction mode is the third among the registered neighboring points based on the distance and the corresponding point.
  • an attribute of a nearby neighboring point is determined as a predicted attribute value of the corresponding point.
  • the point cloud data transmission method, the transmission device, the point cloud data reception method, and the reception device may provide a point cloud service with high quality.
  • the point cloud data transmission method, the transmission device, the point cloud data reception method, and the reception device may achieve various video codec schemes.
  • the point cloud data transmission method, the transmission device, the point cloud data reception method, and the reception device may provide general-purpose point cloud content such as an autonomous driving service.
  • the point cloud data transmission method, the transmission device, the point cloud data reception method, and the reception device perform spatial adaptive division of point cloud data for independent encoding and decoding of the point cloud data, thereby improving parallel processing and It can provide scalability.
  • the point cloud data transmission method, the transmission device, the point cloud data reception method, and the reception device perform encoding and decoding by spatially dividing the point cloud data into tiles and/or slice units, and signaling necessary data for this. It is possible to improve the encoding and decoding performance of the point cloud.
  • the point cloud data transmission method, the transmission device, the point cloud data reception method, and the reception device may increase attribute decoding efficiency by changing a prediction mode and a bitstream configuration of a residual attribute value when encoding an attribute.
  • the point cloud data transmission method, the transmission device, the point cloud data reception method, and the reception device separate a prediction mode and a residual attribute value from all points when an attribute is encoded, and the prediction mode is divided into the separated prediction modes.
  • attribute decoding efficiency can be improved.
  • parallel processing is possible and the encoding/decoding speed is increased.
  • the point cloud data transmission method, the transmission device, the point cloud data reception method, and the reception device separate the prediction mode and the residual attribute value from all points when the attribute is encoded, and prediction consisting of the separated prediction modes.
  • the size of the attribute bitstream can be reduced.
  • the peak signal-to-noise ratio (PSNR) does not change, thereby increasing the attribute compression efficiency.
  • FIG. 1 shows a system for providing point cloud content according to embodiments.
  • FIG. 2 shows a process for providing Point Cloud content according to embodiments.
  • FIG 3 shows the arrangement of Point Cloud capture equipment according to embodiments.
  • FIG. 4 shows a point cloud video encoder according to embodiments.
  • 5 shows voxels in a 3D space according to embodiments.
  • FIG. 6 shows an example of an octree and an occupancy code according to embodiments.
  • FIG. 7 illustrates an example of a neighbor node pattern according to embodiments.
  • FIG. 8 shows an example of a point configuration of Point Cloud content for each LOD according to embodiments.
  • FIG 9 shows an example of a point configuration of Point Cloud content for each LOD according to embodiments.
  • FIG. 10 shows an example of a block diagram of a point cloud video decoder according to embodiments.
  • FIG. 11 shows an example of a point cloud video decoder according to embodiments.
  • FIG. 12 shows components for encoding Point Cloud video of a transmitter according to embodiments.
  • FIG. 13 shows components for decoding Point Cloud video of a receiver according to embodiments.
  • FIG. 14 shows an example of a structure that can be interlocked with a point cloud data method/device according to embodiments.
  • 15 is a diagram illustrating another example of an apparatus for transmitting a point cloud according to embodiments.
  • 16(a) to 16(c) show an embodiment of dividing the bounding box into one or more tiles.
  • 17 is a detailed block diagram showing another example of a geometry encoder and an attribute encoder according to embodiments.
  • 18(a) is a diagram illustrating an example in which a prediction mode and a residual attribute value are grouped into pairs for each point to configure a bitstream according to embodiments.
  • 18(b) shows an example of separating a prediction mode and a residual attribute value for all points according to embodiments, configuring a prediction mode stream with prediction modes, and configuring a residual attribute value stream with residual attribute values. It is a drawing.
  • 19 is a detailed block diagram showing an example of a zero-run length encoding process according to embodiments.
  • FIG. 20 is a diagram illustrating another example of an apparatus for receiving a point cloud according to embodiments.
  • 21 is a detailed block diagram showing another example of a geometry decoder and an attribute decoder according to embodiments.
  • FIG. 22 is a diagram illustrating an example of a bitstream structure of point cloud data for transmission/reception according to embodiments.
  • FIG. 23 is a diagram illustrating an example of a bitstream structure of point cloud data according to embodiments.
  • 24 is a diagram illustrating a connection relationship between components in a bitstream of point cloud data according to embodiments.
  • 25 is a diagram showing an embodiment of a syntax structure of a sequence parameter set according to the present specification.
  • 26 is a diagram showing an embodiment of a syntax structure of a geometry parameter set according to the present specification.
  • 27 is a diagram illustrating an embodiment of a syntax structure of an attribute parameter set according to the present specification.
  • FIG. 28 is a diagram illustrating an embodiment of a syntax structure of a tile parameter set according to the present specification.
  • 29 is a diagram showing an embodiment of a syntax structure of a geometry slice bitstream () according to the present specification.
  • FIG. 30 is a diagram illustrating an embodiment of a syntax structure of a geometry slice header according to the present specification.
  • FIG. 31 is a diagram illustrating an embodiment of a syntax structure of geometry slice data according to the present specification.
  • 32 is a diagram showing an embodiment of a syntax structure of an attribute slice bitstream () according to the present specification.
  • 33 is a diagram illustrating an embodiment of a syntax structure of an attribute slice header according to the present specification.
  • 34 is a diagram illustrating an embodiment of a syntax structure of attribute slice data according to the present specification.
  • 35 is a flowchart of a method for transmitting point cloud data according to embodiments.
  • 36 is a flowchart of a method for receiving point cloud data according to embodiments.
  • FIG. 1 shows an example of a point cloud content providing system according to embodiments.
  • the point cloud content providing system illustrated in FIG. 1 may include a transmission device 10000 and a reception device 10004.
  • the transmission device 10000 and the reception device 10004 may perform wired or wireless communication in order to transmit and receive point cloud data.
  • the transmission device 10000 may secure, process, and transmit a point cloud video (or point cloud content).
  • the transmission device 10000 is a fixed station, a base transceiver system (BTS), a network, an artificial intelligence (AI) device and/or system, a robot, an AR/VR/XR device and/or server. And the like.
  • the transmission device 10000 uses a wireless access technology (eg, 5G NR (New RAT), LTE (Long Term Evolution)) to communicate with a base station and/or other wireless devices, Robots, vehicles, AR/VR/XR devices, portable devices, home appliances, Internet of Thing (IoT) devices, AI devices/servers, and the like may be included.
  • 5G NR New RAT
  • LTE Long Term Evolution
  • the transmission device 10000 includes a Point Cloud Video Acquisition unit (10001), a Point Cloud Video Encoder (10002) and/or a transmitter (Transmitter (or Communication module)), 10003)
  • the point cloud video acquisition unit 10001 acquires a point cloud video through a process such as capture, synthesis, or generation.
  • a point cloud video is a point cloud content expressed as a point cloud, which is a set of points located in a three-dimensional space, and may be referred to as point cloud video data or the like.
  • a point cloud video according to embodiments may include one or more frames. One frame represents a still image/picture. Accordingly, the point cloud video may include a point cloud image/frame/picture, and may be referred to as any one of a point cloud image, a frame, and a picture.
  • the point cloud video encoder 10002 encodes the secured point cloud video data.
  • the point cloud video encoder 10002 may encode point cloud video data based on point cloud compression coding.
  • Point cloud compression coding may include Geometry-based Point Cloud Compression (G-PCC) coding and/or Video based Point Cloud Compression (V-PCC) coding or next-generation coding.
  • G-PCC Geometry-based Point Cloud Compression
  • V-PCC Video based Point Cloud Compression
  • the point cloud video encoder 10002 may output a bitstream including encoded point cloud video data.
  • the bitstream may include not only the encoded point cloud video data, but also signaling information related to encoding of the point cloud video data.
  • the transmitter 10003 transmits a bitstream including encoded point cloud video data.
  • the bitstream according to the embodiments is encapsulated into a file or segment (for example, a streaming segment) and transmitted through various networks such as a broadcasting network and/or a broadband network.
  • the transmission device 10000 may include an encapsulation unit (or an encapsulation module) that performs an encapsulation operation.
  • the encapsulation unit may be included in the transmitter 10003.
  • a file or segment may be transmitted to the receiving device 10004 through a network or stored in a digital storage medium (eg, USB, SD, CD, DVD, Blu-ray, HDD, SSD, etc.).
  • the transmitter 10003 may perform wired/wireless communication with the receiving device 10004 (or a receiver 10005) through a network such as 4G, 5G, or 6G.
  • the transmitter 10003 may perform necessary data processing operations according to a network system (for example, a communication network system such as 4G, 5G, or 6G).
  • the transmission device 10000 may transmit encapsulated data according to an on demand method.
  • the receiving device 10004 includes a receiver 10005, a point cloud video decoder 10006, and/or a renderer 10007.
  • the receiving device 10004 uses a wireless access technology (e.g., 5G NR (New RAT), LTE (Long Term Evolution)) to communicate with a base station and/or other wireless devices, a robot , Vehicles, AR/VR/XR devices, portable devices, home appliances, Internet of Thing (IoT) devices, AI devices/servers, and the like.
  • 5G NR New RAT
  • LTE Long Term Evolution
  • the receiver 10005 receives a bitstream including point cloud video data or a file/segment in which the bitstream is encapsulated from a network or a storage medium.
  • the receiver 10005 may perform a necessary data processing operation according to a network system (for example, a communication network system such as 4G, 5G, or 6G).
  • the receiver 10005 may output a bitstream by decapsulating the received file/segment.
  • the receiver 10005 may include a decapsulation unit (or a decapsulation module) for performing a decapsulation operation.
  • the decapsulation unit may be implemented as an element (or component or module) separate from the receiver 10005.
  • the point cloud video decoder 10006 decodes a bitstream including point cloud video data.
  • the point cloud video decoder 10006 may decode the point cloud video data according to the encoding method (for example, a reverse process of the operation of the point cloud video encoder 10002). Accordingly, the point cloud video decoder 10006 may decode the point cloud video data by performing point cloud decompression coding, which is a reverse process of the point cloud compression.
  • Point cloud decompression coding includes G-PCC coding.
  • the renderer 10007 renders the decoded point cloud video data.
  • the renderer 10007 may output point cloud content by rendering audio data as well as point cloud video data.
  • the renderer 10007 may include a display for displaying point cloud content.
  • the display is not included in the renderer 10007 and may be implemented as a separate device or component.
  • the feedback information is information for reflecting an interaction ratio with a user who consumes point cloud content, and includes user information (eg, head orientation information, viewport information, etc.).
  • user information eg, head orientation information, viewport information, etc.
  • the feedback information is the content sending side (for example, the transmission device 10000) and/or a service provider. Can be delivered to.
  • the feedback information may be used not only in the transmitting device 10000 but also in the receiving device 10004 or may not be provided.
  • Head orientation information is information on a position, direction, angle, and movement of a user's head.
  • the reception device 10004 may calculate viewport information based on the head orientation information.
  • the viewport information is information on the area of the point cloud video that the user is viewing.
  • the viewpoint is a point at which the user is watching the point cloud video, and may mean a center point of the viewport area. That is, the viewport is an area centered on a viewpoint, and the size and shape of the area may be determined by a field of view (FOV).
  • FOV field of view
  • the receiving device 10004 may extract viewport information based on a vertical or horizontal FOV supported by the device in addition to the head orientation information.
  • the receiving device 10004 performs a gaze analysis and the like to check the point cloud consumption method of the user, the point cloud video area the user is staring, and the gaze time.
  • the receiving device 10004 may transmit feedback information including a result of the gaze analysis to the transmitting device 10000.
  • Feedback information may be obtained during rendering and/or display.
  • Feedback information may be secured by one or more sensors included in the receiving device 10004.
  • the feedback information may be secured by the renderer 10007 or a separate external element (or device, component, etc.).
  • a dotted line in FIG. 1 shows a process of transmitting feedback information secured by the renderer 10007.
  • the point cloud content providing system may process (encode/decode) point cloud data based on feedback information. Accordingly, the point cloud video decoder 10006 may perform a decoding operation based on the feedback information. Also, the receiving device 10004 may transmit feedback information to the transmitting device 10000. The transmission device 10000 (or the point cloud video encoder 10002) may perform an encoding operation based on the feedback information. Therefore, the point cloud content providing system does not process (encode/decode) all point cloud data, but efficiently processes necessary data (e.g., point cloud data corresponding to the user's head position) based on feedback information. Point cloud content can be provided to users.
  • the transmission apparatus 10000 may be referred to as an encoder, a transmission device, a transmitter, a transmission system, and the like
  • the reception apparatus 10004 may be referred to as a decoder, a reception device, a receiver, a reception system, and the like.
  • Point cloud data (processed in a series of acquisition/encoding/transmission/decoding/rendering) processed in the point cloud content providing system of FIG. 1 according to embodiments may be referred to as point cloud content data or point cloud video data.
  • the point cloud content data may be used as a concept including metadata or signaling information related to the point cloud data.
  • Elements of the point cloud content providing system shown in FIG. 1 may be implemented by hardware, software, a processor, and/or a combination thereof.
  • FIG. 2 is a block diagram illustrating an operation of providing point cloud content according to embodiments.
  • the block diagram of FIG. 2 shows the operation of the point cloud content providing system described in FIG. 1.
  • the point cloud content providing system may process point cloud data based on point cloud compression coding (eg, G-PCC).
  • point cloud compression coding eg, G-PCC
  • the point cloud content providing system may acquire a point cloud video (20000).
  • the point cloud video is expressed as a point cloud belonging to a coordinate system representing a three-dimensional space.
  • the point cloud video according to embodiments may include a Ply (Polygon File format or the Stanford Triangle format) file.
  • Ply files contain point cloud data such as the geometry and/or attributes of the point.
  • the geometry includes the positions of the points.
  • the position of each point may be expressed by parameters (eg, values of each of the X-axis, Y-axis, and Z-axis) representing a three-dimensional coordinate system (eg, a coordinate system composed of XYZ axes).
  • the attribute includes attributes of points (eg, texture information of each point, color (YCbCr or RGB), reflectance (r), transparency, etc.).
  • a point has one or more attributes (or attributes).
  • one point may have an attribute of one color or two attributes of a color and reflectance.
  • geometry may be referred to as positions, geometry information, geometry data, and the like, and attributes may be referred to as attributes, attribute information, attribute data, and the like.
  • the point cloud content providing system (for example, the point cloud transmission device 10000 or the point cloud video acquisition unit 10001) provides points from information (eg, depth information, color information, etc.) related to the acquisition process of the point cloud video. Cloud data can be secured.
  • the point cloud content providing system may encode point cloud data (20001).
  • the point cloud content providing system may encode point cloud data based on point cloud compression coding.
  • the point cloud data may include the geometry and attributes of the point.
  • the point cloud content providing system may output a geometry bitstream by performing geometry encoding for encoding geometry.
  • the point cloud content providing system may output an attribute bitstream by performing attribute encoding for encoding the attribute.
  • the point cloud content providing system may perform attribute encoding based on geometry encoding.
  • the geometry bitstream and the attribute bitstream according to the embodiments may be multiplexed and output as one bitstream.
  • the bitstream according to embodiments may further include signaling information related to geometry encoding and attribute encoding.
  • the point cloud content providing system may transmit encoded point cloud data (20002).
  • the encoded point cloud data may be expressed as a geometry bitstream and an attribute bitstream.
  • the encoded point cloud data may be transmitted in the form of a bitstream together with signaling information related to encoding of the point cloud data (eg, signaling information related to geometry encoding and attribute encoding).
  • the point cloud content providing system may encapsulate the bitstream for transmitting the encoded point cloud data and transmit it in the form of a file or segment.
  • the point cloud content providing system may receive a bitstream including encoded point cloud data.
  • the point cloud content providing system may demultiplex the bitstream.
  • the point cloud content providing system can decode the encoded point cloud data (e.g., geometry bitstream, attribute bitstream) transmitted as a bitstream.
  • the point cloud content providing system may decode the point cloud video data based on signaling information related to encoding of the point cloud video data included in the bitstream.
  • a point cloud content providing system (for example, the receiving device 10004 or the point cloud video decoder 10005) may restore positions (geometry) of points by decoding a geometry bitstream.
  • the point cloud content providing system may restore the attributes of points by decoding the attribute bitstream based on the restored geometry.
  • the point cloud content providing system (for example, the receiving device 10004 or the point cloud video decoder 10005) may restore the point cloud video based on the decoded attributes and positions according to the restored geometry.
  • the point cloud content providing system may render the decoded point cloud data (20004 ).
  • the point cloud content providing system may render the decoded geometry and attributes through a decoding process according to various rendering methods. Points of the point cloud content may be rendered as a vertex having a certain thickness, a cube having a specific minimum size centered on the vertex position, or a circle centered on the vertex position. All or part of the rendered point cloud content is provided to the user through a display (e.g., VR/AR display, general display, etc.).
  • a display e.g., VR/AR display, general display, etc.
  • the point cloud content providing system may secure feedback information (20005).
  • the point cloud content providing system may encode and/or decode point cloud data based on the feedback information. Since the operation of the feedback information and point cloud content providing system according to the embodiments is the same as the feedback information and operation described in FIG. 1, a detailed description will be omitted.
  • FIG 3 shows an example of a point cloud video capture process according to embodiments.
  • FIGS. 1 to 2 shows an example of a point cloud video capture process of the point cloud content providing system described in FIGS. 1 to 2.
  • Point cloud content is an object located in various three-dimensional spaces (for example, a three-dimensional space representing a real environment, a three-dimensional space representing a virtual environment, etc.) and/or a point cloud video (images and/or Videos). Therefore, the point cloud content providing system according to the embodiments includes one or more cameras (eg, an infrared camera capable of securing depth information, color information corresponding to the depth information) to generate the point cloud content.
  • the point cloud video can be captured using an RGB camera that can extract the image), a projector (for example, an infrared pattern projector to secure depth information), and LiDAR.
  • the point cloud content providing system may obtain point cloud data by extracting a shape of a geometry composed of points in a 3D space from depth information, and extracting an attribute of each point from color information.
  • An image and/or an image according to embodiments may be captured based on at least one or more of an inward-facing method and an outward-facing method.
  • the left side of FIG. 3 shows an inword-facing scheme.
  • the in-word-facing method refers to a method in which one or more cameras (or camera sensors) located surrounding a central object capture a central object.
  • the in-word-facing method provides point cloud content that provides users with 360-degree images of core objects (e.g., provides users with 360-degree images of objects (e.g., key objects such as characters, players, objects, actors, etc.) VR/AR content).
  • the right side of FIG. 3 shows an outword-pacing scheme.
  • the outward-facing method refers to a method in which one or more cameras (or camera sensors) located surrounding the central object capture the environment of the central object other than the central object.
  • the outward-facing method may be used to generate point cloud content (eg, content representing an external environment that may be provided to a user of an autonomous vehicle) to provide a surrounding environment appearing from a user's point of view.
  • point cloud content eg, content representing an external environment that may be provided to a user of an autonomous vehicle
  • the point cloud content may be generated based on a capture operation of one or more cameras.
  • the point cloud content providing system may calibrate one or more cameras to set a global coordinate system before the capture operation.
  • the point cloud content providing system may generate point cloud content by synthesizing an image and/or image captured by the above-described capture method with an arbitrary image and/or image.
  • the capture operation described in FIG. 3 may not be performed.
  • the point cloud content providing system may perform post-processing on the captured image and/or image. That is, the point cloud content providing system removes unwanted areas (e.g., background), recognizes the space where captured images and/or images are connected, and performs an operation to fill in a spatial hole if there is. I can.
  • the point cloud content providing system may generate one point cloud content by performing coordinate system transformation on points of the point cloud video acquired from each camera.
  • the point cloud content providing system may perform a coordinate system transformation of points based on the position coordinates of each camera. Accordingly, the point cloud content providing system may generate content representing one wide range, or may generate point cloud content having a high density of points.
  • FIG. 4 shows an example of a point cloud video encoder according to embodiments.
  • the point cloud video encoder uses point cloud data (e.g., positions and/or positions of points) to adjust the quality of the point cloud content (e.g., lossless-lossless, loss-lossy, near-lossless) according to network conditions or applications. Or attributes) and perform an encoding operation.
  • point cloud data e.g., positions and/or positions of points
  • the quality of the point cloud content e.g., lossless-lossless, loss-lossy, near-lossless
  • 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 bitrate in order to provide it in accordance with the network environment.
  • the point cloud video encoder may perform geometry encoding and attribute encoding. Geometry encoding is performed before attribute encoding.
  • a point cloud video encoder includes a coordinate system transform unit (Transformation Coordinates unit, 40000), a quantization unit (40001), an octree analysis unit (40002), and a surface approximation analysis unit (Surface Approximation).
  • the coordinate system transform unit 40000, the quantization unit 40001, the octree analysis unit 40002, the surface aproximation analysis unit 40003, the arithmetic encoder 40004, and the geometry reconstruction unit 40005 perform geometry encoding. can do.
  • Geometry encoding according to embodiments may include octree geometry coding, direct coding, trisoup geometry encoding, and entropy encoding. Direct coding and trisoup geometry encoding are applied selectively or in combination. Also, geometry encoding is not limited to the above example.
  • the coordinate system conversion unit 40000 receives positions and converts them into a coordinate system.
  • positions may be converted into position information in a three-dimensional space (eg, a three-dimensional space represented by an XYZ coordinate system).
  • Location information of a 3D space according to embodiments may be referred to as geometry information.
  • the quantization unit 40001 quantizes geometry information. For example, the quantization unit 40001 may quantize points based on the minimum position values of all points (eg, minimum values on each axis with respect to the X-axis, Y-axis, and Z-axis). The quantization unit 40001 multiplies the difference between the minimum position value and the position value of each point by a preset quantum scale value, and then performs a quantization operation to find the nearest integer value by performing a lowering or a rounding. Thus, one or more points may have the same quantized position (or position value). The quantization unit 40001 according to embodiments performs voxelization based on quantized positions to reconstruct quantized points.
  • the quantization unit 40001 performs voxelization based on quantized positions to reconstruct quantized points.
  • Voxelization refers to the minimum unit expressing position information in a three-dimensional space.
  • Points of point cloud content (or 3D point cloud video) according to embodiments may be included in one or more voxels.
  • the quantization unit 40001 may match groups of points in a 3D space with voxels. According to embodiments, one voxel may include only one point.
  • one voxel may include one or more points.
  • a position of a center point of a corresponding voxel may be set based on positions of one or more points included in one voxel.
  • attributes of all positions included in one voxel may be combined and assigned to a corresponding voxel.
  • the octree analysis unit 40002 performs octree geometry coding (or octree coding) to represent voxels in an octree structure.
  • the octree structure represents points matched to voxels based on an octal tree structure.
  • the surface aproxiation analysis unit 40003 may analyze and approximate an octree.
  • the octree analysis and approximation according to the embodiments is a process of analyzing to voxelize a region including a plurality of points in order to efficiently provide octree and voxelization.
  • the arithmetic encoder 40004 entropy encodes the octree and/or the approximated octree.
  • the encoding method includes an Arithmetic encoding method.
  • a geometry bitstream is generated.
  • Color conversion unit 40006, attribute conversion unit 40007, RAHT conversion unit 40008, LOD generation unit 40009, lifting conversion unit 40010, coefficient quantization unit 40011 and/or Arismatic encoder 40012 Performs attribute encoding.
  • one point may have one or more attributes. Attribute encoding according to embodiments is applied equally to attributes of one point. However, when one attribute (eg, color) includes one or more elements, independent attribute encoding is applied to each element.
  • Attribute encoding 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 with an update/lifting step (Lifting Transform)) coding may be included.
  • RAHT region adaptive hierarchical transform
  • coding predictive transform coding
  • lifting transform coding may be selectively used, or a combination of one or more codings may be used.
  • attribute encoding according to embodiments is not limited to the above-described example.
  • the color conversion unit 40006 performs color conversion coding for converting color values (or textures) included in attributes.
  • the color conversion unit 40006 may convert the format of color information (eg, convert from RGB to YCbCr).
  • the operation of the color conversion unit 40006 according to the embodiments may be selectively applied according to color values included in attributes.
  • the geometry reconstruction unit 40005 reconstructs (decompresses) an octree and/or an approximated octree.
  • the geometry reconstruction unit 40005 reconstructs an octree/voxel based on a result of analyzing the distribution of points.
  • the reconstructed octree/voxel may be referred to as reconstructed geometry (or reconstructed geometry).
  • the attribute conversion unit 40007 performs attribute conversion for converting attributes based on the reconstructed geometry and/or positions for which geometry encoding has not been performed. As described above, since the attributes are dependent on geometry, the attribute conversion unit 40007 may convert the attributes based on the reconstructed geometry information. For example, the attribute conversion unit 40007 may convert an attribute of the point of the position based on the position value of the point included in the voxel. As described above, when a position of a center point of a corresponding voxel is set based on positions of one or more points included in one voxel, the attribute conversion unit 40007 converts attributes of one or more points. When trisoup geometry encoding is performed, the attribute converter 40007 may convert attributes based on trisoup geometry encoding.
  • the attribute conversion unit 40007 is an average value of attributes or attribute values (for example, color of each point, reflectance, etc.) of points neighboring within a specific position/radius from the position (or position value) of the center point of each voxel. Attribute conversion can be performed by calculating.
  • the attribute conversion unit 40007 may apply a weight according to a distance from a central point to each point when calculating an average value. Thus, each voxel has a position and a calculated attribute (or attribute value).
  • the attribute converter 40007 may search for neighboring points existing within a specific position/radius from the position of the center point of each voxel based on a K-D tree or a morton code.
  • the K-D tree is a binary search tree and supports a data structure that can manage points based on location so that the Nearest Neighbor Search (NNS) can be quickly performed.
  • the Molton code represents a coordinate value (for example, (x, y, z)) representing a three-dimensional position of all points as a bit value, and is generated by mixing the bits. For example, if the coordinate value indicating the position of the point is (5, 9, 1), the bit value of the coordinate value is (0101, 1001, 0001).
  • the attribute conversion unit 40007 may sort points based on a Molton code value and perform a shortest neighbor search (NNS) through a depth-first traversal process. After the attribute transformation operation, when a shortest neighbor search (NNS) is required in another transformation process for attribute coding, a K-D tree or a Molton code is used.
  • NSS shortest neighbor search
  • the converted attributes are input to the RAHT conversion unit 40008 and/or the LOD generation unit 40009.
  • the RAHT transform unit 40008 performs RAHT coding for predicting attribute information based on the reconstructed geometry information. For example, the RAHT transform unit 40008 may predict attribute information of a node at a higher level of the octree based on attribute information associated with a node at a lower level of the octree.
  • the LOD generator 40009 generates a level of detail (LOD).
  • LOD level of detail
  • the LOD according to the embodiments is a degree representing the detail of the point cloud content, and a smaller LOD value indicates that the detail of the point cloud content decreases, and a larger LOD value indicates that the detail of the point cloud content is high. Points can be classified according to LOD.
  • the lifting transform unit 40010 performs lifting transform coding that transforms the attributes of the point cloud based on weights. As described above, the lifting transform coding can be selectively applied.
  • the coefficient quantization unit 40011 quantizes attribute-coded attributes based on coefficients.
  • Arismatic encoder 40012 encodes quantized attributes based on Arismatic coding.
  • Elements of the point cloud video encoder of FIG. 4 are not shown in the drawing, but include one or more processors or integrated circuits configured to communicate with one or more memories included in the point cloud content providing apparatus. It may be implemented in hardware, software, firmware, or a combination thereof. One or more processors may perform at least one or more of the operations and/or functions of the elements of the point cloud video encoder of FIG. 4 described above. In addition, one or more processors may operate or execute a set of software programs and/or instructions for performing operations and/or functions of elements of the point cloud video encoder of FIG. 4.
  • One or more memories may include high speed random access memory, and nonvolatile memory (e.g., one or more magnetic disk storage devices, flash memory devices, or other nonvolatile solid state memory devices). Memory devices (solid-state memory devices, etc.).
  • FIG. 5 shows an example of a voxel according to embodiments.
  • voxels located in a three-dimensional space represented by a coordinate system composed of three axes of the X-axis, Y-axis, and Z-axis.
  • a point cloud video encoder eg, quantization unit 40001, etc.
  • FIG. 5 is an octree structure for recursive subdividing of a cubical axis-aligned bounding box defined by two poles (0,0,0) and (2 d , 2 d , 2 d) Shows an example of a voxel generated through.
  • One voxel includes at least one or more points.
  • the voxel can estimate spatial coordinates from the positional relationship with the voxel group.
  • voxels have attributes (color or reflectance, etc.) like pixels of a 2D image/video. A detailed description of the voxel is the same as that described with reference to FIG. 4 and thus will be omitted.
  • FIG. 6 shows an example of an octree and an occupancy code according to embodiments.
  • the point cloud content providing system (point cloud video encoder 10002) or the octree analysis unit 40002 of the point cloud video encoder efficiently manages the region and/or position of the voxel.
  • Octree geometry coding (or octree coding) based on an octree structure is performed.
  • the top of FIG. 6 shows an octree structure.
  • the three-dimensional space of the point cloud content according to the embodiments is represented by axes of a coordinate system (eg, X-axis, Y-axis, Z-axis).
  • the octree structure is created by recursive subdividing a cubical axis-aligned bounding box defined by two poles (0,0,0) and (2 d , 2 d , 2 d ). . 2d may be set to a value constituting the smallest bounding box surrounding all points of the point cloud content (or point cloud video).
  • d represents the depth of the octree.
  • the d value is determined according to Equation 1 below.
  • (x int n , y int n , z int n ) represents positions (or position values) of quantized points.
  • the entire 3D space may be divided into eight spaces according to the division.
  • Each divided space is represented by a cube with 6 faces.
  • each of the eight spaces is divided again based on the axes of the coordinate system (eg, X-axis, Y-axis, Z-axis).
  • the divided small space is also represented as a cube with 6 faces. This division method is applied until a leaf node of an octree becomes a voxel.
  • the lower part of FIG. 6 shows the octree's ocupancy code.
  • the octree's occupancy code is generated to indicate whether each of the eight divided spaces generated by dividing one space includes at least one point. Therefore, one Okufanshi code is represented by 8 child nodes. Each child node represents the occupancy of the divided space, and the child node has a value of 1 bit. Therefore, the Okufanshi code is expressed as an 8-bit code. That is, if at least one point is included in the space corresponding to the child node, the node has a value of 1. If the point is not included in the space corresponding to the child node (empty), the node has a value of 0. Since the ocupancy code shown in FIG.
  • the point cloud video encoder (for example, the Arismatic encoder 40004) according to the embodiments may entropy encode an ocupancy code. In addition, in order to increase compression efficiency, the point cloud video encoder can intra/inter code the ocupancy code.
  • the reception device (for example, the reception device 10004 or the point cloud video decoder 10006) according to the embodiments reconstructs an octree based on an ocupancy code.
  • a point cloud video encoder (eg, octree analysis unit 40002) may perform voxelization and octree coding to store positions of points.
  • the points in the 3D space are not always evenly distributed, there may be a specific area where there are not many points. In this case, it is inefficient to perform voxelization on the entire 3D space. For example, if there are almost no points in a specific area, it is not necessary to perform voxelization to the corresponding area.
  • the point cloud video encoder does not perform voxelization on the above-described specific region (or nodes other than the leaf nodes of the octree), but directly codes the positions of points included in the specific region. coding) can be performed. Coordinates of a direct coding point according to embodiments are referred to as a direct coding mode (DCM).
  • DCM direct coding mode
  • the point cloud video encoder may perform trisoup geometry encoding in which positions of points in a specific region (or node) are reconstructed based on voxels based on a surface model. .
  • Trisoup geometry encoding is a geometry encoding that expresses the representation of an object as a series of triangle meshes.
  • the point cloud video decoder can generate a point cloud from the mesh surface.
  • Direct coding and trisoup geometry encoding according to embodiments may be selectively performed.
  • direct coding and trisoup geometry encoding according to embodiments may be performed in combination with octree geometry coding (or octree coding).
  • the option to use direct mode for applying direct coding must be activated, and the node to which direct coding is applied is not a leaf node, but is below the threshold within a specific node. There must be points of. In addition, the total number of points subject to direct coding must not exceed a preset limit value.
  • the point cloud video encoder eg, the Arismatic encoder 40004
  • the embodiments may entropy-code the positions (or position values) of the points.
  • the point cloud video encoder determines a specific level of the octree (if the level is less than the depth d of the octree), and from that level, the surface model is used. It is possible to perform trisoup geometry encoding in which the position of a point in the node region is reconstructed based on voxels (tri-soup mode).
  • the point cloud video encoder according to embodiments may designate a level to which trisoup geometry encoding is applied. For example, if the specified level is equal to the depth of the octree, the point cloud video encoder does not operate in the try-soup mode.
  • the point cloud video encoder may operate in the try-soup mode only when the specified level is less than the depth value of the octree.
  • a 3D cube area of nodes of a designated level according to the embodiments is referred to as a block.
  • One block may include one or more voxels.
  • the block or voxel may correspond to a brick.
  • the geometry is represented by a surface.
  • the surface according to embodiments may intersect each edge (edge) of the block at most once.
  • one block has 12 edges, there are at least 12 intersections within one block. Each intersection is called a vertex (vertex, or vertex). Vertices existing along an edge are detected when there is at least one occupied voxel adjacent to the edge among all blocks sharing the edge.
  • An occupied voxel according to embodiments means a voxel including a point. The position of the vertex detected along the edge is the average position along the edge of all voxels among all blocks sharing the edge.
  • the point cloud video encoder When a vertex is detected, the point cloud video encoder according to the embodiments includes the starting point (x, y, z) of the edge and the direction vector of the edge ( x, y, z), vertex position values (relative position values within an edge) may be entropy-coded.
  • the point cloud video encoder for example, the geometry reconstruction unit 40005
  • the point cloud video encoder performs a triangle reconstruction, up-sampling, and voxelization process. To create reconstructed geometry (reconstructed geometry).
  • Vertices located at the edge of the block determine the surface through which the block passes.
  • the surface according to the embodiments is a non-planar polygon.
  • the triangle reconstruction process reconstructs the surface represented by a triangle based on the starting point of the edge, the direction vector of the edge, and the position value of the vertex.
  • the triangle reconstruction process is shown in Equation 2 below. 1 Calculate the centroid value of each vertex, 2 calculate the squared values to the values subtracting the center value from each vertex value, and calculate the sum of all the values.
  • each vertex is projected on the x-axis based on the center of the block, and projected on the (y, z) plane. If the projected value on the (y, z) plane is (ai, bi), ⁇ is obtained through atan2(bi, ai), and the vertices are aligned based on the ⁇ value.
  • Table 1 shows combinations of vertices for generating a triangle according to the number of vertices. Vertices are ordered from 1 to n.
  • the first triangle may be composed of 1st, 2nd, and 3rd vertices among the aligned vertices
  • the second triangle may be composed of 3rd, 4th, and 1st vertices among the aligned vertices.
  • the upsampling process is performed to voxelize by adding points in the middle along the edge of the triangle. Additional points are created based on the upsampling factor and the width of the block. The additional point is called a refined vertice.
  • the point cloud video encoder may voxelize refined vertices. In addition, the point cloud video encoder may perform attribute encoding based on the voxelized position (or position value).
  • FIG. 7 illustrates an example of a neighbor node pattern according to embodiments.
  • the point cloud video encoder may perform entropy coding based on context adaptive arithmetic coding.
  • the point cloud content providing system, the point cloud video encoder 10002 of FIG. 2, or the point cloud video encoder or Arismatic encoder 40004 of FIG. 4 can directly entropy code the ocupancy code. have.
  • the point cloud content providing system or point cloud video encoder performs entropy encoding (intra encoding) based on the ocupancy code of the current node and the ocupancy code of neighboring nodes, or entropy encoding (intermediate encoding) based on the ocupancy code of the previous frame. Encoding).
  • a frame refers to a set of point cloud videos generated at the same time.
  • the compression efficiency of intra-encoding/inter-encoding may vary depending on the number of referenced neighbor nodes. The larger the bit, the more complicated it is, but it can be skewed to one side, increasing the compression efficiency. For example, if you have a 3-bit context, you have to code in 8 ways. The divided coding part affects the complexity of the implementation. Therefore, it is necessary to match the appropriate level of compression efficiency and complexity.
  • a point cloud video encoder determines occupancy of neighboring nodes of each node of an octree and obtains a neighbor pattern value.
  • the neighboring node pattern is used to infer the occupancy pattern of the corresponding node.
  • the left side of FIG. 7 shows a cube corresponding to a node (a cube located in the center) and six cubes (neighbor nodes) that share at least one surface with the cube.
  • Nodes shown in the figure are nodes of the same depth (depth). Numbers shown in the figure indicate weights (1, 2, 4, 8, 16, 32, etc.) associated with each of the six nodes. Each weight is sequentially assigned according to the positions of neighboring nodes.
  • the right side of FIG. 7 shows neighboring node pattern values.
  • the neighbor node pattern value is the sum of values multiplied by the weights of the occupied neighbor nodes (neighbor nodes having points). Therefore, the value of the neighboring node pattern ranges from 0 to 63. If the neighbor node pattern value is 0, it indicates that no node (occupied node) has a point among neighboring nodes of the corresponding node. If the neighboring node pattern value is 63, it indicates that all neighboring nodes are occupied nodes. As shown in the figure, since neighboring nodes to which weights 1, 2, 4, and 8 are assigned are occupied nodes, the neighboring node pattern value is 15, which is the sum of 1, 2, 4, and 8.
  • the point cloud video encoder may perform coding according to the neighbor node pattern value (eg, if the neighbor node pattern value is 63, 64 types of coding are performed). According to embodiments, the point cloud video encoder may reduce coding complexity by changing a neighbor node pattern value (for example, based on a table changing 64 to 10 or 6).
  • the encoded geometry is reconstructed (decompressed) before attribute encoding is performed.
  • the geometry reconstruction operation may include changing the arrangement of the direct coded points (eg, placing the direct coded points in front of the point cloud data).
  • the geometry reconstruction process is triangular reconstruction, upsampling, voxelization, and the attribute depends on the geometry, so the attribute encoding is performed based on the reconstructed geometry.
  • the point cloud video encoder may reorganize or group points by LOD.
  • 8 shows point cloud content corresponding to the LOD.
  • the leftmost of FIG. 8 shows the original point cloud content.
  • the second figure from the left of FIG. 8 shows the distribution of the points of the lowest LOD, and the rightmost figure of FIG. 8 shows the distribution of the points of the highest LOD. That is, the points of the lowest LOD are sparse, and the points of the highest LOD are densely distributed. That is, as the LOD increases in the direction of the arrow indicated at the bottom of FIG. 8, the distance (or distance) between the points becomes shorter.
  • a point cloud content providing system or a point cloud video encoder (e.g., a point cloud video encoder 10002 in FIG. 2, a point cloud video encoder in FIG. 4, or an LOD generator 40009) ) Can generate LOD.
  • the LOD is generated by reorganizing the points into a set of refinement levels according to a set LOD distance value (or a set of Euclidean distance).
  • the LOD generation process is performed not only in the point cloud video encoder but also in the point cloud video decoder.
  • FIG. 9 shows examples (P0 to P9) of points of point cloud content distributed in a three-dimensional space.
  • the original order of FIG. 9 represents the order of points P0 to P9 before LOD generation.
  • the LOD based order of FIG. 9 represents the order of points according to LOD generation. Points are rearranged by LOD. Also, the high LOD includes points belonging to the low LOD.
  • LOD0 includes P0, P5, P4 and P2.
  • LOD1 includes the points of LOD0 and P1, P6 and P3.
  • LOD2 includes points of LOD0, points of LOD1 and P9, P8 and P7.
  • the point cloud video encoder may selectively or combine LOD-based predictive transform coding, lifting transform coding, and RAHT transform coding.
  • a point cloud video encoder may generate a predictor for points and perform LOD-based predictive transform coding to set a predicted attribute (or predicted attribute value) of each point. That is, N predictors may be generated for N points.
  • the predicted attribute (or attribute value) is a weight calculated based on the distance to each neighboring point on the attributes (or attribute values, for example, color, reflectance, etc.) of neighboring points set in the predictor of each point. It is set as the average value multiplied by (or weight value).
  • a point cloud video encoder e.g., the coefficient quantization unit 40011
  • Attributes, residual attribute values, attribute prediction residual values, prediction error attribute values, etc. can be quantized and inverse quantized. Is shown in Table 2.
  • Table 2 the inverse quantization process of the receiving device performed on the quantized residual attribute value is shown in Table 3.
  • the point cloud video encoder (for example, the arithmetic encoder 40012) entropy the quantized and dequantized residual attribute values as described above when there are points adjacent to the predictors of each point. You can code.
  • the point cloud video encoder (for example, the arithmetic encoder 40012) according to the embodiments may entropy-code the attributes of the corresponding point without performing the above-described process if there are no points adjacent to the predictor of each point.
  • the point cloud video encoder (for example, the lifting transform unit 40010) according to the examples generates a predictor of each point, sets the calculated LOD to the predictor, registers neighboring points, and Lifting transform coding can be performed by setting weights.
  • the lifting transform coding according to the embodiments is similar to the above-described LOD-based predictive transform coding, but differs in that a weight is accumulated and applied to an attribute value.
  • a process of cumulatively applying a weight to an attribute value according to embodiments is as follows.
  • the weights calculated by additionally multiplying the weights calculated for all predictors by the weights stored in the QW corresponding to the predictor indexes are cumulatively added to the update weight array by the indexes of neighboring nodes.
  • the value obtained by multiplying the calculated weight by the attribute value of the index of the neighboring node is accumulated and summed.
  • the predicted attribute value is calculated by additionally multiplying the attribute value updated through the lift update process by the weight updated through the lift prediction process (stored in QW).
  • a point cloud video encoder for example, the coefficient quantization unit 40011
  • the point cloud video encoder for example, the Arismatic encoder 40012
  • the point cloud video encoder entropy-codes the quantized attribute values.
  • the point cloud video encoder (for example, the RAHT transform unit 40008) according to the embodiments may perform RAHT transform coding that estimates the attributes of higher-level nodes by using an attribute associated with a node at a lower level of the octree. have.
  • RAHT transform coding is an example of attribute intra coding through octree backward scan.
  • the point cloud video encoder according to the embodiments scans from voxels to the entire area, and repeats the merging process up to the root node while merging the voxels into larger blocks in each step.
  • the merging process according to the embodiments is performed only for an occupied node.
  • the merging process is not performed for the empty node, and the merging process is performed for the node immediately above the empty node.
  • Equation 3 represents the RAHT transformation matrix.
  • g lx,y,z represents the average attribute value of voxels at level l.
  • g lx,y,z can be calculated from g l+1 2x,y,z and g l+1 2x+1,y,z.
  • g l-1 x,y,z are low-pass values and are used in the merging process at the next higher level.
  • h l-1 x,y,z are high-pass coefficients, and the high-pass coefficients in each step are quantized and entropy-coded (for example, encoding of the arithmetic encoder 40012).
  • the root node is generated as shown in Equation 4 below through the last g 1 0,0,0 and g 1 0,0,1.
  • the gDC value is also quantized and entropy coded like the high pass coefficient.
  • FIG. 10 shows an example of a point cloud video decoder according to embodiments.
  • the point cloud video decoder illustrated in FIG. 10 is an example of the point cloud video decoder 10006 described in FIG. 1 and may perform the same or similar operation as that of the point cloud video decoder 10006 described in FIG. 1.
  • the point cloud video decoder may receive a geometry bitstream and an attribute bitstream included 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 a geometry bitstream and outputs decoded geometry.
  • the attribute decoder outputs decoded attributes by performing attribute decoding on the attribute bitstream based on the decoded geometry.
  • the decoded geometry and decoded attributes are used to reconstruct the point cloud content.
  • FIG. 11 shows an example of a point cloud video decoder according to embodiments.
  • the point cloud video decoder illustrated in FIG. 11 is a detailed example of the point cloud video decoder described in FIG. 10, and may perform a decoding operation that is a reverse process of the encoding operation of the point cloud video encoder described in FIGS. 1 to 9.
  • the point cloud video decoder may perform geometry decoding and attribute decoding. Geometry decoding is performed before attribute decoding.
  • a point cloud video decoder includes an arithmetic decoder (11000), an octree synthesis unit (11001), a surface approximation synthesis unit (11002), and a geometry reconstruction unit. (geometry reconstruction unit, 11003), coordinates inverse transformation unit (11004), arithmetic decoder (11005), inverse quantization unit (11006), RAHT transform unit (11007), LOD generation A unit (LOD generation unit, 11008), an inverse lifting unit (11009), and/or a color inverse transformation unit (11010).
  • the arithmetic decoder 11000, the octree synthesizer 11001, the surface opoxidation synthesizer 11002, the geometry reconstruction unit 11003, and the coordinate system inverse transform unit 11004 may perform geometry decoding.
  • Geometry decoding according to embodiments may include direct decoding and trisoup geometry decoding. Direct decoding and trisoup geometry decoding are selectively applied. Further, the geometry decoding is not limited to the above example, and is performed in the reverse process of the geometry encoding described in FIGS. 1 to 9.
  • the Arismatic decoder 11000 decodes the received geometry bitstream based on Arismatic coding.
  • the operation of the Arismatic decoder 11000 corresponds to the reverse process of the Arismatic encoder 40004.
  • the octree synthesizer 11001 may generate an octree by obtaining an ocupancy code from a decoded geometry bitstream (or information on a geometry obtained as a result of decoding).
  • a detailed description of the OQFancy code is as described in FIGS. 1 to 9.
  • the surface opoxidation synthesizer 11002 may synthesize a surface based on the decoded geometry and/or the generated octree.
  • the geometry reconstruction unit 11003 may regenerate the geometry based on the surface and/or the decoded geometry. 1 to 9, direct coding and trisoup geometry encoding are selectively applied. Therefore, the geometry reconstruction unit 11003 directly fetches and adds position information of points to which direct coding is applied. In addition, when trisoup geometry encoding is applied, the geometry reconstruction unit 11003 performs a reconstruction operation of the geometry reconstruction unit 40005, for example, triangle reconstruction, up-sampling, and voxelization to restore the geometry. have. Detailed contents are the same as those described in FIG. 6 and thus will be omitted.
  • the reconstructed geometry may include a point cloud picture or frame that does not include attributes.
  • the coordinate system inverse transform unit 11004 may obtain positions of points by transforming a coordinate system based on the restored geometry.
  • Arithmetic decoder 11005, inverse quantization unit 11006, RAHT conversion unit 11007, LOD generation unit 11008, inverse lifting unit 11009, and/or color inverse conversion unit 11010 are the attributes described in FIG. Decoding can be performed.
  • Attribute decoding according to embodiments includes Region Adaptive Hierarchical Transform (RAHT) decoding, Interpolation-based hierarchical nearest-neighbor prediction-Prediction Transform decoding, and interpolation-based hierarchical nearest-neighbor prediction with an update/lifting.
  • step (Lifting Transform)) decoding may be included.
  • the above three decodings may be used selectively, or a combination of one or more decodings may be used.
  • attribute decoding according to embodiments is not limited to the above-described example.
  • the Arismatic decoder 11005 decodes the attribute bitstream by Arismatic coding.
  • the inverse quantization unit 11006 inverse quantizes information on the decoded attribute bitstream or the attribute obtained as a result of decoding, and outputs inverse quantized attributes (or attribute values). Inverse quantization can be selectively applied based on the attribute encoding of the point cloud video encoder.
  • the RAHT conversion unit 11007, the LOD generation unit 11008, and/or the inverse lifting unit 11009 may process reconstructed geometry and inverse quantized attributes. As described above, the RAHT conversion unit 11007, the LOD generation unit 11008, and/or the inverse lifting unit 11009 may selectively perform a decoding operation corresponding thereto according to the encoding of the point cloud video encoder.
  • the color inverse transform unit 11010 performs inverse transform coding for inverse transforming a color value (or texture) included in the decoded attributes.
  • the operation of the color inverse transform unit 11010 may be selectively performed based on the operation of the color transform unit 40006 of the point cloud video encoder.
  • the elements of the point cloud video decoder of FIG. 11 are not shown in the figure, but include one or more processors or integrated circuits configured to communicate with one or more memories included in the point cloud content providing system. It may be implemented in hardware, software, firmware, or a combination thereof. One or more processors may perform at least one or more of the operations and/or functions of the elements of the point cloud video decoder of FIG. 11 described above. In addition, one or more processors may operate or execute a set of software programs and/or instructions for performing operations and/or functions of elements of the point cloud video decoder of FIG. 11.
  • the transmission device illustrated in FIG. 12 is an example of the transmission device 10000 of FIG. 1 (or the point cloud video encoder of FIG. 4 ).
  • the transmission device illustrated in FIG. 12 may perform at least one or more of the same or similar operations and methods as the operations and encoding methods of the point cloud video encoder described in FIGS. 1 to 9.
  • the transmission device includes a data input unit 12000, a quantization processing unit 12001, a voxelization processing unit 12002, an octree occupancy code generation unit 12003, a surface model processing unit 12004, and an intra/ Inter-coding processing unit (12005), Arithmetic coder (12006), metadata processing unit (12007), color conversion processing unit (12008), attribute transformation processing unit (or attribute transformation processing unit) (12009), prediction/lifting/RAHT transformation
  • a processing unit 12010, an Arithmetic coder 12011, and/or a transmission processing unit 12012 may be included.
  • the data input unit 12000 receives or acquires point cloud data.
  • the data input unit 12000 may perform the same or similar operation and/or an acquisition method to the operation and/or acquisition method of the point cloud video acquisition unit 10001 (or the acquisition process 20000 described in FIG. 2 ).
  • the coder 12006 performs geometry encoding.
  • the geometry encoding according to the embodiments is the same as or similar to the geometry encoding described in FIGS. 1 to 9, so a detailed description thereof will be omitted.
  • the quantization processing unit 12001 quantizes a geometry (eg, a position value or a position value of points).
  • the operation and/or quantization of the quantization processing unit 12001 is the same as or similar to the operation and/or quantization of the quantization unit 40001 described in FIG. 4. Detailed descriptions are the same as those described in FIGS. 1 to 9.
  • the voxelization processing unit 12002 voxelizes the position values of the quantized points.
  • the voxelization processing unit 120002 may perform the same or similar operation and/or process as the operation and/or the voxelization process of the quantization unit 40001 described in FIG. 4. Detailed descriptions are the same as those described in FIGS. 1 to 9.
  • the octree ocupancy code generation unit 12003 performs octree coding on positions of voxelized points based on an octree structure.
  • the octree ocupancy code generation unit 12003 may generate an ocupancy code.
  • the octree occupancy code generation unit 12003 may perform the same or similar operation and/or method as the operation and/or method of the point cloud video encoder (or octree analysis unit 40002) described in FIGS. 4 and 6. . Detailed descriptions are the same as those described in FIGS. 1 to 9.
  • the surface model processing unit 12004 may perform trisoup geometry encoding for reconstructing positions of points in a specific area (or node) based on a voxel based on a surface model.
  • the face model processing unit 12004 may perform the same or similar operation and/or method as the operation and/or method of the point cloud video encoder (for example, the surface aproximation analysis unit 40003) described in FIG. 4. Detailed descriptions are the same as those described in FIGS. 1 to 9.
  • the intra/inter coding processing unit 12005 may intra/inter code point cloud data.
  • the intra/inter coding processing unit 12005 may perform the same or similar coding as the intra/inter coding described in FIG. 7. The detailed description is the same as described in FIG. 7.
  • the intra/inter coding processor 12005 may be included in the arithmetic coder 12006.
  • the arithmetic coder 12006 entropy encodes an octree and/or an approximated octree of point cloud data.
  • the encoding method includes an Arithmetic encoding method.
  • the Arismatic coder 12006 performs the same or similar operation and/or method to the operation and/or method of the Arismatic encoder 40004.
  • the metadata processing unit 12007 processes metadata about point cloud data, for example, a set value, and provides it to a necessary processing process such as geometry encoding and/or attribute encoding. Also, the metadata processing unit 12007 according to embodiments may generate and/or process signaling information related to geometry encoding and/or attribute encoding. Signaling information according to embodiments may be encoded separately from geometry encoding and/or attribute encoding. In addition, signaling information according to embodiments may be interleaved.
  • the color conversion processing unit 12008, the attribute conversion processing unit 12009, the prediction/lifting/RAHT conversion processing unit 12010, and the Arithmetic coder 12011 perform attribute encoding.
  • Attribute encoding according to embodiments is the same as or similar to the attribute encoding described in FIGS. 1 to 9, and thus a detailed description thereof will be omitted.
  • the color conversion processing unit 12008 performs color conversion coding for converting color values included in attributes.
  • the color conversion processing unit 12008 may perform color conversion coding based on the reconstructed geometry. Description of the reconstructed geometry is the same as described in FIGS. 1 to 9. In addition, the same or similar operation and/or method to the operation and/or method of the color conversion unit 40006 described in FIG. 4 is performed. Detailed description is omitted.
  • the attribute transformation processing unit 12009 performs attribute transformation for transforming attributes based on positions in which geometry encoding has not been performed and/or reconstructed geometry.
  • the attribute conversion processing unit 12009 performs the same or similar operation and/or method to the operation and/or method of the attribute conversion unit 40007 described in FIG. 4. Detailed description is omitted.
  • the prediction/lifting/RAHT transform processing unit 12010 may code transformed attributes by using one or a combination of RAHT coding, LOD-based predictive transform coding, and lifting transform coding.
  • the prediction/lifting/RAHT conversion processing unit 12010 performs at least one of the same or similar operations as the RAHT conversion unit 40008, LOD generation unit 40009, and lifting conversion unit 40010 described in FIG. 4. do.
  • descriptions of LOD-based predictive transform coding, lifting transform coding, and RAHT transform coding are the same as those described with reference to FIGS. 1 to 9, and thus detailed descriptions thereof will be omitted.
  • the Arismatic coder 12011 may encode coded attributes based on Arismatic coding.
  • the Arismatic coder 12011 performs the same or similar operation and/or method to the operation and/or method of the Arismatic encoder 40012.
  • the transmission processing unit 12012 transmits each bitstream including the encoded geometry and/or the encoded attribute and/or metadata, or transmits the encoded geometry and/or the encoded attribute and/or metadata. It can be configured and transmitted as one bitstream.
  • the bitstream may include one or more sub-bitstreams.
  • the bitstream according to the embodiments includes a sequence parameter set (SPS) for signaling of a sequence level, a geometry parameter set (GPS) for signaling of geometry information coding, an attribute parameter set (APS) for signaling of attribute information coding, and a tile.
  • SPS sequence parameter set
  • GPS geometry parameter set
  • APS attribute parameter set
  • Slice data may include information on one or more slices.
  • One slice according to embodiments may include one geometry bitstream (Geom0 0 ) and one or more attribute bitstreams (Attr0 0 and Attr1 0 ).
  • a slice refers to a series of syntax elements representing all or part of a coded point cloud frame.
  • the TPS may include information about each tile (eg, coordinate value information and height/size information of a bounding box) with respect to one or more tiles.
  • the geometry bitstream may include a header and a payload.
  • the header of the geometry bitstream according to the embodiments may include identification information (geom_parameter_set_id) of a parameter set included in GPS, a tile identifier (geom_tile_id), a slice identifier (geom_slice_id), information about data included in the payload, and the like. have.
  • the metadata processing unit 12007 may generate and/or process signaling information and transmit the generated and/or processed signaling information to the transmission processing unit 12012.
  • elements that perform geometry encoding and elements that perform attribute encoding may share data/information with each other as dotted line processing.
  • the transmission processing unit 12012 according to the embodiments may perform the same or similar operation and/or transmission method as the operation and/or transmission method of the transmitter 10003. Detailed descriptions are the same as those described in FIGS. 1 to 2, and thus will be omitted.
  • FIG 13 is an example of a reception device according to embodiments.
  • the receiving device illustrated in FIG. 13 is an example of the receiving device 10004 of FIG. 1 (or the point cloud video decoder of FIGS. 10 and 11 ).
  • the receiving device illustrated in FIG. 13 may perform at least one or more of the same or similar operations and methods as the operations and decoding methods of the point cloud video decoder described in FIGS. 1 to 11.
  • the receiving device includes a receiving unit 13000, a receiving processing unit 13001, an arithmetic decoder 13002, an octree reconstruction processing unit 13003 based on an occupancy code, and a surface model processing unit (triangle reconstruction).
  • a receiving unit 13000 Up-sampling, voxelization) 13004, inverse quantization processing unit 13005, metadata parser 13006, arithmetic decoder 13007, inverse quantization processing unit 13008, prediction A /lifting/RAHT inverse transformation processing unit 13009, an inverse color transformation processing unit 13010, and/or a renderer 13011 may be included.
  • Each component of the decoding according to the embodiments may perform the reverse process of the component of the encoding according to the embodiments.
  • the receiving unit 13000 receives point cloud data.
  • the receiving unit 13000 may perform the same or similar operation and/or a receiving method to the operation and/or receiving method of the receiver 10005 of FIG. 1. Detailed description is omitted.
  • the reception processor 13001 may obtain a geometry bitstream and/or an attribute bitstream from received data.
  • the reception processing unit 13001 may be included in the reception unit 13000.
  • the arithmetic decoder 13002, the ocupancy code-based octree reconstruction processing unit 13003, the surface model processing unit 13004, and the inverse quantization processing unit 13005 may perform geometry decoding. Since the geometry decoding according to the embodiments is the same as or similar to the geometry decoding described in FIGS. 1 to 10, a detailed description will be omitted.
  • the Arismatic decoder 13002 may decode a geometry bitstream based on Arismatic coding.
  • the Arismatic decoder 13002 performs the same or similar operation and/or coding as the operation and/or coding of the Arismatic decoder 11000.
  • the ocupancy code-based octree reconstruction processing unit 13003 may obtain an ocupancy code from a decoded geometry bitstream (or information on a geometry obtained as a result of decoding) to reconstruct the octree.
  • the ocupancy code-based octree reconstruction processing unit 13003 performs the same or similar operation and/or method as the operation of the octree synthesis unit 11001 and/or the method of generating an octree.
  • the surface model processing unit 13004 decodes the trisoup geometry based on the surface model method and reconstructs the related geometry (e.g., triangle reconstruction, up-sampling, voxelization). You can do it.
  • the surface model processing unit 13004 performs an operation identical or similar to that of the surface opoxidation synthesis unit 11002 and/or the geometry reconstruction unit 11003.
  • the inverse quantization processor 13005 may inverse quantize the decoded geometry.
  • the metadata parser 13006 may parse metadata included in the received point cloud data, for example, a setting value.
  • the metadata parser 13006 may pass metadata to geometry decoding and/or attribute decoding. A detailed description of the metadata is the same as that described with reference to FIG. 12, and thus will be omitted.
  • the arithmetic decoder 13007, the inverse quantization processing unit 13008, the prediction/lifting/RAHT inverse transformation processing unit 13009, and the color inverse transformation processing unit 13010 perform attribute decoding. Since attribute decoding is the same as or similar to the attribute decoding described in FIGS. 1 to 10, detailed descriptions will be omitted.
  • the Arismatic decoder 13007 may decode the attribute bitstream through Arismatic coding.
  • the arithmetic decoder 13007 may decode the attribute bitstream based on the reconstructed geometry.
  • the Arismatic decoder 13007 performs the same or similar operation and/or coding as the operation and/or coding of the Arismatic decoder 11005.
  • the inverse quantization processor 13008 may inverse quantize the decoded attribute bitstream.
  • the inverse quantization processing unit 13008 performs the same or similar operation and/or method as the operation and/or the inverse quantization method of the inverse quantization unit 11006.
  • the prediction/lifting/RAHT inverse transform processing unit 13009 may process reconstructed geometry and inverse quantized attributes.
  • the prediction/lifting/RAHT inverse transform processing unit 13009 is the same or similar to the operations and/or decodings of the RAHT transform unit 11007, the LOD generation unit 11008 and/or the inverse lifting unit 11009, and/or At least one or more of the decodings is performed.
  • the inverse color transform processing unit 13010 according to embodiments performs inverse transform coding for inverse transforming a color value (or texture) included in the decoded attributes.
  • the color inverse transform processing unit 13010 performs the same or similar operation and/or inverse transform coding as the operation and/or inverse transform coding of the color inverse transform unit 11010.
  • the renderer 13011 may render point cloud data.
  • FIG. 14 shows an example of a structure capable of interworking with a method/device for transmitting and receiving point cloud data according to embodiments.
  • the structure of FIG. 14 is 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-mount display (HMD) 17700. At least one of them represents a configuration connected to the cloud network 17100.
  • the robot 17100, the autonomous vehicle 17200, the XR device 17300, the smartphone 17400, the home appliance 17500, and the like are referred to as devices.
  • the XR device 17300 may correspond to a point cloud compressed data (PCC) device according to embodiments or may be interlocked with a PCC device.
  • PCC point cloud compressed data
  • the cloud network 17000 may constitute a part of the cloud computing infrastructure or may refer to a network that exists in the cloud computing infrastructure.
  • the cloud network 17000 may be configured using a 3G network, a 4G or Long Term Evolution (LTE) network, or a 5G network.
  • LTE Long Term Evolution
  • the server 17600 includes at least one of a robot 17100, an autonomous vehicle 17200, an XR device 17300, a smartphone 17400, a home appliance 17500, and/or an HMD 17700, and a cloud network 17000.
  • the connected devices 17100 to 17700 may be connected through, and may assist at least some of the processing of the connected devices.
  • the HMD (Head-Mount Display) 17700 represents one of types in which an XR device and/or a PCC device according to embodiments may be implemented.
  • the HMD type device according to the embodiments includes a communication unit, a control unit, a memory unit, an I/O unit, a sensor unit, and a power supply unit.
  • the devices 17100 to 17500 shown in FIG. 14 may be interlocked/coupled with the point cloud data transmission/reception apparatus according to the above-described embodiments.
  • the XR/PCC device 17300 is applied with PCC and/or XR (AR+VR) technology to provide a head-mount display (HMD), a head-up display (HUD) provided in a vehicle, a television, a mobile phone, a smart phone, It may be implemented as a computer, a wearable device, a home appliance, a digital signage, a vehicle, a fixed robot or a mobile robot.
  • HMD head-mount display
  • HUD head-up display
  • vehicle a television
  • mobile phone a smart phone
  • It may be implemented as a computer, a wearable device, a home appliance, a digital signage, a vehicle, a fixed robot or a mobile robot.
  • the XR/PCC device 17300 analyzes 3D point cloud data or image data acquired through various sensors or from an external device to generate position data and attribute data for 3D points, thereby Information can be obtained, and the XR object to be output can be rendered and output.
  • the XR/PCC device 17300 may output an XR object including additional information on the recognized object in correspondence with the recognized object.
  • the autonomous vehicle 17200 may be implemented as a mobile robot, a vehicle, or an unmanned aerial vehicle by applying PCC technology and XR technology.
  • the autonomous driving vehicle 17200 to which the XR/PCC technology is applied may refer to an autonomous driving vehicle equipped with a means for providing an XR image, or an autonomous driving vehicle that is an object of control/interaction within the XR image.
  • the autonomous vehicle 17200 which is an object of control/interaction in the XR image, is distinguished from the XR device 17300 and may be interlocked with each other.
  • the autonomous vehicle 17200 having a means for providing an XR/PCC image may obtain sensor information from sensors including a camera, and may output an XR/PCC image generated based on the acquired sensor information.
  • the autonomous vehicle 17200 may provide an XR/PCC object corresponding to a real object or an object in a screen to the occupant by outputting an XR/PCC image with a HUD.
  • the XR/PCC object when the XR/PCC object is output to the HUD, at least a part of the XR/PCC object may be output to overlap the actual object facing the occupant's gaze.
  • the XR/PCC object when the XR/PCC object is output on a display provided inside the autonomous vehicle, at least a part of the XR/PCC object may be output to overlap the object in the screen.
  • the autonomous vehicle 17200 may output XR/PCC objects corresponding to objects such as lanes, other vehicles, traffic lights, traffic signs, motorcycles, pedestrians, and buildings.
  • VR Virtual Reality
  • AR Augmented Reality
  • MR Magnetic Reality
  • PCC Point Cloud Compression
  • VR technology is a display technology that provides objects or backgrounds in the real world only as CG images.
  • AR technology refers to a technology that shows a virtually created CG image on an image of a real object.
  • the MR technology is similar to the above-described AR technology in that it mixes and combines virtual objects in the real world.
  • real objects and virtual objects made of CG images are clear, and virtual objects are used in a form that complements real objects, whereas in MR technology, virtual objects are regarded as having the same characteristics as real objects. It is distinct from technology. More specifically, for example, it is a hologram service to which the aforementioned MR technology is applied.
  • VR, AR, and MR technologies are sometimes referred to as XR (extended reality) technology rather than clearly distinguishing between them. Therefore, the embodiments of the present specification are applicable to all of VR, AR, MR, and XR technologies.
  • This technology can be applied to encoding/decoding based on PCC, V-PCC, and G-PCC technology.
  • the PCC method/apparatus according to the embodiments may be applied to a vehicle providing an autonomous driving service.
  • Vehicles providing autonomous driving services are connected to PCC devices to enable wired/wireless communication.
  • the point cloud compressed data (PCC) transmission/reception device receives/processes AR/VR/PCC service related content data that can be provided with an autonomous driving service when connected to enable wired/wireless communication with a vehicle. Can be transferred to the vehicle.
  • the point cloud transmission/reception device may receive/process content data related to AR/VR/PCC service according to a user input signal input through the user interface device and provide it to the user.
  • the vehicle or user interface device may receive a user input signal.
  • the user input signal according to embodiments may include a signal indicating an autonomous driving service.
  • the point cloud video encoder of the transmitting side may further perform a spatial division process of dividing the point cloud data into one or more 3D blocks before encoding the point cloud data. That is, in order to perform encoding and transmission operations of the transmitting device and decoding and rendering operations of the receiving device in real time and to be processed with low delay, the transmitting device may spatially divide the point cloud data into a plurality of regions. In addition, the transmitting device independently or non-independently encodes the spatially divided regions (or blocks), thereby enabling random access and parallel encoding in the three-dimensional space occupied by the point cloud data. to provide. In addition, by performing encoding and decoding independently or non-independently in units of spatially divided regions (or blocks), the transmitting device and the receiving device may prevent errors accumulated in the encoding and decoding process.
  • 15 is a diagram illustrating another example of a point cloud transmission apparatus according to embodiments, and is an example including a space division unit.
  • the point cloud transmission apparatus includes a data input unit 51001, a coordinate system conversion unit 51002, a quantization processing unit 51003, a spatial division unit 51004, a signaling processing unit 51005, a geometry encoder 51006, and an attribute encoder. (51007), and a transmission processing unit (51008).
  • the coordinate system transform unit 51002, the quantization processing unit 51003, the spatial division unit 51004, the geometry encoder 51006, and the attribute encoder 51007 may be referred to as a point cloud video encoder.
  • the data input unit 51001 may perform some or all of the operations of the point cloud video acquisition unit 10001 of FIG. 1, or may perform some or all of the operations of the data input unit 12000 of FIG. 12.
  • the coordinate system conversion unit 51002 may perform some or all of the operations of the coordinate system conversion unit 40000 of FIG. 4.
  • the quantization processor 5103 may perform some or all of the operations of the quantization unit 40001 of FIG. 4, or may perform some or all of the operations of the quantization processor 12001 of FIG. 12.
  • the spatial dividing unit 51004 may spatially divide the point cloud data quantized and output from the quantization processing unit 5103 into one or more 3D blocks based on a bounding box and/or a sub-bounding box.
  • the 3D block may mean a tile group or a tile or a slice or a coding unit (CU), a prediction unit (PU), or a transformation unit (TU).
  • the signaling information for spatial division is entropy-encoded in the signaling processing unit 51005 and then transmitted through the transmission processing unit 51008 in the form of a bitstream.
  • the point cloud object corresponding to the point cloud data can be represented in the form of a box based on a coordinate system, which is called a bounding box.
  • the bounding box refers to a cube that can contain all the points of the point cloud.
  • 16(b) and 16(c) show an example in which the bounding box of FIG. 16(a) is divided into tile 1 (tile 1#) and tile 2 (tile 2#), and tile 2 (tile 2#) Shows an example of splitting into slice 1 (slice 1#) and slice 2 (slice 2#) again.
  • the point cloud content may be one person, several people, one object, or several objects such as an actor, but may be a map for autonomous driving in a larger range, or a map for indoor navigation of a robot. have.
  • the point cloud content may be a vast amount of data connected locally.
  • tile partitioning can be performed before compression of the point cloud content is performed. For example, you can divide the 101 into one tile and the other 102 into another tile in the building. In order to support fast encoding/decoding by applying parallelization to the divided tiles, it can be partitioned (or divided) into slices again. This may be referred to as slice partitioning (or splitting).
  • a tile may mean a partial area (eg, a rectangular cube) of a 3D space occupied by point cloud data according to embodiments.
  • a tile according to embodiments may include one or more slices. The tile according to the embodiments is divided (partitioned) into one or more slices, so that the point cloud video encoder may encode point cloud data in parallel.
  • a slice is a unit of data (or bitstream) that can be independently encoded by a point cloud video encoder according to embodiments and/or data that can be independently decoded by a point cloud video decoder ( Or bitstream).
  • a slice according to embodiments may refer to a set of data in a 3D space occupied by point cloud data, or may refer to a set of some data among point cloud data.
  • a slice may mean an area of points or a set of points included in a tile according to embodiments.
  • a tile according to embodiments may be divided into one or more slices based on the number of points included in one tile. For example, one tile may mean a set of points divided by the number of points.
  • a tile according to embodiments may be divided into one or more slices based on the number of points, and some data may be split or merged during the dividing process. That is, a slice may be a unit that can be independently coded within a corresponding tile. A tile divided into spaces as described above can be divided into one or more slices for fast and efficient processing.
  • the point cloud video encoder may encode point cloud data in a slice unit or a tile unit including one or more slices.
  • the point cloud video encoder according to embodiments may perform different quantization and/or transformation for each tile or for each slice.
  • Positions of one or more 3D blocks (e.g., slices) spatially divided by the spatial dividing unit 51004 are output to a geometry encoder 51006, and attribute information (or attributes) is used as an attribute encoder 51007. Is output.
  • the positions may be position information of points included in a divided unit (box or block or tile or tile group or slice), and is referred to as geometry information.
  • the geometry encoder 51006 constructs and encodes (ie, compresses) an octree based on positions output from the spatial division unit 51004 to output a geometry bitstream.
  • the geometry encoder 51006 may reconstruct the octree and/or the approximated octree and output it to the attribute encoder 51007.
  • the reconstructed octree may be referred to as reconstructed geometry (or reconstructed geometry).
  • the attribute encoder 51007 encodes (ie, compresses) the attributes output from the spatial division unit 51004 based on the reconstructed geometry output from the geometry encoder 51006 to output an attribute bitstream.
  • FIG. 17 is a detailed block diagram showing another example of a geometry encoder 51006 and an attribute encoder 51007 according to embodiments.
  • the voxelization processing unit 53001, the octree generation unit 53002, the geometry information prediction unit 53003, and the arithmetic coder 53004 of the geometry encoder 51006 of FIG. 17 are an octree analysis unit 40002 and a surface of FIG. Part or all of the operations of the aproximation analysis unit 40003, the arithmetic encoder 40004, and the geometry reconstruction unit 40005 may be performed, or the voxelization processing unit 12002 of FIG. Some or all of the operations of the code generation unit 12003, the surface model processing unit 12004, the intra/inter coding processing unit 12005, and the Arithmetic coder 12006 may be performed.
  • the attribute encoder 51007 of FIG. 17 includes a color conversion processing unit 53005, an attribute conversion processing unit 5306, an LOD configuration unit 53007, a neighboring point set configuration unit 5308, an attribute information prediction unit 5301, and residual attribute information. It may include a quantization processing unit 5301 and an arithmetic coder 5301.
  • a quantization processing unit may be further provided between the spatial division unit 51004 and the voxelization processing unit 53001.
  • the quantization processing unit quantizes positions of one or more 3D blocks (eg, slices) spatially divided by the spatial division unit 51004.
  • the quantization unit may perform some or all of the operations of the quantization unit 40001 of FIG. 4, or may perform some or all of the operations of the quantization processing unit 12001 of FIG. 12.
  • the quantization processing unit 51003 of FIG. 15 may or may not be omitted.
  • the voxelization processor 53001 performs voxelization based on positions of one or more spatially divided 3D blocks (eg, slices) or quantized positions.
  • Voxelization refers to the minimum unit expressing position information in a three-dimensional space.
  • Points of point cloud content (or 3D point cloud video) may be included in one or more voxels.
  • one voxel may include one or more points.
  • quantization is performed before voxelization is performed, a case in which a plurality of points belong to one voxel may occur.
  • the voxelization processing unit 53001 may output the redundant points belonging to one voxel to the octree generation unit 53002 as it is without merging, or merge the redundant points into one point and the octree generation unit ( 53002).
  • the octree generation unit 53002 generates an octree based on a voxel output from the voxelization processing unit 53001.
  • the geometry information prediction unit 53003 predicts and compresses geometry information based on the octree generated by the octree generation unit 53002, and outputs the prediction and compression to the arithmetic coding unit 53004.
  • the geometry information prediction unit 53003 reconstructs the geometry based on the positions changed through 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 prediction unit 53003.
  • the reconstructed geometry may also be provided to the attribute conversion processing unit 5306 of the attribute encoder 51007.
  • the color conversion processing unit 53005 of the attribute encoder 51007 corresponds to the color conversion unit 40006 of FIG. 4 or the color conversion processing unit 12008 of FIG. 12.
  • the color conversion processing unit 5305 according to the embodiments performs color conversion coding for converting color values (or textures) included in attributes provided from the data input unit 51101 and/or the spatial division unit 51004. .
  • the color conversion processor 5305 may convert the format of color information (eg, convert from RGB to YCbCr).
  • the operation of the color conversion processing unit 5305 according to the embodiments may be selectively applied according to color values included in attributes.
  • the color conversion processing unit 5305 may perform color conversion coding based on the reconstructed geometry. For a detailed description of the geometry reconstruction, reference will be made to the descriptions of FIGS. 1 to 9.
  • the attribute conversion processing unit 5306 may perform attribute conversion for converting attributes based on positions in which geometry encoding has not been performed and/or reconstructed geometry.
  • the attribute conversion processing unit 53006 may be referred to as a color recoloring unit.
  • the operation of the attribute conversion processing unit 5306 according to the embodiments may be optionally applied according to whether or not duplicated points are merged. According to an embodiment, whether the overlapping points are merged is performed by the voxelization processing unit 53001 of the geometry encoder 51006.
  • the attribute transformation is performed by the attribute transformation processing unit 5306 as an embodiment.
  • the attribute conversion processing unit 5306 performs the same or similar operation and/or method as the operation and/or method of the attribute conversion unit 40007 of FIG. 4 or the attribute conversion processing unit 12009 of FIG. 12.
  • the geometry information reconstructed by the geometry information prediction unit 53003 and the attribute information output from the attribute conversion processing unit 53006 are provided to the LOD construction unit 53007 for attribute compression.
  • the attribute information output from the attribute transformation processing unit 5306 is a combination of any one or two or more of a RAHT coding method, an LOD-based predictive transform coding method, and a lifting transform coding method based on reconstructed geometry information. Can be compressed.
  • attribute compression is performed by combining any one or both of an LOD-based predictive transform coding technique and a lifting transform coding technique. Therefore, description of the RAHT coding technique will be omitted.
  • description of RAHT transform coding refer to the description of FIGS. 1 to 9.
  • the LOD configuration unit 53007 generates a Level of Detail (LOD).
  • LOD Level of Detail
  • the LOD is a degree representing the detail of the point cloud content, and a smaller LOD value indicates that the detail of the point cloud content decreases, and a larger LOD value indicates that the detail of the point cloud content is high. Points can be classified according to LOD.
  • points may be divided into LODs and grouped.
  • LOD generation process This is referred to as an LOD generation process, and a group having different LODs may be referred to as an LOD l set.
  • l represents the LOD and is an integer starting from 0.
  • LOD 0 is a set consisting of points with the largest distance between points, and as l increases, the distance between points belonging to LOD l decreases.
  • Example neighboring point set part (53 008) according to this LOD l set generated by the LOD part (53 007), equal to the LOD or less (that is, the distance between the nodes greater) based on LOD l set group X(>0) nearest neighbors can be found and registered as a set of neighboring points in a predictor.
  • X is the maximum number that can be set as a neighboring point and can be input as a user parameter.
  • a neighboring point of P3 belonging to LOD 1 is found in LOD 0 and LOD 1 .
  • the maximum number (X) that can be set as a neighboring point is 3
  • three neighboring nodes closest to P3 may be P2 P4 P6. These three nodes are registered as a set of neighboring points to the predictor of P3.
  • the neighboring node P4 is the closest to P3 based on distance, then P6, and then P2 as an embodiment.
  • all points of the point cloud data may each have a predictor.
  • the attribute information predictor 5309 predicts an attribute from neighboring points registered in the predictor.
  • the predictor of the node P3 calculates the weight based on the distance value of each neighboring point with (P2 P4 P6) as a set of neighboring points.
  • the weight of each neighboring point is Can be
  • the neighboring point set constructing unit 5308 or the attribute information predicting unit 5301 normalizes the weights of each neighboring point with the total sum of the weights of the neighboring points when the neighboring point set of the predictor is set. can do.
  • the attribute information predictor 5301 may predict attribute information through a predictor.
  • the average of the values obtained by multiplying the attributes (e.g., color, reflectance, etc.) of neighboring points registered in the predictor by a weight (or normalized weight) is set as the predicted result (i.e., predicted attribute value).
  • an attribute of a specific point may be set as a predicted result (ie, a predicted attribute value).
  • the predicted attribute value may be referred to as predicted attribute information.
  • the residual attribute value is the predicted attribute value of the point from the attribute value of the point (i.e., the original attribute value) (this is referred to as predicted attribute value or predicted attribute information). It can be found by subtracting.
  • a prediction mode ie, predictor index
  • a prediction mode is used in the same meaning as a predictor index (preindex), and may be broadly referred to as a prediction method.
  • the process of finding the most suitable prediction mode for each point and setting the found prediction mode to the predictor of the corresponding point is performed by the attribute information predictor 5301 as an embodiment.
  • a predicted attribute value calculated through a weighted average that is, an average value of a value obtained by multiplying the attributes of neighboring points set in the predictor of each point by a weight calculated based on the distance to each neighboring point
  • a prediction mode of the predictor of a corresponding point can be set by obtaining a residual attribute value at a predetermined time and selecting the smallest residual attribute value among the above residual attribute values.
  • a prediction mode having the smallest residual attribute value among residual attribute values obtained for each prediction mode may be selected as the prediction mode of the corresponding point.
  • the prediction method in which the attribute of the second neighboring point is determined as the predicted attribute value is selected as the prediction mode of the predictor of the corresponding point.
  • the neighboring point P4 of the neighboring points P2 P4 P6 of P3 is the closest to the corresponding point P3 based on distance
  • the neighboring point P6 and then the neighboring point P2 the first neighboring point is P4.
  • the second neighboring point may be P6, and the third neighboring point may be P2.
  • a value of a prediction mode that calculates a predicted attribute value through a weighted average is 0, a value of a prediction mode that determines an attribute of a first neighboring point as a predicted attribute value is 1, and an attribute of a second neighboring point is a predicted attribute value.
  • a value of the prediction mode determined as 2 may be assigned, and 3 may be assigned as a value of the prediction mode in which the attribute of the third neighboring point is set as the prediction attribute value.
  • the value of the prediction mode is 0, it may indicate that the attribute is predicted through the weighted average, if it is 1, the first neighboring node (i.e., neighboring point), if it is 2, the second neighboring node, and if it is 3, the third neighboring node.
  • the process of finding the most suitable prediction mode among the plurality of prediction modes and setting it as the prediction mode of the predictor of the corresponding point may be performed when a preset condition is satisfied. Therefore, when a predetermined condition is not satisfied, a prediction mode in which a prediction attribute value is calculated through a weighted average without performing a process of finding the most suitable prediction mode may be set as a prediction mode of the predictor of a corresponding point. As an embodiment, this process is performed for each point.
  • a difference value between attribute elements (eg, R, G, B) between neighboring points registered in the predictor of the corresponding point is a preset threshold. It may be larger, or if the difference between attribute elements (eg, R, G, B) between neighboring points registered in the predictor of the corresponding point is calculated, and the sum of the largest element values is greater than a preset threshold. May be. For example, it is assumed that the P3 point is the corresponding point, and P2 P4 P6 points are registered as neighboring points of the P3 point.
  • the difference values of R,G,B between points P2 and P4 are obtained, the difference values of R,G,B between points P2 and P6 are obtained, and R between points P4 and P6,
  • the difference in R is the largest between the points P2 and P4
  • the difference in G is the largest between the points P4 and P6
  • the difference in B is the largest between the points P2 and P6.
  • the R between points P2 and P4 of the largest R difference i.e. between P2 and P4
  • the largest G difference i.e. between P4 and P6
  • the largest B difference i.e. between P2 and P6. It is assumed that the difference is the largest.
  • the prediction mode can be signaled only when the largest R difference value is greater than a preset threshold value, or the sum of the largest R difference value, the largest G difference value, and the largest B difference value is greater than a preset threshold value. have. That is, the prediction mode may be signaled through the signaling processor 51005 only when a process of finding the most suitable prediction mode among the plurality of prediction modes is performed.
  • the transmitting side calculates a prediction attribute value based on a prediction mode set as a default (eg, prediction mode 0), and calculates a residual attribute value based on the difference between the original attribute value and the prediction attribute value.
  • the receiving side may obtain a prediction attribute value based on a prediction mode (eg, prediction mode 0) set as a default and restore the attribute value by adding it to the received residual attribute value.
  • the threshold value may be directly input or signaled in an attribute parameter set (eg, a lifting_adaptive_prediction_threshold field included in the APS).
  • an attribute parameter set eg, a lifting_adaptive_prediction_threshold field included in the APS.
  • the prediction mode selected for each point through the above-described process and the residual attribute value in the selected prediction mode are output to the residual attribute information quantization processor 5310.
  • the attribute information prediction unit 5301 configures a bitstream in which a prediction mode and a residual attribute value are used as one pair for each point as shown in FIG. 18(a), and then the residual attribute information quantization processing unit 53010 Can be printed as For example, the process of outputting the prediction mode of the point P1 and the residual attribute value to the residual attribute information quantization processing unit 5301, and then outputting the prediction mode and the residual attribute value of the P2 point to the residual attribute information quantization processing unit 5301 This also applies to the rest of the points.
  • the attribute information prediction unit 5301 separates the prediction mode and the residual attribute value for all points as shown in FIG. 18(b), and then separates the prediction mode bitstream composed of the separated prediction modes.
  • Each residual attribute value bitstream composed of the remaining attribute values may be output to the residual attribute information quantization processor 5310.
  • Two bitstreams may be output in parallel, or a bitstream including residual attribute values may be serially output after outputting a bitstream including prediction modes.
  • information about whether the prediction mode and the residual attribute value are separated may be signaled.
  • This information may be signaled to at least one of a sequence parameter set, an attribute parameter set, a tile parameter set, and an attribute slice header.
  • the separate prediction modes are additionally run-length. length) coding can be performed.
  • information on whether run-length coding is applied to the separated prediction modes eg, attribute_pred_residual_separate_encoding_flag
  • This information may be signaled to at least one of a sequence parameter set, an attribute parameter set, a tile parameter set, and an attribute slice header.
  • run-length coding when run-length coding is always applied to a bitstream of separated prediction modes, information on whether or not run-length coding of the prediction modes may be signaled.
  • the present specification includes information on whether the prediction mode and the residual attribute value are separated (eg, attribute_pred_residual_separate_encoding_flag) and information on whether run-length coding has been applied to the prediction modes (eg, attribute_pred_residual_separate_encoding_flag), and options related to prediction mode processing. It will be referred to as information.
  • the name of the above-described prediction mode processing related option information, information on whether the prediction mode and residual attribute values are separated, and information on whether run-length coding is applied to the prediction modes is within the scope of the meaning and function of the signaling information. Can be understood from.
  • each prediction mode of all transmitted points The value is 0, that is, there is a high probability that it is a prediction mode in which a predicted attribute value is calculated through a weighted average.
  • the preset threshold is set to 0. This is one embodiment, and the preset threshold may be set to a value greater than 0.
  • This specification describes the maximum value among the difference values between attribute elements (eg, R, G, B) between neighboring points registered as neighbors of a specific point or attribute elements between neighboring points registered as neighbors of a specific point (e.g., If the sum of the largest element values among the difference values of R, G, B) is greater than the preset threshold, the prediction mode and attribute values are separated from each point, and the prediction modes of all points are collected and the configured prediction mode bitstream is added. For example, zero run-length coding is applied.
  • attribute elements eg, R, G, B
  • 19 is a diagram illustrating an example of zero run-length coding according to embodiments.
  • the number of zeros in the list of prediction modes (selected predictor indexes, or prediction modes) may be counted in order through zerorun.
  • the first prediction mode is 1. Therefore, zerorun 0 is inserted into the coded prediction mode bitstream, and then 1 is inserted.
  • the next prediction mode is 0, and if the next value is 1 after 3 consecutive 0s appear, 3 counted through zerorun is inserted into the coded prediction mode bitstream, and 1 is then inserted.
  • the zero run-length coded prediction mode bitstream may be configured by putting zerorun 0 into the coded prediction mode bitstream and putting a value of 2 into the coded prediction mode bitstream. That is, taking FIG. 19 as an example, the size of the zero run-length coded prediction mode bitstream is reduced from '100012200000' to '013102025'.
  • the size of the attribute bitstream can be reduced by encoding through zero run-length coding as shown in FIG. 19.
  • the peak signal-to-noise ratio (PSNR) does not change, so attribute compression efficiency may be increased.
  • the residual attribute information quantization processing unit 53010 may include a bitstream in which a prediction mode and a residual attribute value are paired for each point as shown in FIG. 18(a) or FIG. 18(b) from the attribute information prediction unit 5301.
  • a prediction mode bitstream composed of prediction modes of all points and a residual attribute value bitstream composed of residual attribute values are input, and among them, quantization is performed on residual attribute values.
  • the prediction mode bitstream composed of prediction modes may be a prediction mode bitstream in which zero run-length coding has been performed, or a prediction mode bitstream in which zero run-length coding is not performed.
  • the residual attribute information quantization processing unit 5301 may be configured with respect to residual attribute values included in the input bitstream as shown in FIG. 18(a) or the residual attribute value bitstream input as shown in FIG. 18(b). Zero run-length coding may be applied to the included residual attribute values.
  • the residual attribute information quantization processing unit 5301 may basically apply zero run-length coding to residual attribute values included in the input residual attribute value bitstream as shown in FIG. 18(b), or , Zero run-length coding may be applied to the residual attribute values of FIG. 18(a) or FIG. 18(b) based on information on whether to separate the prediction mode and the residual attribute value (e.g., attribute_pred_residual_separate_encoding_flag).
  • zero run-length coding is performed on a bitstream composed of prediction modes of all points, and all points
  • quantization and zero run-length coding are performed on a bitstream composed of residual attribute values of.
  • the arithmetic coder 5301 applies arithmetic coding to the prediction modes and residual attribute values output from the residual attribute information quantization processor 5310 and outputs the result as an attribute bitstream.
  • encoding is first performed on the prediction modes of all the separated points, and then encoding the residual attribute values of all points. You can do it.
  • the encoding is zero run-length encoding.
  • the receiving device may first decode the prediction modes of all points, for example, when decoding a geometry by first decoding the prediction modes in another processor or a different thread, and can be used immediately when decoding a residual attribute value.
  • the geometry bitstream that is compressed and output by the geometry encoder 51006 and the attribute bitstream that is compressed and output by the attribute encoder 51007 are output to the transmission processing unit 5108.
  • the transmission processing unit 5108 may perform the same or similar operation and/or transmission method as the operation and/or transmission method of the transmission processing unit 12012 of FIG. 12, and the transmitter 10003 of FIG. 1 The same or similar operation and/or transmission method as the operation and/or transmission method may be performed.
  • FIG. 1 or 12 For a detailed description, reference will be made to the description of FIG. 1 or 12 and will be omitted here.
  • the transmission processing unit 5108 may include a geometry bitstream output from the geometry encoder 51006, an attribute bitstream output from the attribute encoder 51007, and a signaling bitstream output from the signaling processing unit 51005. Each may be transmitted or may be multiplexed into one bitstream and transmitted.
  • the transmission processor 5108 may encapsulate the bitstream into a file or segment (eg, a streaming segment) and then transmit it through various networks such as a broadcasting network and/or a broadband network.
  • a file or segment eg, a streaming segment
  • various networks such as a broadcasting network and/or a broadband network.
  • the signaling processing unit 51005 may generate and/or process signaling information and output the generated and/or processed signaling information to the transmission processing unit 5108 in the form of a bitstream.
  • the signaling information generated and/or processed by the signaling processing unit 51005 will be provided to the geometry encoder 51006, the attribute encoder 51007, and/or the transmission processing unit 5108 for geometry encoding, attribute encoding, and transmission processing.
  • the signaling processing unit 51005 may be provided with signaling information generated by the geometry encoder 51006, the attribute encoder 51007, and/or the transmission processing unit 5108.
  • signaling information may be signaled and transmitted in a parameter set (SPS: sequence parameter set, GPS: geometry parameter set, APS: attribute parameter set, TPS: Tile parameter set, etc.). Also, such as slices or tiles, signals may be transmitted in units of coding units of each image.
  • signaling information may include metadata (eg, a setting value, etc.) regarding point cloud data, and a geometry encoder 51006 and an attribute encoder 51007 for geometry encoding, attribute encoding, and transmission processing, And/or it may be provided to the transmission processing unit (51008).
  • the signaling information is file format, dynamic adaptive streaming over HTTP (DASH), MPEG media transport (MMT), etc., or high definition multimedia interface (HDMI), Display Port, Video Electronics Standards Association (VESA), CTA, etc. It can also be defined at the wired interface level of.
  • DASH dynamic adaptive streaming over HTTP
  • MMT MPEG media transport
  • HDMI high definition multimedia interface
  • VESA Video Electronics Standards Association
  • CTA CTA
  • a method/apparatus according to the embodiments may signal related information to add/perform an operation of the embodiments.
  • Signaling information according to embodiments may be used in a transmitting device and/or a receiving device.
  • option information related to prediction mode processing for example, information on whether the prediction mode and the residual attribute value are separated (e.g., attribute_pred_residual_separate_encoding_flag) and information on whether run-length coding is applied to the separated prediction modes ( Yes, attribute_pred_residual_separate_encoding_flag) is signaled to at least one of a sequence parameter set, an attribute parameter set, a tile parameter set, and an attribute slice header.
  • attribute_pred_residual_separate_encoding_flag information on whether the prediction mode and the residual attribute value are separated
  • the LOD l set is generated, and the nearest neighbor points are searched based on the LOD l set to the predictor. Is registered, and the process of normalizing by calculating the weight based on the distance value from each neighboring point is performed in the same or similar manner. Then, the received prediction mode is decoded, and an attribute value of a corresponding point is predicted according to the decoded prediction mode. In addition, after decoding the received residual attribute value, the attribute value of the corresponding point may be restored by adding the predicted attribute value.
  • the attribute encoder of the transmitting device separates the prediction mode and the residual attribute value from all points, configures the prediction mode bitstream with the separated prediction modes, and the residual attribute value bitstream with the separated residual attribute values. By configuring, it is possible to improve decoding efficiency when restoring an attribute value in the attribute decoder of the receiving device.
  • FIG. 20 is a diagram illustrating another example of an apparatus for receiving a point cloud according to embodiments.
  • the point cloud receiving apparatus may include a reception processing unit 61001, a signaling processing unit 61022, a geometry decoder 6103, an attribute decoder 6104, and a post-processor 6101. .
  • the geometry decoder 6103 and the attribute decoder 6104 may be referred to as point cloud video decoders.
  • 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, or the like.
  • the reception processor 61001 may receive one bitstream, or may receive a geometry bitstream, an attribute bitstream, and a signaling bitstream, respectively.
  • the reception processing unit 61001 may decapsulate the received file and/or segment and output it as a bitstream.
  • the reception processing unit 61001 When one bitstream is received (or decapsulated), the reception processing unit 61001 according to the embodiments demultiplexes a geometry bitstream, an attribute bitstream, and/or a signaling bitstream from one bitstream, and demultiplexes the The multiplexed signaling bitstream is output to the signaling processing unit 61022, the geometry bitstream is output to the geometry decoder 6103, and the attribute bitstream is output to the attribute decoder 61044.
  • the reception processing unit 61001 receives (or decapsulates) a geometry bitstream, an attribute bitstream, and/or a signaling bitstream, respectively, the signaling bitstream to the signaling processing unit 61022, and the geometry bitstream.
  • the s[deg.]m may be transmitted to the geometry decoder 6103, and the attribute bitstream may be delivered to the attribute decoder 6104.
  • the signaling processing unit 61022 parses and processes signaling information, e.g., SPS, GPS, APS, TPS, metadata, and the like from the input signaling bitstream, to provide a geometry decoder 6103, an attribute decoder 6104, and It may be provided to the post-processing unit 610105.
  • signaling information included in the geometry slice header and/or the attribute slice header may also be parsed in advance by the signaling processor 6102 before decoding the corresponding slice data. That is, if the point cloud data at the transmitting side is divided into tiles and/or slices as shown in FIG. 16, since the TPS includes the number of slices included in each tile, the point cloud video decoder according to the embodiments is The number of can be checked, and information for parallel decoding can be quickly parsed.
  • the point cloud video decoder can quickly parse a bitstream including point cloud data by receiving an SPS having a reduced amount of data.
  • the receiving device may perform decoding of a corresponding tile as soon as it receives the tiles, and may maximize decoding efficiency by performing decoding for each slice based on GPS and APS included in the tile for each tile.
  • the geometry decoder 6103 may restore the geometry by performing the reverse process of the geometry encoder 51006 of FIG. 15 based on signaling information (eg, geometry related parameters) for the compressed geometry bitstream.
  • the geometry reconstructed (or reconstructed) by the geometry decoder 6103 is provided to the attribute decoder 61044.
  • the attribute decoder 6204 may restore the attribute by performing the reverse process of the attribute encoder 51007 of FIG. 15 based on signaling information (e.g., attribute related parameters) and reconstructed geometry for the compressed attribute bitstream. have.
  • the geometry decoder 6103 and the attribute decoder 6104 decodes geometry and attributes in units of tiles and/or slices. Decoding can be performed.
  • 21 is a detailed block diagram showing another example of a geometry decoder 6103 and an attribute decoder 6104 according to embodiments.
  • the arithmetic decoder 63001, the octree reconstruction unit 63002, the geometry information prediction unit 63003, the inverse quantization processing unit 63004, and the coordinate system inverse transform unit 63005 included in the geometry decoder 6103 of FIG. 21 are shown in FIG. It is also possible to perform some or all of the operations of the arithmetic decoder 11000, the octree synthesis unit 11001, the surface opoxidation synthesis unit 11002, the geometry reconstruction unit 11003, and the coordinate system inverse transform unit 11004 of Alternatively, some or all of the operations of the arithmetic decoder 13002 of FIG.
  • the ocupancy code-based octree reconstruction processing unit 13003, the surface model processing unit 13004, and the inverse quantization processing unit 13005 may be performed.
  • the positions restored by the geometry decoder 6103 are output to a post-process unit 6101.
  • information on whether to separate a prediction mode and a residual attribute value in at least one of a sequence parameter set (SPS), an attribute parameter set (APS), a tile parameter set (TPS), and an attribute slice header e.g., attribute_pred_residual_separate_encoding_flag
  • attribute_pred_residual_separate_encoding_flag information about whether run-length coding has been applied to the separate prediction modes
  • attribute_pred_residual_separate_encoding_flag is signaled, it is obtained from the signaling processing unit 6102 and provided to the attribute decoder 6104, or the attribute It can also be obtained directly from the decoder (61004).
  • a prediction mode decoding unit 6301 may include a residual attribute information inverse quantization processing unit 6302, and a color inverse transformation processing unit 6301.
  • the arithmetic decoder 63006 may arithmetically decode the input attribute bitstream.
  • the arithmetic decoder 63006 may decode the attribute bitstream based on the reconstructed geometry.
  • the Arismatic decoder 63006 performs the same or similar operation and/or decoding as the operation and/or decoding of the Arismatic decoder 11005 of FIG. 11 or the Arismatic decoder 13007 of FIG. 13.
  • the attribute bitstream output from the arithmetic decoder 63006 is decoded by combining any one or two or more of RAHT decoding, LOD-based predictive transform decoding method, and lifting transform decoding method based on reconstructed geometry information. Can be.
  • the transmission device performs attribute compression by combining any one or both of the LOD-based predictive transform coding technique and the lifting transform coding technique
  • the LOD-based predictive transform decoding technique also in the receiving device It will be described as an embodiment of performing attribute decoding by combining any one or both of and lifting transform decoding techniques. Therefore, the description of the RAHT decoding technique in the receiving device will be omitted.
  • an attribute bitstream that is Arismatically decoded by the Arismatic decoder 63006 is provided to the LOD configuration unit 63007.
  • an attribute bitstream provided from the arithmetic decoder 63006 to the LOD constructing unit 63007 may include prediction modes and residual attribute values.
  • the LOD configuration unit 63007 generates the LOD in the same or similar manner as the LOD configuration unit 53007 of the transmission device and outputs the generated LOD to the neighboring point set configuration unit 63008.
  • the LOD configuration unit 63007 divides and groups points into LODs. At this time, a group having different LODs is referred to as an LOD l set.
  • LOD l represents the LOD and is an integer starting from 0.
  • LOD 0 is a set consisting of points with the largest distance between points, and as l increases, the distance between points belonging to LOD l decreases.
  • prediction modes and residual attribute values encoded by the transmission device may exist for each LOD or may exist only for a leaf node.
  • the neighboring point set constructing unit 63008 when the LOD l set is generated by the LOD constructing unit 63007, the neighboring point set constructing unit 63008 has the same or smaller LOD (that is, the distance between nodes is large) based on the LOD l set. It is possible to find X (>0) nearest neighbors in the group and register them as a set of neighboring points in a predictor.
  • the X number may be input as a user parameter as the maximum number that can be set as a neighboring point, or may be included in signaling information such as SPS, APS, GPS, TPS, geometry slice header, and attribute slice header and received.
  • the neighboring point set configuration unit 63008 may select neighboring points of the corresponding point for each point based on signaling information such as SPS, APS, GPS, TPS, geometry slice header, and attribute slice header. To this end, the neighbor point set configuration unit 63008 may receive corresponding information from the signaling processing unit 6102.
  • signaling information such as SPS, APS, GPS, TPS, geometry slice header, and attribute slice header.
  • P2 P4 P6 points may be selected as neighboring points of a P3 point (ie, a node) belonging to LOD 1 and registered as a neighboring point set in a predictor of P3.
  • the attribute information predictor 6301 performs a process of predicting an attribute from at least one neighboring point registered in a predictor of a corresponding point based on a prediction mode of a specific point.
  • the attribute prediction process is performed on all points or at least some points of the reconstructed geometry.
  • the attribute information prediction unit 6301 may receive a prediction mode of each point from the prediction mode determination unit 6301 or the prediction mode decoding unit 6301 to predict the attribute value of each point.
  • the attribute information predictor 6301 may predict an attribute value of each point based on a prediction mode set as a default (eg, prediction mode 0).
  • the average of the values obtained by multiplying the attributes of P2 P4 P6, which are neighboring points registered in the predictor of P3, by a weight (or normalized weight) is calculated, and the average value is calculated as P3. It can be determined by the predicted attribute value of the point.
  • the attribute value of the neighboring point P4 registered in the predictor of P3 may be determined as the predicted attribute value of the point P3.
  • the attribute value of the neighboring point P6 registered in the predictor of P3 may be determined as the predicted attribute value of the point P3.
  • the attribute value of the neighboring point P2 registered in the predictor of P3 may be determined as the predicted attribute value of the point P3.
  • the residual attribute information inverse quantization processing unit (63012) determines the attribute information in the residual attribute value of each received point. After reconstructing the attribute value of the corresponding point by adding the predicted attribute value of the corresponding point predicted by the prediction unit 6301, inverse quantization is performed in the reverse of the quantization process of the transmitting device.
  • the prediction mode of each point to predict the attribute value of each point may be a value predetermined by an appointment of the transmitting/receiving side, or may be signaled and received in signaling information such as SPS, APS, TPS, and attribute slice headers. have. According to an embodiment, the prediction mode determined in advance by an appointment of the transmitting/receiving side is 0.
  • the prediction mode may be signaled and received in signaling information such as SPS, APS, TPS, and/or attribute slice header.
  • the prediction mode of each point may be received in a bitstream that is paired with the residual attribute value of the corresponding point, or all points
  • the separated prediction modes and the separated residual attribute values may be independently configured and received as a bitstream.
  • the bitstream of the separated prediction modes may be received by applying zero run-length coding in the transmission device.
  • residual attribute values may be received by applying quantization and zero run-length coding in a transmitting device.
  • the prediction mode determination unit 6301 includes information signaled to at least one of the SPS, APS, TPS, and attribute slice headers, that is, information on whether to separate the prediction mode and the residual attribute value (e.g., attribute_pred_residual_separate_encoding_flag). Based on information on whether run-length coding is applied to the prediction modes separated from and (e.g., attribute_pred_residual_separate_encoding_flag), whether the prediction mode and the residual attribute value of each point are separated, and the prediction modes of all points are zero. It can be determined whether run-length coding has been applied.
  • the prediction mode determination unit 6301 may include signaling information parsed from at least one of the SPS, APS, TPS, and attribute slice headers from the signaling processing unit 61022, for example, information on whether to separate a prediction mode and a residual attribute value.
  • attribute_pred_residual_separate_encoding_flag and information on whether run-length coding is applied to separate prediction modes (eg, attribute_pred_residual_separate_encoding_flag) is provided as an embodiment.
  • the prediction mode determination unit 6301 may receive an arithmetically decoded prediction mode of each point through the LOD configuration unit 63007.
  • the prediction mode of each point may be provided as a pair with the residual attribute value of the corresponding point, or prediction modes of all points may be provided separately from the residual attribute values.
  • zero run length coding may be applied to each of the separated residual attribute values of all points and the separated prediction modes at the transmitting side.
  • the prediction mode determination unit 6301 determines that the prediction mode and the residual attribute value of each point are separated based on the signaling information, and that zero run-length coding is applied to the prediction modes of the points. Then, the prediction mode decoding unit 6301 performs zero run-length decoding on the prediction modes of the points in the reverse process of FIG. 18(b) and outputs them to the attribute information prediction unit 6301.
  • the prediction mode decoding unit 6301 performs zero run-length decoding on prediction modes of points, and provides the zero run-length decoded prediction modes to the attribute information prediction unit 6301. do.
  • the attribute information prediction unit 6301 predicts attribute values of points based on prediction modes of points provided from the prediction mode decoding unit 6301. For example, if the prediction mode of the P3 point is 1, among the P2 P4 P6 points registered as neighboring points of the P3 point, the attribute value of the P4 point closest to the P3 point based on the distance becomes the predicted attribute value of the P3 point. This process is applied to each point to be predicted to obtain a predicted attribute value of each point.
  • the predicted attribute value of each point obtained by the attribute information predicting unit 6301 is provided to the residual attribute information inverse quantization processing unit 6302.
  • the residual attribute values of the points transmitted from the transmitting side and which have been arithmetically decoded are the residual attribute values through one of the attribute information predictor (63009), the prediction mode determination unit (63010), and the prediction mode decoding unit (63011).
  • the attribute information may be provided to the inverse quantization processing unit 6302.
  • the residual attribute information inverse quantization processing unit 6302 performs inverse quantization of the input residual attribute values in an inverse process of the transmitting side, and then adds a predicted attribute value of the corresponding point to the residual attribute value of each inverse quantized point. To restore the attribute value of each point.
  • the residual attribute information inverse quantization processing unit 6302 performs zero run-length decoding on the residual attribute values of points. After that, inverse quantization is performed.
  • the residual attribute value of the corresponding point is calculated based on the prediction mode of each point that has been arithmetically decoded. By performing the addition process, the attribute value of each point is restored.
  • the attribute values restored by the residual attribute information inverse quantization processing unit 6302 through the above-described process are output to the color inverse transformation processing unit 6301.
  • the inverse color transformation processing unit 6301 performs inverse transformation coding for inverse transformation of the color values (or textures) included in the reconstructed attribute values, and outputs the attributes to the post processing unit 6101.
  • the inverse color transform processing unit 6301 performs the same or similar operation and/or inverse transform coding as the operation and/or inverse transform coding of the inverse color transform unit 11010 of FIG. 11 or the inverse color transform processor 13010 of FIG. 13.
  • the post-processing unit 6101 05 may reconstruct the point cloud data by matching positions restored and output from the geometry decoder 6103 with attributes restored and output from the attribute decoder 6104.
  • the post-processing unit 6101 may perform a reverse process of spatial division of the transmitting side based on the signaling information. For example, if the bounding box as shown in FIG. 16(a) is divided into tiles and slices as shown in FIGS. 16(b) and 16(c), tiles and/or slices are combined based on signaling information. As shown in Fig. 16(a), the bounding box can be restored.
  • FIG. 22 shows an example of a bitstream structure of point cloud data for transmission/reception according to embodiments.
  • the bitstream may include one or more sub-bitstreams.
  • the bitstream according to the embodiments is a sequence parameter set (SPS) for signaling of a sequence level, a geometry parameter set (GPS) for signaling of geometry information coding, at least one attribute parameter set (APS) for signaling of attribute information coding, APS 0 , APS 1 ), TPS (Tile Parameter Set) for signaling of a tile level, and one or more slices (slice 0 to slice n) may be included.
  • SPS sequence parameter set
  • GPS geometry parameter set
  • APS attribute parameter set
  • TPS TPS
  • slices slice 0 to slice n
  • a bitstream of point cloud data 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 may include information about each tile (eg, coordinate value information of a bounding box and height/size information) with respect to one or more tiles.
  • Each slice may include one geometry bitstream (Geom0) and one or more attribute bitstreams (Attr0, Attr1).
  • the first slice (slice 0) may include one geometry bitstream (Geom0 0 ) and one or more attribute bitstreams (Attr0 0 , Attr1 0 ).
  • a geometry bitstream (or referred to as a geometry slice) within each slice may be composed of a geometry slice header (geom_slice_header) and geometry slice data (geom_slice_data).
  • the geometry slice header (geom_slice_header) according to the embodiments is included in the identification information (geom_parameter_set_id), the tile identifier (geom_tile_id), the slice identifier (geom_slice_id), and the geometry slice data (geom_slice_data) of the parameter set included in the geometry parameter set (GPS).
  • geomBoxOrigin is the geometry box origin information indicating the box origin of the geometry slice data
  • geom_box_log2_scale is information indicating the log scale of the geometry slice data
  • geom_max_node_size_log2 is information indicating the size of the root geometry octree node
  • geom_num_points is the geometry slice data. This is information related to the number of points of.
  • the geometry slice data (geom_slice_data) may include geometry information (or geometry data) of point cloud data within a corresponding slice.
  • Each attribute bitstream (or attribute slice) in each slice may include an attribute slice header (attr_slice_header) and attribute slice data (attr_slice_data).
  • the attribute slice header (attr_slice_header) may include information on the attribute slice data, and the attribute slice data includes attribute information (or attribute data or attribute value) of point cloud data in the slice. can do.
  • each may include different attribute information. For example, one attribute bitstream may include attribute information corresponding to color, and the other attribute stream may include attribute information corresponding to reflectance.
  • FIG. 23 shows an example of a bitstream structure of point cloud data according to embodiments.
  • FIG. 24 illustrates a connection relationship between components in a bitstream of point cloud data according to embodiments.
  • the bitstream structure of the point cloud data shown in FIGS. 23 and 24 may mean the bitstream structure of the point cloud data shown in FIG. 22.
  • the SPS includes an identifier (seq_parameter_set_id) for identifying the corresponding SPS
  • the GPS includes an identifier (geom_parameter_set_id) for identifying the corresponding GPS and an identifier indicating an active SPS (Active SPS) to which the GPS belongs (seq_parameter_set_id).
  • the APS may include an identifier (attr_parameter_set_id) for identifying a corresponding APS and an identifier (seq_parameter_set_id) indicating an active SPS (Active SPS) to which the APS belongs.
  • a geometry bitstream (or geometry slice) includes 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 in the geometry slice.
  • the geometry slice header may further include an identifier (geom_slice_id) for identifying a corresponding geometry slice and/or an identifier (geom_tile_id) for identifying a corresponding tile.
  • the geometry slice data may include geometry information belonging to a corresponding slice.
  • the attribute bitstream (or attribute slice) includes an attribute slice header and attribute slice data
  • the attribute slice header is an identifier (attr_parameter_set_id) of the active APS to be referred to in the attribute slice and geometry related to the attribute slice. It may include an identifier (geom_slice_id) for identifying the slice.
  • the attribute slice data may include attribute information belonging to a corresponding slice.
  • the geometry slice refers to the GPS
  • the GPS refers to the SPS.
  • the SPS lists the available attributes, assigns an identifier to each, and identifies a decoding method.
  • the attribute slice is mapped to output attributes according to the identifier, and the attribute slice itself has a dependency on the preceding (decoded) geometry slice and the APS.
  • APS refers to SPS.
  • parameters necessary for encoding the point cloud data may be newly defined in a parameter set and/or a corresponding slice header of the point cloud data.
  • attribute information may be added to an attribute parameter set (APS) when encoding attribute information, and to a tile and/or slice header when performing tile-based encoding.
  • APS attribute parameter set
  • a bitstream of point cloud data provides a tile or slice so that the point cloud data can be divided and processed by regions.
  • Each region of a bitstream according to embodiments may have a different importance. Therefore, when the point cloud data is divided into tiles, different filters (encoding methods) and different filter units may be applied for each tile. In addition, when the point cloud data is divided into slices, different filters and different filter units may be applied for each slice.
  • the transmitting device and the receiving device may transmit and receive a bitstream in a high-level syntax structure for selective transmission of attribute information in the divided region.
  • the transmission apparatus transmits point cloud data according to the structure of the bitstream as shown in FIGS. 22 to 24, so that different encoding operations can be applied according to importance, and an encoding method having good quality. It can provide a method that can be used in important areas. In addition, it supports efficient encoding and transmission according to the characteristics of point cloud data and can provide attribute values according to user requirements.
  • the receiving device receives the point cloud data according to the structure of the bitstream as shown in FIGS. 22 to 24, and thus a complex decoding (filtering) method for the entire point cloud data according to the processing capacity of the receiving device.
  • a complex decoding (filtering) method for the entire point cloud data according to the processing capacity of the receiving device.
  • different filtering (decoding methods) can be applied for each area (area divided into tiles or divided into slices). Therefore, it is possible to ensure better image quality in an area important to the user and an appropriate latency for the system.
  • a tile or a slice is provided to divide the point cloud data by region and process it. And, when dividing point cloud data by area, by setting an option to create a different set of neighboring points for each area, the complexity is low but the reliability is slightly lower, or conversely, the complexity is high but the reliability is high. have.
  • At least one of the SPS, APS, TPS, and attribute slice headers for each slice may include offset information related to prediction mode processing.
  • the prediction mode processing-related offset information includes information on whether a prediction mode and a residual attribute value are separated (e.g., attribute_pred_residual_separate_encoding_flag) and information on whether run-length coding is applied to the separated prediction modes. (Eg, attribute_pred_residual_separate_encoding_flag) may be included.
  • attribute slice data may be changed and signaled according to offset information related to prediction mode processing.
  • a field which is a term used in the syntaxes of the present specification described later, may have the same meaning as a parameter or element.
  • SPS sequence parameter set
  • the SPS may include sequence information of a point cloud data bitstream, and in particular, an example including offset information related to prediction mode processing is shown.
  • SPS may include a profile_idc field, a profile_compatibility_flags field, a level_idc field, a sps_bounding_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.
  • the profile_idc field represents a profile that the bitstream conforms to.
  • profile_compatibility_flags field When the value of the profile_compatibility_flags field is 1, it may indicate that the bitstream conforms to the profile indicated by profile_idc.
  • the level_idc field indicates a level to which the bitstream follows.
  • the sps_bounding_box_present_flag field indicates whether source bounding box information is signaled to the SPS.
  • the source bounding box information may include source bounding box offset and size information. For example, if the value of the sps_bounding_box_present_flag field is 1, it indicates that source bounding box information is signaled to the SPS, and if it is 0, it is not signaled.
  • the sps_source_scale_factor field indicates the scale factor of the source point cloud.
  • the sps_seq_parameter_set_id field provides an identifier for the SPS for reference by other syntax elements.
  • the sps_num_attribute_sets field indicates the number of coded attributes in the bitstream (indicates the number of coded attributes in the bitstream).
  • the sps_extension_present_flag field indicates whether the sps_extension_data syntax structure exists in the corresponding SPS syntax structure. For example, if the value of the sps_extension_present_flag field is 1, the sps_extension_data syntax structure exists in this SPS syntax structure, and if it is 0, it indicates that the sps_extension_data syntax structure is present in the SPS syntax structure. When not present, the value of the sps_extension_present_flag field is inferred to be equal to 0).
  • the SPS may include a sps_bounding_box_offset_x field, sps_bounding_box_offset_y field, sps_bounding_box_offset_z field, sps_bounding_box_scale_factor field, sps_bounding_box_scale_factor field, sps_bounding_heights_size_box_size field, sps_bounding_box_size_box_size field, and sps_bounding_box_size_box_size field.
  • the sps_bounding_box_offset_x field represents the x offset of the source bounding box in Cartesian coordinates. If there is no x offset of the source bounding box, the value of the sps_bounding_box_offset_x field is 0.
  • the sps_bounding_box_offset_y field represents a y offset of a source bounding box in a Cartesian coordinate system. If there is no y offset of the source bounding box, the value of the sps_bounding_box_offset_y field is 0.
  • the sps_bounding_box_offset_z field represents a z offset of a source bounding box in a Cartesian coordinate system. If there is no z offset of the source bounding box, the value of the sps_bounding_box_offset_z field is 0.
  • the sps_bounding_box_scale_factor field represents a scale factor of a source bounding box in a Cartesian coordinate system. If the scale factor of the source bounding box does not exist, the value of the sps_bounding_box_scale_factor field may be 1.
  • the sps_bounding_box_size_width field represents the width of a source bounding box in a Cartesian coordinate system. If the width of the source bounding box does not exist, the value of the sps_bounding_box_size_width field may be 1.
  • the sps_bounding_box_size_height field represents the height of the source bounding box in a Cartesian coordinate system. If the height of the source bounding box does not exist, the value of the sps_bounding_box_size_height field may be 1.
  • the sps_bounding_box_size_depth field represents the depth of a source bounding box in a Cartesian coordinate system. If the depth of the source bounding box does not exist, the value of the sps_bounding_box_size_depth field may be 1.
  • the SPS includes a repetition sentence repeated by the value of the sps_num_attribute_sets field.
  • i is initialized to 0, increases by 1 each time the loop is executed, and the loop is repeated until the value of i becomes the value of the sps_num_attribute_sets field.
  • This loop includes attribute_dimension[i] field, attribute_instance_id[i] field, attribute_bitdepth[i] field, attribute_cicp_colour_primaries[i] field, attribute_cicp_transfer_characteristics[i] field, attribute_cicp_matrix_coeffs[i] field, attribute_cicp_video_label_full_range_flag field, and known_cicp_video_full_flag_flag field. May contain fields.
  • the attribute_dimension[i] field specifies the number of components of the i-th attribute.
  • the attribute_instance_id[i] field represents the instance identifier of the i-th attribute.
  • the attribute_bitdepth[i] field indicates the bitdepth of the i-th attribute signal(s) (specifies the bitdepth of the i-th attribute signal(s)).
  • the attribute_cicp_colour_primaries[i] field represents chromaticity coordinates of the color attribute source primary of the i-th attribute.
  • the attribute_cicp_transfer_characteristics[i] field is a reference opto-electronic transfer characteristic as a source input linear optical intensity having a nominal real-valued range of 0 to 1 of the i-th attribute. function) or represents the inverse of the reference opto-electronic transfer characteristic function as a function of output linear optical intensity. (either indicates the reference opto-electronic transfer characteristic function of the color attribute as a function of a source input linear optical intensity with a nominal real-valued range of 0 to 1 or indicates the inverse of the reference electro-optical transfer characteristic function as a function of an output linear optical intensity.)
  • the attribute_cicp_matrix_coeffs[i] field describes a matrix coefficient used to derive luma and chroma signals from green, blue, and red (or three primary colors of Y, Z, and X) of the i-th attribute. (describes the matrix coefficients used in deriving luma and chroma signals from the green, blue, and red, or Y, Z, and X primaries.)
  • the attribute_cicp_video_full_range_flag[i] field is a black level, luma, and saturation signal derived from E'Y, E'PB and E'PR or E'R, E'G and E'B real-value component signals of the i-th attribute. Indicates the range of. (specifies indicates the black level and range of the luma and chroma signals as derived from E'Y, E'PB, and E'PR or E'R, E'G real-valued component signals)
  • the known_attribute_label[i] field indicates whether a know_attribute_label field or an attribute_label_four_bytes field is signaled for the i-th attribute. For example, if the value of the known_attribute_label_flag[i] field is 1, the know_attribute_label field is signaled for the i-th attribute, and if the value of the known_attribute_label_flag[i] field is 1, it indicates that the attribute_label_four_bytes field is signaled for the i-th attribute. .
  • the known_attribute_label[i] field represents an attribute type. For example, if the value of the known_attribute_label[i] field is 0, it indicates that the ith attribute is a color, and if the value of the known_attribute_label[i] field is 1, it indicates that the ith attribute is reflectance, and the known_attribute_label[i] field When the value of is 1, it may indicate that the i-th attribute is a frame index.
  • the attribute_label_four_bytes field indicates a known attribute type in a 4-byte code.
  • a color may be indicated, and a value of 1 may indicate a reflectance.
  • SPS may further include a sps_extension_data_flag field when the value of the sps_extension_present_flag field is 1.
  • the sps_extension_data_flag field may have any value.
  • the SPS may further include offset information related to prediction mode processing.
  • the prediction mode processing-related offset information includes information on whether a prediction mode and a residual attribute value are separated (e.g., attribute_pred_residual_separate_encoding_flag) and information on whether run-length coding is applied to the separated prediction modes. (Eg, attribute_pred_residual_separate_encoding_flag) may be included.
  • the prediction mode processing-related offset information may be included in a repetition statement repeated by a value of the sps_num_attribute_sets field described above.
  • the repetition statement may further include an attribute_pred_residual_separate_encoding_flag[i] field.
  • the attribute_pred_residual_separate_encoding_flag[i] field indicates whether a prediction mode and a residual attribute value are separated. For example, if the value of the attribute_pred_residual_separate_encoding_flag[i] field is 1, it indicates that the prediction mode and the residual attribute value are separated, and if it is 0, it indicates that the prediction mode and the residual attribute value are not separated.
  • the repeat statement may further include an attribute_pred_mode_zero_run_length_coding_flag[i] field.
  • the attribute_pred_mode_zero_run_length_coding_flag[i] field indicates whether run-length coding is applied to prediction modes. For example, if the value of the attribute_pred_mode_zero_run_length_coding_flag[i] field is 1, it indicates that run-length coding is applied to prediction modes, and if it is 0, it indicates that run-length coding is not applied to the prediction modes.
  • attribute_pred_residual_separate_encoding_flag field and the attribute_pred_mode_zero_run_length_coding_flag field may be signaled to the SPS.
  • the GPS is a diagram showing an embodiment of a syntax structure of a geometry parameter set (geometry_parameter_set()) (GPS) according to the present specification.
  • the GPS may include information on a method of encoding geometry information of point cloud data included in one or more slices.
  • GPS may include a field gps_geom_parameter_set_id, gps_seq_parameter_set_id field, gps_box_present_flag field, unique_geometry_points_flag field, neighbour_context_restriction_flag field, inferred_direct_coding_mode_enabled_flag field, bitwise_occupancy_coding_flag field, adjacent_child_contextualization_enabled_flag field, log2_neighbour_avail_boundary field, log2_intra_pred_max_node_size field, log2_trisoup_node_size field, and gps_extension_present_flag field.
  • the gps_geom_parameter_set_id field provides an identifier of the GPS referenced by other syntax elements (provides an identifier for the GPS for reference by other syntax elements).
  • the gps_seq_parameter_set_id field indicates a value of the seq_parameter_set_id field for a corresponding active SPS (specifies the value of sps_seq_parameter_set_id for the active SPS).
  • the gps_box_present_flag field indicates whether additional bounding box information is provided in a geometry slice header referring to a current GPS. For example, if the value of the gps_box_present_flag field is 1, it may indicate that additional bounding box information is provided in a geometry header referring to the current GPS. Therefore, if the value of the gps_box_present_flag field is 1, the GPS may further include a gps_gsh_box_log2_scale_present_flag field.
  • the gps_gsh_box_log2_scale_present_flag field indicates whether the gps_gsh_box_log2_scale field is signaled in each geometry slice header referring to the current GPS. For example, if the value of the gps_gsh_box_log2_scale_present_flag field is 1, it may indicate that the gps_gsh_box_log2_scale field is signaled to each geometry slice header referring to the current GPS.
  • the value of the gps_gsh_box_log2_scale_present_flag field is 0, it indicates that the gps_gsh_box_log2_scale field is not signaled in each geometry slice header referring to the current GPS, and a common scale for all slices is signaled in the gps_gsh_box_log2_scale field of the current GPS. can do.
  • the GPS may further include a gps_gsh_box_log2_scale field.
  • the gps_gsh_box_log2_scale field represents a common scale factor of a bounding box origin for all slices referring to the current GPS.
  • the unique_geometry_points_flag field indicates whether all output points have unique positions. For example, if the value of the unique_geometry_points_flag field is 1, it indicates that all output points have unique positions. If the value of the unique_geometry_points_flag field is 0, it indicates that two or more output points can have the same positions (equal to 1 indicates that all output points have unique positions.unique_geometry_points_flag field equal to 0 indicates that the output points may have same positions).
  • the neighbor_context_restriction_flag field represents contexts used for octree occupancy coding. For example, if the value of the neighbour_context_restriction_flag field is 0, it indicates that octree occupancy coding uses contexts determined based on six neighboring parent nodes. If the value of the neighbor_context_restriction_flag field is 1, it indicates that octree occupancy coding uses contexts determined based only on sibling nodes (equal to 0 indicates that octree occupancy coding uses contexts determined from six neighboring parent nodes.neighbour_context_restriction_flag field) equal to 1 indicates that octree occupancy coding uses contexts determined from sibling nodes only.).
  • the inferred_direct_coding_mode_enabled_flag field indicates whether the direct_mode_flag field exists in the corresponding geometry node syntax. For example, if the value of the inferred_direct_coding_mode_enabled_flag field is 1, it indicates that the direct_mode_flag field exists in the corresponding geometry node syntax. For example, if the value of the inferred_direct_coding_mode_enabled_flag field is 0, it indicates that the direct_mode_flag field does not exist in the corresponding geometry node syntax.
  • the bitwise_occupancy_coding_flag field indicates whether the geometry node occupancy is encoded using bitwise contextualization of the syntax element occupancy map. For example, if the value of the bitwise_occupancy_coding_flag field is 1, it indicates that the geometry node occupancy is encoded using bitwise contextualization of the syntax element occupancy_map. For example, if the value of the bitwise_occupancy_coding_flag field is 0, it indicates that the geometry node occupancy is encoded using the directory-encoded syntax element occupancy_byte.
  • the adjacent_child_contextualization_enabled_flag field indicates whether adjacent children of neighboring octree nodes are used for bitwise occupancy contextualization. For example, if the value of the adjacent_child_contextualization_enabled_flag field is 1, it indicates that adjacent children of neighboring octree nodes are used for bitwise occupancy contextualization. For example, if the value of the adjacent_child_contextualization_enabled_flag field is 0, it indicates that children of neighboring octree nodes are not used for bitwise occupancy contextualization.
  • the log2_neighbour_avail_boundary field specifies the value of the variable NeighbAvailBoundary that is used in the decoding process as follows: ).
  • NeighbAvailBoundary 2 log2_neighbour_avail_boundary
  • the NeighbAvailabilityMask may be set to 1. For example, if the value of the neighbour_context_restriction_flag field is 0, the NeighbAvailabilityMask may be set to 1 ⁇ log2_neighbour_avail_boundary.
  • the log2_intra_pred_max_node_size field specifies the octree nodesize eligible for occupancy intra prediction during occupancy intra prediction.
  • the log2_trisoup_node_size field specifies the variable TrisoupNodeSize as the size of the triangle nodes as follows as the size of triangle nodes determined as follows.
  • TrisoupNodeSize 1 ⁇ log2_trisoup_node_size
  • the gps_extension_present_flag field indicates whether a gps_extension_data syntax structure exists in a corresponding GPS syntax. For example, if the value of the gps_extension_present_flag field is 1, it indicates that the gps_extension_data syntax structure exists in the corresponding GPS syntax. For example, if the value of the gps_extension_present_flag field is 0, it indicates that the gps_extension_data syntax structure does not exist in the corresponding GPS syntax.
  • the GPS according to embodiments may further include a gps_extension_data_flag field when a value of the gps_extension_present_flag field is 1.
  • the gps_extension_data_flag field may have any value. Its presence and value do not affect the decoder conformance to profiles.
  • FIG. 27 is a diagram showing an embodiment of a syntax structure of an attribute parameter set (attribute_parameter_set()) (APS) according to the present specification.
  • the APS according to the embodiments may include information on a method of encoding attribute information of point cloud data included in one or more slices, and in particular, an example including offset information related to prediction mode processing is shown.
  • the APS may include an aps_attr_parameter_set_id field, an aps_seq_parameter_set_id field, an attr_coding_type field, aps_attr_initial_qp field, aps_attr_chroma_qp_offset field, aps_slice_qp_delta_present_flag field, and aps_flag field.
  • the aps_attr_parameter_set_id field represents an identifier of an APS for reference by other syntax elements.
  • the aps_seq_parameter_set_id field represents a value of sps_seq_parameter_set_id for an active SPS.
  • the attr_coding_type field represents a coding type for an attribute.
  • the coding type may indicate predicting weight lifting, if it is 1, the coding type may indicate RAHT, and if it is 2, it may indicate fixed weight lifting.
  • the aps_attr_initial_qp field pecifies the initial value of the variable SliceQp for each slice referring to the APS.
  • the initial value of SliceQp is modified at the attribute slice segment layer when a non-zero value of slice_qp_delta_luma or slice_qp_delta_luma is decoded. of slice_qp_delta_luma or slice_qp_delta_luma are decoded).
  • the aps_attr_chroma_qp_offset field specifies the offsets to the initial quantization parameter signaled by the syntax aps_attr_initial_qp.
  • the aps_slice_qp_delta_present_flag field indicates whether the ash_attr_qp_delta_luma and ash_attr_qp_delta_luma syntax elements are present in the corresponding attribute slice header (ASH). For example, if the value of the aps_slice_qp_delta_present_flag field is 1, it indicates that the ash_attr_qp_delta_luma and ash_attr_qp_delta_luma syntax elements are present in the corresponding attribute slice header (ASH) (equal to 1 specifies that the ash_attr_delta present in qp_delta_ASH) .
  • aps_slice_qp_delta_present_flag field 0 when the value of the aps_slice_qp_delta_present_flag field is 0, it indicates that the ash_attr_qp_delta_luma and ash_attr_qp_delta_luma syntax elements do not exist in the corresponding attribute slice header (ASH) (specifies that the ash_attr_delta_present_delma elements are not present in the corresponding attribute slice header (ASH)). the ASH).
  • the value of the attr_coding_type field is 0 or 2, that is, if the coding type is predicting weight lifting or fix weight lifting, the lifting_num_pred_nearest_neighbours field, lifting_max_num_direct_predictors field, A lifting_search_range field, lifting_lod_regular_sampling_enabled_flag field, lifting_num_detail_levels_minus1 field, attribute_pred_residual_separate_encoding_flag field may be further included.
  • the lifting_num_pred_nearest_neighbours field represents the maximum number of nearest neighbors to be used for prediction.
  • the lifting_max_num_direct_predictors field represents the maximum number of predictors to be used for direct prediction.
  • the value of the variable MaxNumPredictors used in the point cloud data decoding process according to the embodiments may be expressed as follows. (specifies the maximum number of predictor to be used for direct prediction.The value of the variable MaxNumPredictors that is used in the decoding process as follows:)
  • MaxNumPredictors lifting_max_num_direct_predicots field + 1
  • the lifting_lifting_search_range field specifies the search range used to determine nearest neighbors to be determining nearest neighbors to be used for prediction and building distance-based levels of detail (LOD). used for prediction and to build distance-based levels of detail).
  • LOD distance-based levels of detail
  • the lifting_lod_regular_sampling_enabled_flag field indicates whether levels of detail (LOD) are created by a regular sampling strategy. For example, if the value of the lifting_lod_regular_sampling_enabled_flag field is 1, it indicates that the levels of detail (LOD) are created by the regular sampling strategy.
  • the lifting_lod_regular_sampling_enabled_flag 1 specifies levels of detail are built by using a regular sampling strategy.
  • the lifting_lod_regular_sampling_enabled_enabled_flag 0 specifies that a distance-based sampling strategy is used instead).
  • the lifting_num_detail_levels_minus1 field indicates the number of LODs for attribute coding (specifies the number of levels of detail for the attribute coding).
  • the APS includes a repeating statement repeated by the value of the lifting_num_detail_levels_minus1 field.
  • the index (idx) is initialized to 0, increases by 1 each time the loop is executed, and the loop is repeated until the index (idx) is greater than the value of the lifting_num_detail_levels_minus1 field.
  • This loop may include the lifting_sampling_period[idx] field if the value of the lifting_lod_decimation_enabled_flag field is true (eg, 1), and may include the lifting_sampling_distance_squared[idx] field if it is false (eg, 0).
  • the lifting_sampling_period[idx] field specifies the sampling period for the level of detail idx.
  • the lifting_sampling_distance_squared[idx] field specifies the square of the sampling distance for the level of detail idx.
  • the attribute_pred_residual_separate_encoding_flag field indicates whether a prediction mode and a residual attribute value are separated. For example, if the value of the attribute_pred_residual_separate_encoding_flag field is 1, it indicates that the prediction mode and the residual attribute value are separated, and if it is 0, it indicates that the prediction mode and the residual attribute value are not separated.
  • the APS according to embodiments may further include an attribute_pred_mode_zero_run_length_coding_flag field when the value of the attribute_pred_residual_separate_encoding_flag field is 1.
  • the attribute_pred_mode_zero_run_length_coding_flag field indicates whether zero run-length coding is applied to the prediction mode. For example, if the value of the attribute_pred_mode_zero_run_length_coding_flag field is 1, it indicates that zero run-length coding is applied to prediction modes, and if it is 0, it indicates that zero run-length coding is not applied to prediction modes.
  • attribute_pred_residual_separate_encoding_flag field and the attribute_pred_mode_zero_run_length_coding_flag field may be signaled to the APS.
  • APS may further include a lifting_adaptive_prediction_threshold field and a lifting_intra_lod_prediction_num_layers field if the value of the attr_coding_type field is 0, that is, if the coding type is predicting weight lifting.
  • the lifting_adaptive_prediction_threshold field specifies the threshold to enable adaptive prediction.
  • the lifting_intra_lod_prediction_num_layers field specifies the number of LOD layers that decoded points in the same LOD layer can refer to to generate a predicted value of the target point (specifies number of LOD layer where decoded points in the same LOD layer could be referred to) generate prediction value of target point).
  • the value of the lifting_intra_lod_prediction_num_layers field is a value of the num_detail_levels_minus1 field + 1, it indicates that the target point can refer to decoded points in the same LOD layer for all LOD layers (The lifting_intra_lod_prediction_num_layers field equal to num_minus1 plus 1 level indicates that target point could refer decoded points in the same LOD layer for all LOD layers).
  • the target point cannot refer to decoded points in the same LOD layer for arbitrary LOD layers (The lifting_intra_lod_prediction_num_layers field equal to 0 indicates that target point could not refer) decoded points in the same LoD layer for any LoD layers).
  • the aps_extension_present_flag field indicates whether the aps_extension_data syntax structure exists in the corresponding APS syntax structure. For example, if the value of the aps_extension_present_flag field is 1, it indicates that the aps_extension_data syntax structure exists in the corresponding APS syntax structure. For example, if the value of the aps_extension_present_flag field is 0, it indicates that the aps_extension_data syntax structure does not exist in the corresponding APS syntax structure.
  • the APS according to embodiments may further include an aps_extension_data_flag field when a value of the aps_extension_present_flag field is 1.
  • the aps_extension_data_flag field may have any value. Its presence and value do not affect the decoder conformance to profiles.
  • TPS tile parameter set
  • a tile parameter set may be referred to as a tile inventory.
  • the TPS according to the embodiments includes information related to each tile for each tile, and in particular, an example including offset information related to prediction mode processing is shown.
  • TPS includes a num_tiles field.
  • the num_tiles field represents the number of tiles signaled for the bitstream. If there are no tiles, the value of the num_tiles field will be 0 (when not present, num_tiles is inferred to be 0).
  • the TPS includes a repeating statement repeated by the value of the num_tiles field.
  • i is initialized to 0, increases by 1 each time the loop is executed, and the loop is repeated until the value of i becomes the value of the num_tiles field.
  • This loop 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, a tile_bounding_box_size_coding_size_depth, and an attribute_pardi field.
  • the tile_bounding_box_offset_x[i] field indicates the x offset of the i-th tile in the cartesian coordinates.
  • the tile_bounding_box_offset_y[i] field represents the y offset of the i-th tile in a Cartesian coordinate system.
  • the tile_bounding_box_offset_z[i] field represents the z offset of the i-th tile in a Cartesian coordinate system.
  • the tile_bounding_box_size_width[i] field represents the width of the i-th tile in a Cartesian coordinate system.
  • the tile_bounding_box_size_height[i] field represents the height of the i-th tile in a Cartesian coordinate system.
  • the tile_bounding_box_size_depth[i] field represents the depth of the i-th tile in a Cartesian coordinate system.
  • the attribute_pred_residual_separate_encoding_flag[i] field indicates whether a prediction mode and a residual attribute value are separated in an i-th tile. For example, if the value of the attribute_pred_residual_separate_encoding_flag[i] field is 1, it indicates that the prediction mode and the residual attribute value are separated, and if it is 0, it indicates that the prediction mode and the residual attribute value are not separated.
  • TPS may further include an attribute_pred_mode_zero_run_length_coding_flag[i] field when a value of the attribute_pred_residual_separate_encoding_flag[i] field is 1.
  • the attribute_pred_mode_zero_run_length_coding_flag[i] field indicates whether zero run-length coding is applied to prediction modes in the i-th tile. For example, if the value of the attribute_pred_mode_zero_run_length_coding_flag[i] field is 1, it indicates that zero run-length coding is applied to the prediction modes, and if it is 0, it indicates that zero run-length coding is not applied to the prediction modes. do.
  • attribute_pred_residual_separate_encoding_flag[i] field and the attribute_pred_mode_zero_run_length_coding_flag[i] field may be signaled to the TPS.
  • 29 is a diagram showing an embodiment of a syntax structure of a geometry slice bitstream () according to the present specification.
  • the geometry slice bitstream (geometry_slice_bitstream ()) may include a geometry slice header (geometry_slice_header()) and geometry slice data (geometry_slice_data()).
  • FIG. 30 is a diagram showing an embodiment of a syntax structure of a geometry slice header (geometry_slice_header()) according to the present specification.
  • a bitstream transmitted by a transmitting 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).
  • the geometry slice header (geometry_slice_header()) 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.
  • the value of the gps_box_present_flag field included in the geometry parameter set (GPS) is true (for example, 1)
  • the value of the gps_gsh_box_log2_scale_present_flag field is true (for example, 1 )
  • a gsh_box_log2_scale field may be further included.
  • the gsh_geom_parameter_set_id field indicates a value of gps_geom_parameter_set_id of the active GPS (specifies the value of the gps_geom_parameter_set_id of the active GPS).
  • the gsh_tile_id field represents an identifier of a corresponding tile referenced by a corresponding geometry slice header (GSH).
  • the gsh_slice_id represents an identifier of a corresponding slice for reference by other syntax elements.
  • the gsh_box_log2_scale field represents a scaling factor of a bounding box origin for a corresponding slice.
  • the gsh_box_origin_x field represents the x value of the bounding box origin scaled by the value of the gsh_box_log2_scale field.
  • the gsh_box_origin_y field represents the y value of the bounding box origin scaled by the value of the gsh_box_log2_scale field.
  • the gsh_box_origin_z field represents the z value of the bounding box origin scaled by the value of the gsh_box_log2_scale field.
  • the gsh_max_node_size_log2 field represents the size of a root geometry octree node.
  • the gsh_points_number field represents the number of coded points in a corresponding slice.
  • Geometry slice data (geometry_slice_data()) according to embodiments may transmit a geometry bitstream belonging to a corresponding slice.
  • the geometry slice data (geometry_slice_data()) may include a first loop repeated by a value of MaxGeometryOctreeDepth.
  • the depth is initialized to 0, increases by 1 each time the loop is executed, and the first loop is repeated until the depth becomes the value of MaxGeometryOctreeDepth.
  • the first loop statement may include a second loop statement repeated by a value of NumNodesAtDepth. In this case, nodeidx is initialized to 0, increases by 1 each time the loop is executed, and the second loop is repeated until nodeidx becomes the value of NumNodesAtDepth.
  • MaxGeometryOctreeDepth represents the maximum value of the geometry octree depth
  • NumNodesAtDepth represents the number of nodes to be decoded at the corresponding depth.
  • NodeX[depth][nodeIdx], NodeY[depth][nodeIdx], NodeZ[depth][nodeIdx] represent the z, y, z coordinates of the nodeIdx th node in decoding order at a given depth.
  • the geometry bitstream of the node of the corresponding depth is transmitted through geometry_node(depth, nodeIdx, xN, yN, zN).
  • Geometry slice data may further include geometry_trisoup_data() if the value of the log2_trisoup_node_size field is greater than 0. That is, if the size of the triangle nodes is greater than 0, a geometry bitstream encoded with a trish geometry is transmitted through geometry_trisoup_data().
  • 32 is a diagram showing an embodiment of a syntax structure of an attribute slice bitstream () according to the present specification.
  • the attribute slice bitstream (attribute_slice_bitstream ()) may include an attribute slice header (attribute_slice_header()) and attribute slice data (attribute_slice_data()).
  • attribute slice header includes signaling information for a corresponding attribute slice, and in particular, an example including offset information related to prediction mode processing is shown.
  • the attribute slice header (attribute_slice_header()) may include an ash_attr_parameter_set_id field, an ash_attr_sps_attr_idx field, an ash_attr_geom_slice_id field, and an attribute_pred_residual_separate_encoding_flag field.
  • the attribute slice header (attribute_slice_header()) may further include an ash_qp_delta_luma field and an ash_qp_delta_chroma field if a value of the aps_slice_qp_delta_present_flag field of the attribute parameter set (APS) is true (eg, 1).
  • the ash_attr_parameter_set_id field represents a value of the aps_attr_parameter_set_id field of the current active APS (for example, the aps_attr_parameter_set_id field included in the APS described in FIG. 27).
  • the ash_attr_sps_attr_idx field identifies an attribute set in the currently active SPS.
  • the value of the ash_attr_sps_attr_idx field is in a range from 0 to the sps_num_attribute_sets field included in the current active SPS.
  • the ash_attr_geom_slice_id field represents a value of the gsh_slice_id field of the current geometry slice header.
  • the ash_qp_delta_luma field represents a luma delta quantization parameter qp derived from an initial slice qp in an active attribute parameter set.
  • the ash_qp_delta_chroma field represents a chroma delta quantization parameter qp derived from an initial slice qp in an active attribute parameter set.
  • the attribute_pred_residual_separate_encoding_flag field indicates whether a prediction mode and a residual attribute value are separated in a corresponding slice. For example, if the value of the attribute_pred_residual_separate_encoding_flag field is 1, it indicates that the prediction mode and the residual attribute value are separated, and if it is 0, it indicates that the prediction mode and the residual attribute value are not separated.
  • the attribute slice header may further include an attribute_pred_mode_zero_run_length_coding_flag field when a value of the attribute_pred_residual_separate_encoding_flag field is 1.
  • the attribute_pred_mode_zero_run_length_coding_flag field indicates whether zero run-length coding is applied to prediction modes in a corresponding slice. For example, if the value of the attribute_pred_mode_zero_run_length_coding_flag field is 1, it indicates that zero run-length coding is applied to prediction modes, and if it is 0, it indicates that zero run-length coding is not applied to prediction modes.
  • attribute_pred_residual_separate_encoding_flag field and the attribute_pred_mode_zero_run_length_coding_flag field may be signaled in an attribute slice header.
  • attribute slice data (attribute_slice_data()) according to the present specification.
  • the attribute slice data (attribute_slice_data()) according to embodiments may transmit an attribute bitstream belonging to a corresponding slice.
  • the attribute dimension (attribute_dimension) refers to the number of components constituting an attribute. Attributes according to embodiments represent reflectance, color, and the like. Therefore, the number of components that an attribute has is different. For example, an attribute corresponding to a color may have three color components (eg, RGB). Therefore, an attribute corresponding to reflectance may be a mono-dimensional attribute, and an attribute corresponding to a color may be a three-dimensional attribute.
  • Attributes according to embodiments may be attribute-encoded in units of dimensions.
  • an attribute corresponding to reflectance and an attribute corresponding to color may be attribute-encoded, respectively.
  • attributes according to embodiments may be attribute-encoded together regardless of dimensions.
  • an attribute corresponding to reflectance and an attribute corresponding to color may be attribute encoded together.
  • zero run-length coding is applied to the separated prediction modes.
  • zero run length coding for prediction modes is performed by the attribute information predictor 53009 of FIG. 17, and zero run-length coding for residual attribute values is performed by the residual attribute information quantization processor 53010 of FIG. This is done as an example. That is, zero run-length coding for prediction modes is performed before zero run-length coding for residual attribute values.
  • zero run-length coding is applied to the separated prediction modes.
  • zero run-length coding is not applied. Modes and residual attribute values to which zero run-length coding is applied are transmitted. In the present specification, zero run-length coding for residual attribute values is performed by the residual attribute information quantization processor 5310 of FIG. 17 as an embodiment.
  • the residual attribute information quantization processing unit 53010 of FIG. 17 may additionally apply and transmit zero run-length coding on residual attribute values.
  • i denotes the i-th point value of the attribute
  • zerorun denotes the number of zeros before the predictor index (preindex, ie, prediction mode) or residual attribute value (zerorun specifies the number of 0 prior to preindex). or residual).
  • the attribute_pred_residual_separate_encoding_flag field and the attribute_pred_mode_zero_run_length_coding_flag field are signaled to at least one of SPS (see FIG. 25), APS (see FIG. 27), TPS (see FIG. 28), and an attribute slice header (see FIG. 33), and the According to an embodiment, the attr_coding_type field and the lifting_adaptive_prediction_threshold field are signaled to the APS (refer to FIG. 27).
  • variable MaxNumPredictors of FIG. 34 is a variable used in the point cloud data decoding process, and may be obtained as follows based on the lifting_adaptive_prediction_threshold field value signaled to the APS of FIG. 27.
  • MaxNumPredictors lifting_max_num_direct_predicots field + 1
  • the lifting_max_num_direct_predictors field represents the maximum number of predictors to be used for direct prediction.
  • Pred_index[i] specifies the predictor index to decode the i-th point value of the attribute attribute).
  • the value of pred_index[i] is in the range from 0 to the value of the lifting_max_num_direct_predictors field.
  • the variable MaxPredDiff[i] may be calculated as follows.
  • k i is the set of k-nearest neighbors of the current point i
  • (a j ) jEXi is defined as their decoded/ reconstructed attribute values
  • Let k i be the set of the k-nearest neighbors of the current point i and let (a j ) jEXi be their decoded/reconstructed attribute values).
  • the number of nearest neighbors, k i is in the range of from 1 to the value of the field lifting_num_pred_nearest_neighbours (The number of nearest neighbours, k i shall be range of 1 to lifting_num_pred_nearest_neighbours).
  • the decoded/reconstructed attribute value of neighbors are derived according to the Predictive Lifting decoding process. .
  • the lifting_num_pred_nearest_neighbours field is signaled to the APS of FIG. 27 and indicates the maximum number of nearest neighbors to be used for prediction.
  • 35 is a flowchart of a method for transmitting point cloud data according to embodiments.
  • the method of transmitting point cloud data includes the steps of encoding (71001) geometry included in point cloud data, encoding attributes included in the point cloud data based on input and/or reconstructed geometry ( 71002), and transmitting a bitstream including the encoded geometry, the encoded attribute, and signaling information (71003).
  • Encoding the geometry and attributes included in the point cloud data includes the point cloud video encoder 10002 of FIG. 1, the encoding 20001 of FIG. 2, the point cloud video encoder of FIG. 4, and the point of FIG. 12. Some or all of the operations of the cloud video encoder, the point cloud encoding of FIG. 14, the point cloud video encoder of FIG. 15, and the geometry encoder and attribute encoder of FIG. 17 may be performed.
  • the prediction modes and residual attribute values are separated from all points, and zero run-length coding is applied to the separated prediction modes and the separated residual attribute values, respectively. can do.
  • a prediction mode and a residual attribute value are configured as a pair for each point, and zero run-length coding may be applied only to residual attribute values in a quantization process.
  • information about whether the prediction mode and the residual attribute value are separated may be signaled to at least one of a sequence parameter set, an attribute parameter set, a tile parameter set, and an attribute slice header.
  • information on whether run-length coding has been applied to the separated prediction modes (e.g., attribute_pred_residual_separate_encoding_flag) is signaled to at least one of a sequence parameter set, an attribute parameter set, a tile parameter set, and an attribute slice header. can do.
  • encoding may be performed in units of a slice or a tile including one or more slices.
  • the step of transmitting a bitstream including the encoded geometry, the encoded attribute, and signaling information (71003) includes the transmitter 10003 of FIG. 1, the transmission step 20002 of FIG. 2, and the transmission processing unit 12012 of FIG. 12. ) Or may be performed by the transmission processing unit 5108 of FIG. 15.
  • 36 is a flowchart of a method for receiving point cloud data according to embodiments.
  • the method for receiving point cloud data includes receiving a bitstream including an encoded geometry, an encoded attribute, and signaling information (81001), and decoding a geometry based on signaling information (81002). , Decoding the attribute based on the decoded/reconstructed geometry and signaling information (81003), and rendering the restored point cloud data based on the decoded geometry and the decoded attribute (81004).
  • the receiving (81001) of the bitstream including the encoded geometry, the encoded attribute, and signaling information according to the embodiments may be performed by the receiver 10005 of FIG. 1, the passivation 20002 of FIG. 2, or the decoding 20003. ), the reception unit 13000 or the reception processing unit 13001 of FIG. 13, or the reception processing unit 61001 of FIG. 20.
  • the decoding of geometry and attributes according to embodiments (81002 and 81003) may perform decoding in units of slices or tiles including one or more slices.
  • the step of decoding the geometry 81002 includes a point cloud video decoder 10006 of FIG. 1, decoding 20003 of FIG. 2, a point cloud video decoder of FIG. 11, a point cloud video decoder of FIG. 13, and Some or all of the operations of the geometry decoder of 20 and the geometry decoder of FIG. 21 may be performed.
  • the step of decoding an attribute 81003 includes a point cloud video decoder 10006 of FIG. 1, decoding 20003 of FIG. 2, a point cloud video decoder of FIG. 11, a point cloud video decoder of FIG. 13, and Some or all of the operations of the attribute decoder of 20 and the attribute decoder of FIG. 21 may be performed.
  • At least one of the signaling information is separated from information on whether to separate a prediction mode and a residual attribute value (eg, attribute_pred_residual_separate_encoding_flag).
  • Information eg, attribute_pred_residual_separate_encoding_flag on whether run-length coding has been applied to the predicted prediction modes may be included.
  • the step of decoding an attribute (81003) information on whether to separate a prediction mode and a residual attribute value included in the signaling information (e.g., attribute_pred_residual_separate_encoding_flag) and run-length coding for the separated prediction modes are performed.
  • the prediction mode of each point and the residual attribute value are separated based on information on whether or not it has been applied (e.g., attribute_pred_residual_separate_encoding_flag), and if it is determined that zero run-length coding has been applied to the prediction modes of the points, the prediction mode of the points.
  • the zero run-length decoding is performed first with the reverse process of the transmitting side for the data, and then zero run-length decoding is performed with the reverse process of the transmitting side with respect to the residual attribute values.
  • the corresponding point corresponds to the predicted attribute value based on the prediction mode of each point.
  • the attribute value of each point is restored by performing a process of adding the residual attribute values of.
  • the reconstructed point cloud data may be rendered according to various rendering methods.
  • the points of the point cloud content may be rendered as a vertex having a certain thickness, a cube having a specific minimum size centered on the vertex position, or a circle centered on the vertex position. All or part of the rendered point cloud content is provided to the user through a display (e.g., VR/AR display, general display, etc.).
  • the rendering of the point cloud data according to embodiments 81004 may be performed by the renderer 10007 of FIG. 1, the rendering 20004 of FIG. 2, or the renderer 13011 of FIG. 13.
  • the present specification provides a prediction mode bitstream consisting of prediction modes by separating a prediction mode and a residual attribute value from each point in order to increase the compression efficiency of attributes of 3D point cloud data and to provide parallel processing more efficiently.
  • Each of the residual attribute value bitstreams consisting of residual attribute values is configured. By doing so, since the dependence between the prediction mode and the residual attribute value is separated, the point cloud video encoder/decoder can provide a structure capable of parallel processing.
  • the size of the prediction mode bitstream can be reduced by applying zero run-length coding to the prediction mode bitstream. For this reason, it is possible to increase the compression efficiency of the attribute.
  • Each of the above-described parts, modules or units may be software, processor, or hardware parts that execute successive execution processes stored in a memory (or storage unit). Each of the steps described in the above-described embodiment may be performed by processor, software, and hardware parts. Each module/block/unit described in the above-described embodiment may operate as a processor, software, or hardware. In addition, the methods suggested by the embodiments may be executed as code. This code can be written to a storage medium that can be read by a processor, and thus can be read by a processor provided by an apparatus.
  • the apparatus and method according to the embodiments are not limitedly applicable to the configuration and method of the described embodiments as described above, but the embodiments are all or part of each of the embodiments selectively combined so that various modifications can be made. It can also be configured.
  • Various components of the apparatus of the embodiments may be implemented by hardware, software, firmware, or a combination thereof.
  • Various components of the embodiments may be implemented with one chip, for example, one hardware circuit.
  • Each of the components according to the embodiments may be implemented as separate chips.
  • At least one or more of the components of the device according to the embodiments may be composed of one or more processors capable of executing one or more programs, and the one or more programs may be operated/ It may include instructions for performing, or performing any one or more operations/methods of the methods.
  • Executable instructions for performing the method/operations of the apparatus according to the embodiments may be stored in a non-transitory CRM or other computer program products configured to be executed by one or more processors, or may be stored in one or more It may be stored in temporary CRM or other computer program products configured to be executed by the processors.
  • the memory according to the embodiments may be used as a concept including not only volatile memory (eg, RAM, etc.) but also non-volatile memory, flash memory, PROM, and the like.
  • it may be implemented in the form of a carrier wave such as transmission through the Internet.
  • the recording medium readable by the processor may be distributed over a computer system connected through a network, so that code readable by the processor may be stored and executed in a distributed manner.
  • Various elements of the embodiments may be performed by hardware, software, firmware, or a combination thereof.
  • Various elements of the embodiments may be implemented on a single chip, such as a hardware circuit.
  • the embodiments may optionally be performed on individual chips.
  • at least one of the elements of the embodiments may be executed in one or more processors including instructions for performing operations according to the embodiments.
  • first and second are used to describe various elements of the embodiments. These terms do not limit the interpretation of the elements of the embodiments. These terms are used to distinguish between one element and another.
  • a first user input signal may be referred to as a second user input signal.
  • the second user input signal may be referred to as a first user input signal.
  • the first user input signal and the second user input signal are both user input signals and do not mean the same user input signals unless clearly indicated in context.
  • Conditional expressions such as, when, and when, used to describe the embodiments are not limited to an optional case. When a specific condition is satisfied, it is intended to perform a related action in response to a specific condition, or to interpret the related definition.

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Abstract

Un procédé de transmission de données de nuage de points selon des modes de réalisation peut comprendre les étapes consistant à : acquérir des données de nuage de points; coder des informations de géométrie des données de nuage de points; coder des informations d'attribut des données de nuage de points sur la base des informations de géométrie; et transmettre un train de bits comprenant les informations de géométrie codées, les informations d'attribut codées et les informations de signalisation.
PCT/KR2020/012072 2019-09-06 2020-09-07 Dispositif de transmission de données de nuage de points, procédé de transmission de données de nuage de points, dispositif de réception de données de nuage de points et procédé de réception de données de nuage de points WO2021045603A1 (fr)

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KHALED MAMMOU, PHILIP A. CHOU, DAVID FLYNN, MAJA KRIVOKUĆA, OHJI NAKAGAMI AND TOSHIYASU SUGIO: "G-PCC codec description v2;C1", ITU-T DRAFT; STUDY PERIOD 2017-2020; STUDY GROUP 15; SERIES C1, INTERNATIONAL TELECOMMUNICATION UNION, GENEVA ; CH, vol. ties/16, 8 March 2019 (2019-03-08), Geneva ; CH, pages 1 - 39, XP044260571 *
SCHWARZ SEBASTIAN; PREDA MARIUS; BARONCINI VITTORIO; BUDAGAVI MADHUKAR; CESAR PABLO; CHOU PHILIP A.; COHEN ROBERT A.; KRIVOKUCA MA: "Emerging MPEG Standards for Point Cloud Compression", IEEE JOURNAL ON EMERGING AND SELECTED TOPICS IN CIRCUITS AND SYSTEMS, IEEE, PISCATAWAY, NJ, USA, vol. 9, no. 1, 30 March 2019 (2019-03-30), Piscataway, NJ, USA, pages 133 - 148, XP011714044, ISSN: 2156-3357, DOI: 10.1109/JETCAS.2018.2885981 *

Cited By (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
EP3901916A4 (fr) * 2018-12-21 2022-02-23 Panasonic Intellectual Property Corporation of America Procédé de codage de données tridimensionnelles, procédé de décodage de données tridimensionnelles, dispositif de codage de données tridimensionnelles et dispositif de décodage de données tridimensionnelles
US11395005B2 (en) 2018-12-21 2022-07-19 Panasonic Intellectual Property Corporation Of America Three-dimensional data encoding method, three-dimensional data decoding method, three-dimensional data encoding device, and three-dimensional data decoding device
US20210407145A1 (en) * 2020-06-30 2021-12-30 Electronics And Telecommunications Research Institute Method of compressing occupancy map of three-dimensional point cloud
US11954891B2 (en) * 2020-06-30 2024-04-09 Electronics And Telecommunications Research Institute Method of compressing occupancy map of three-dimensional point cloud
WO2022247705A1 (fr) * 2021-05-26 2022-12-01 荣耀终端有限公司 Procédé et appareil de codage et de décodage de prédiction pour des informations d'attribut de nuage de points
WO2023240660A1 (fr) * 2022-06-17 2023-12-21 Oppo广东移动通信有限公司 Procédé de décodage, procédé de codage, décodeur et codeur

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