WO2023098802A1 - 点云属性编码方法、点云属性解码方法及终端 - Google Patents

点云属性编码方法、点云属性解码方法及终端 Download PDF

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WO2023098802A1
WO2023098802A1 PCT/CN2022/135874 CN2022135874W WO2023098802A1 WO 2023098802 A1 WO2023098802 A1 WO 2023098802A1 CN 2022135874 W CN2022135874 W CN 2022135874W WO 2023098802 A1 WO2023098802 A1 WO 2023098802A1
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point
attribute
encoded
decoded
prediction
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French (fr)
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张伟
杨付正
张可
吕卓逸
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Vivo Mobile Communication Co Ltd
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    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; 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
    • H04N19/102—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using adaptive coding characterised by the element, parameter or selection affected or controlled by the adaptive coding
    • H04N19/124—Quantisation
    • 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
    • H04N19/102—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using adaptive coding characterised by the element, parameter or selection affected or controlled by the adaptive coding
    • H04N19/13—Adaptive entropy coding, e.g. adaptive variable length coding [AVLC] or context adaptive binary arithmetic coding [CABAC]
    • 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
    • H04N19/50—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using predictive coding
    • H04N19/503—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using predictive coding involving temporal prediction
    • 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
    • H04N19/50—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using predictive coding
    • H04N19/597—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using predictive coding specially adapted for multi-view video sequence encoding
    • 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
    • H04N19/70—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals characterised by syntax aspects related to video coding, e.g. related to compression standards
    • 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
    • H04N19/85—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using pre-processing or post-processing specially adapted for video compression
    • H04N19/88—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using pre-processing or post-processing specially adapted for video compression involving rearrangement of data among different coding units, e.g. shuffling, interleaving, scrambling or permutation of pixel data or permutation of transform coefficient data among different blocks

Definitions

  • the present application belongs to the technical field of point cloud processing, and in particular relates to a point cloud attribute encoding method, a point cloud attribute decoding method and a terminal.
  • a point cloud is a set of discrete point sets randomly distributed in space that express the spatial structure and surface properties of a three-dimensional object or scene.
  • attribute encoding is performed on the point cloud, wherein the attribute encoding of the point cloud includes attribute predictive encoding.
  • the geometric information of the point cloud needs to be reordered, which disrupts the geometric coding order of the point cloud after geometric coding. It is necessary to determine the nearest neighbor point corresponding to the point to be coded in a more complicated way. Attribute predictive coding reduces coding efficiency.
  • the attribute decoding process of point cloud corresponds to the attribute encoding process of point cloud, and the reordering of point cloud during the attribute decoding process also reduces the attribute decoding efficiency of point cloud.
  • the embodiment of the present application provides a point cloud attribute encoding method, a point cloud attribute decoding method and a terminal, which can solve the problem that in the process of attribute prediction encoding, the operation of reordering the geometric information of the point cloud reduces the attribute encoding and decoding efficiency of the point cloud technical issues.
  • a method for encoding point cloud attributes comprising:
  • the encoding end obtains the point cloud to be encoded
  • the encoding end determines at least one prediction point corresponding to the point to be encoded based on the position of the point to be encoded in the point cloud to be encoded;
  • the encoding end determines the predicted attribute value of the point cloud to be encoded based on the reconstructed attribute value of the predicted point and the weight value corresponding to the predicted point;
  • the coding end performs quantization and entropy coding on the first attribute prediction residual to obtain a target code stream, and the first attribute prediction residual is determined based on the real attribute value of the point to be encoded and the predicted attribute value.
  • a device for encoding point cloud attributes including:
  • the first obtaining module is used to obtain the point cloud to be coded
  • a first determination module configured to determine at least one prediction point corresponding to the point to be encoded based on the position of each point to be encoded in the point cloud to be encoded;
  • the second determination module is configured to determine the predicted attribute value of the point cloud to be encoded based on the reconstructed attribute value of the predicted point and the weight value corresponding to the predicted point;
  • An encoding module configured to perform quantization and entropy encoding on a first attribute prediction residual to obtain a target code stream, where the first attribute prediction residual is determined based on the actual attribute value of the point to be encoded and the predicted attribute value.
  • a method for decoding point cloud attributes comprising:
  • the decoding end obtains the target code stream, and the target code stream includes the point cloud to be decoded;
  • the decoding end determines at least one prediction point corresponding to the point to be decoded based on the position of the point to be decoded in the point cloud to be decoded;
  • the decoding end obtains the reconstructed attribute value of the point to be decoded based on the second attribute prediction residual of the point to be decoded, the reconstructed attribute value of the predicted point, and the weight value corresponding to the predicted point.
  • a point cloud attribute decoding device including:
  • the second acquisition module is used to acquire a target code stream, the target code stream includes a point cloud to be decoded;
  • a third determining module configured to determine at least one prediction point corresponding to the point to be decoded based on the position of the point to be decoded in the point cloud to be decoded;
  • the decoding module is configured to obtain the reconstructed attribute value of the point to be decoded based on the second attribute prediction residual of the point to be decoded, the reconstructed attribute value of the predicted point, and the weight value corresponding to the predicted point.
  • a communication device in a fifth aspect, includes a processor and a memory, the memory stores programs or instructions that can run on the processor, and the programs or instructions are implemented when executed by the processor.
  • a sixth aspect provides a readable storage medium, on which a program or instruction is stored, and when the program or instruction is executed by a processor, the steps of the point cloud attribute encoding method as described in the first aspect are implemented , or realize the steps of the point cloud attribute decoding method as described in the third aspect.
  • a chip in a seventh aspect, includes a processor and a communication interface, the communication interface is coupled to the processor, and the processor is used to run programs or instructions to achieve the points described in the first aspect A cloud attribute encoding method, or the steps for realizing the point cloud attribute decoding method as described in the third aspect.
  • a computer program product is provided, the computer program product is stored in a storage medium, and the computer program product is executed by at least one processor to implement the point cloud attribute encoding method as described in the first aspect steps, or realize the steps of the point cloud attribute decoding method as described in the third aspect.
  • a ninth aspect provides an electronic device configured to implement the steps of the method described in the first aspect.
  • an electronic device configured to implement the steps of the method described in the third aspect.
  • the point cloud to be encoded is obtained, and at least one predicted point corresponding to the point to be encoded is determined based on the position of the point to be encoded in the point cloud to be encoded; based on the reconstruction attribute value of the predicted point and the weight value corresponding to the predicted point, Determine the predicted attribute value of the point cloud to be encoded; perform quantization and entropy encoding on the first attribute prediction residual to obtain the target code stream.
  • point cloud attribute encoding process no reordering of the point cloud to be encoded is involved, so the geometric encoding order of the point cloud after geometric encoding will not be disrupted, and the nearest neighbor point of the point to be encoded can be determined based on the geometric encoding order. Attribute predictive coding is performed to improve coding efficiency.
  • Fig. 1 is a schematic diagram of the framework of a point cloud AVS point cloud attribute encoding device
  • Fig. 2 is a schematic diagram of the framework of a point cloud AVS point cloud attribute decoding device
  • Fig. 3 is the flow chart of the point cloud attribute encoding method that the embodiment of the present application provides;
  • FIG. 4 is an application scenario diagram of the point cloud attribute encoding method provided by the embodiment of the present application.
  • Fig. 5 is a flow chart of the point cloud attribute decoding method provided by the embodiment of the present application.
  • FIG. 6 is a structural diagram of a point cloud attribute encoding device provided by an embodiment of the present application.
  • FIG. 7 is a structural diagram of a point cloud attribute decoding device provided by an embodiment of the present application.
  • FIG. 8 is a structural diagram of a communication device provided by an embodiment of the present application.
  • FIG. 9 is a schematic diagram of a hardware structure of a terminal provided by an embodiment of the present application.
  • first, second and the like in the specification and claims of the present application are used to distinguish similar objects, and are not used to describe a specific sequence or sequence. It is to be understood that the terms so used are interchangeable under appropriate circumstances such that the embodiments of the application are capable of operation in sequences other than those illustrated or described herein and that "first" and “second” distinguish objects. It is usually one category, and the number of objects is not limited. For example, there may be one or more first objects.
  • “and/or” in the description and claims means at least one of the connected objects, and the character “/” generally means that the related objects are an "or” relationship.
  • Both the point cloud attribute encoding device corresponding to the point cloud attribute encoding method in the embodiment of the present application and the point cloud attribute decoding device corresponding to the point cloud attribute decoding method can be terminals, and the terminal can also be called terminal equipment or user equipment (User Equipment).
  • the terminal can be a mobile phone, a tablet computer (Tablet Personal Computer), a laptop computer (Laptop Computer) or a notebook computer, a personal digital assistant (Personal Digital Assistant, PDA), a handheld computer, a netbook, a super mobile personal Computer (ultra-mobile personal computer, UMPC), mobile Internet device (Mobile Internet Device, MID), augmented reality (augmented reality, AR) / virtual reality (virtual reality, VR) equipment, robot, wearable device (Wearable Device ) or vehicle equipment (Vehicle User Equipment, VUE), pedestrian terminal (Pedestrian User Equipment, PUE), smart home (home equipment with wireless communication functions, such as refrigerators, TVs, washing machines or furniture, etc.), game consoles, personal computers ( personal computer, PC), teller machines or self-service machines and other terminal-side devices, wearable devices include: smart watches, smart bracelets, smart headphones, smart glasses, smart jewelry (smart bracelets, smart bracelets, smart rings, smart necklaces, smart anklets, smart anklet
  • Fig. 1 As shown in Fig. 1, at present, in the technical standard of digital audio and video codec, the geometric information and Attribute information is encoded separately.
  • coordinate transformation is performed on the geometric information so that all point clouds are contained in a bounding box, and then the coordinates are quantized.
  • Quantization mainly plays the role of scaling. Since quantization will round the geometric coordinates, the geometric information of some points will be the same, which is called duplicate points. It is determined whether to remove duplicate points according to the parameters. Quantization and removal of duplicate points are two steps. Also known as the voxelization process.
  • the bounding box is divided into 8 sub-cubes, and the non-empty sub-cubes continue to be divided until the unit cube with leaf nodes of 1x1x1 is obtained.
  • the number of points in the point is encoded to generate a binary code stream.
  • the points to be encoded need to store the occupancy information of neighbor nodes to perform predictive coding for the occupancy information of the points to be encoded. In this way, for the points to be encoded that are close to the leaf nodes , need to store a large amount of occupancy information, occupying a large amount of memory space.
  • Attribute coding is mainly aimed at color and reflectance information. First, judge whether to perform color space conversion according to the parameters. If color space conversion is performed, the color information is converted from the red green blue (RGB) color space to the brightness color (YUV) color space. Then, the geometrically reconstructed point cloud is recolored with the original point cloud so that the unencoded attribute information corresponds to the reconstructed geometric information.
  • RGB red green blue
  • YUV brightness color
  • the nearest neighbor of the point to be predicted is searched using the geometric spatial relationship, and the predicted point is predicted by using the reconstruction attribute value of the found neighbor to obtain Predict the attribute value, then make a difference between the real attribute value and the predicted attribute value to obtain the prediction residual, and finally quantize and encode the prediction residual to generate a binary code stream.
  • the decoding process in the digital audio and video codec technical standard corresponds to the above encoding process.
  • the frame of the AVS point cloud attribute decoding device is shown in FIG. 2 .
  • an embodiment of the present application provides a point cloud attribute encoding method.
  • the point cloud attribute encoding method provided by the embodiment of the present application will be described in detail below through some embodiments and application scenarios with reference to the accompanying drawings.
  • FIG. 3 is a flow chart of the point cloud attribute encoding method provided by the present application.
  • the point cloud attribute encoding method provided in this embodiment includes the following steps:
  • the point cloud to be encoded in this step is a point cloud that has undergone geometric encoding and has a geometric encoding order; the point cloud to be encoded in this step is a point cloud that has not been reordered, and the point cloud to be encoded in this step is a point cloud that has undergone Point cloud recoloring and color space conversion.
  • the above-mentioned recoloring refers to recoloring the geometrically reconstructed point cloud by using the original point cloud, so that the unencoded attribute information corresponds to the reconstructed geometric information, and the recolored point cloud is obtained;
  • the above-mentioned color space conversion refers to the transformation of the point cloud Color information is converted from RGB space to YUV space.
  • this step when most of the coded blocks in the point cloud to be coded are coded using predictive tree coding, since in predictive tree coding, the front and back nodes in the same coded block are usually the nearest neighbor points, it can be based on The characteristics of the above-mentioned prediction tree coding, according to the position of the point to be coded in the point cloud to be coded, determine the prediction point corresponding to the point to be coded, the above-mentioned prediction point is the nearest neighbor point, and the prediction point is the coded point.
  • the number of prediction points corresponding to a point to be encoded is greater than or equal to 1.
  • the preset attribute information corresponding to the point to be encoded may be directly encoded.
  • the above reconstructed attribute values are attribute values corresponding to encoded prediction points.
  • the reconstructed attribute value of the predicted point can be read directly, and the weight value corresponding to the predicted point can be calculated.
  • the weight value corresponding to the prediction point can be calculated.
  • correlation calculation is performed on the above-mentioned reconstructed attribute value and weight value to obtain the predicted predicted value corresponding to the point cloud to be encoded.
  • the first attribute prediction residual is determined based on the real attribute value of the point to be encoded and the predicted attribute value
  • entropy coding is a coding method that does not lose any information according to the principle of entropy during the coding process.
  • the above-mentioned entropy coding may be Shannon coding, Huffman coding or other types of coding, which are not specifically limited in this embodiment.
  • the above-mentioned target code stream is a binary code stream.
  • the point cloud to be encoded is obtained, and at least one predicted point corresponding to the point to be encoded is determined based on the position of the point to be encoded in the point cloud to be encoded; based on the reconstruction attribute value of the predicted point and the weight value corresponding to the predicted point, Determine the predicted attribute value corresponding to the point cloud to be encoded; perform quantization and entropy encoding on the first attribute prediction residual to obtain the target code stream.
  • point cloud attribute encoding process no reordering of the point cloud to be encoded is involved, so the geometric encoding order of the point cloud after geometric encoding will not be disrupted, and the nearest neighbor point of the point to be encoded can be determined based on the geometric encoding order. Attribute predictive coding is performed to improve coding efficiency.
  • the attribute compression efficiency of the point cloud attribute encoding provided by the embodiment of the present application is higher than the attribute compression efficiency of the point cloud attribute encoding of the traditional point cloud attribute encoding.
  • Table 1 For easy understanding of the technical effects produced by this application, please refer to Table 1.
  • “bpip ratio” in Table 1 is also called the average bit ratio of coding points. It should be understood that under the condition of lossless coding, the lower the value of the average bit ratio of coding points, it shows the attribute compression efficiency of the coding method. The higher the value, the better the encoding performance.
  • the average bit ratio of code points includes overall ratio, geometric information ratio and attribute information ratio, where "Total” in Table 1 can also be expressed as overall ratio, “Geometry” can also be expressed as geometric information ratio, and “Colour” can also be expressed as can be expressed as an attribute information ratio.
  • Code stream 1 and code stream 2 shown in Table 1 are code streams obtained by applying the point cloud attribute encoding provided by the embodiment of the present application, code stream 3, code stream 4, code stream 5, code stream 6 and code stream shown in Table 1
  • Code stream 7 is a code stream obtained by traditional point cloud attribute encoding.
  • the attribute information ratio corresponding to the code stream obtained by the point cloud attribute encoding provided by the embodiment of the present application is lower than the attribute information ratio of the code stream obtained by the traditional point cloud attribute encoding.
  • the method includes:
  • the target parameter in the attribute header information of the point cloud to be encoded is set as a preset value.
  • the point cloud will be divided into octrees to obtain multiple encoding blocks, and it is judged whether the octree level corresponding to the point cloud adopts macroblock encoding.
  • macroblock encoding is adopted, Macroblock coding is adopted for the point cloud, and the number of coding blocks corresponding to the point cloud is determined as the number of macroblocks using the macroblock coding method for the point cloud to be coded.
  • the number of macroblocks for which the above-mentioned point cloud to be encoded adopts the macroblock encoding method may be determined as the second value.
  • the point cloud includes the parameter lcu_node_depth.
  • the octree level octree_node_depth corresponding to the point cloud is obtained.
  • the macro block is used to determine the octree level corresponding to the point cloud encoding method.
  • the point cloud is coded by macroblocks, it is judged whether each coded block continues to use octree coding or predictive tree coding, and the number of macroblocks in the point cloud coded by the predictive tree method is determined as the first a value. That is to say, the number of coded blocks in the point cloud coded by the prediction tree method is determined as the first value.
  • the point cloud includes the high density coefficient geomTreeDensityHigh, the low density coefficient geomTreeDensityLow and the minimum point limit parameter geomPointTh.
  • the method of judging whether the coding block is encoded by the prediction tree method is to calculate the density of the coding block, if the density of the coding block is greater than the value corresponding to the low density coefficient and smaller than the value corresponding to the high density coefficient, and the coding point included in the coding block is If the number is greater than the value corresponding to the minimum number of points limit parameter, it can be determined that the coding block is coded using the prediction tree method.
  • the quotient of the first value and the second value is determined as the first proportional value.
  • the condition for enabling macroblock coding is met. Since the point cloud is divided into 8 coding blocks at the second level of octree division, the second value can be determined as 8. If 4 of the above-mentioned 8 coding blocks adopt predictive tree coding, then it can be determined that the first value is 4, and the first ratio value is 0.5.
  • a threshold is set, and when the first ratio value is greater than or equal to the threshold, it means that most of the coding blocks in the point cloud use prediction tree coding, and the target in the attribute header information of the point cloud to be coded Parameters are set to default values.
  • the aforementioned threshold may be an empirical threshold, or may be a threshold set by a user.
  • the above preset value is used to indicate that the point cloud to be encoded satisfies the first condition, that is, most of the encoded blocks in the point cloud to be encoded use prediction tree encoding.
  • the predicted point corresponding to the point to be encoded is determined based on the position of the point to be encoded in the point cloud to be encoded.
  • a target parameter can be set in the Attribute Brick Header (ABH) of the point cloud to be encoded, and the target parameter can be expressed as use_pred_tree_code_attr, wherein the above preset value can be 1. It should be understood that when the assignment of the target parameter is not a preset value, for example, when the assignment of the target parameter is 0, it means that most of the coding blocks in the point cloud to be coded do not use prediction tree coding.
  • ABS Attribute Brick Header
  • the determining at least one prediction point corresponding to the point to be encoded based on the position of each point to be encoded in the point cloud to be encoded includes:
  • the preceding and following coding points in the same coding block are usually the nearest neighbor points.
  • the position of the point to be encoded can be used as the search center, the encoded point can be searched within the preset range of the point to be encoded, and the searched encoded point can be determined as the predicted point.
  • the aforementioned preset range refers to the coded range corresponding to the point to be coded.
  • the coding points in Figure 4 form a geometric coding sequence based on the single-chain structure of the KD tree, where the P0 point is the point to be coded, and P1, P2 and P3 3 coded points, determine the above 3 coded points as prediction points.
  • the coding points in the coding block are not reordered based on the coding block, but based on the geometric coding order of the coding block to determine the prediction points to improve coding efficiency.
  • the determining the predicted attribute value of the point to be encoded according to the reconstructed attribute value of the predicted point and the weight value corresponding to the predicted point includes:
  • the product result of the reconstruction attribute value of the prediction point and the weight value corresponding to other prediction points is determined as the first target value corresponding to the prediction point;
  • the sum of the first target values corresponding to each predicted point is determined as the predicted attribute value of the point to be encoded.
  • the weight value corresponding to each prediction point may be determined based on the geometric spatial positional relationship between the point to be encoded and each prediction point.
  • the weight value corresponding to each prediction point may be determined based on the geometric spatial positional relationship between the point to be encoded and each prediction point.
  • the reconstruction attribute value of the prediction point is multiplied by the weight values corresponding to other prediction points, and the obtained multiplication result is used as the first target value corresponding to the prediction point. Further, the first target values of all predicted points corresponding to the point to be encoded are accumulated to obtain the predicted attribute value of the point to be encoded.
  • the implementation scenario shown in FIG. 4 is taken as an example for description.
  • the point P0 to be encoded corresponds to three prediction points P1, P2 and P3, please refer to the following formula:
  • predAttr W2*W3*Attr1+W1*W3*Attr2+W1*W2*Attr3
  • predAttr is the predicted attribute value of the point to be encoded, that is, the predicted attribute value of P0;
  • W1, W2, and W3 are the weight values corresponding to the predicted point, where W1 is the weight value corresponding to P1, and W2 is the weight value corresponding to P2.
  • W3 is the weight value corresponding to P3;
  • Attr1, Attr2 and Attr3 are the reconstructed attribute values corresponding to the predicted points, among which, Attr1 is the reconstructed attribute value corresponding to P1, Attr 2 is the reconstructed attribute value corresponding to P2, and Attr 3 is the reconstructed attribute value corresponding to P3 attribute value.
  • the predicted attribute value corresponding to the point to be encoded P0 can be obtained through the above formula.
  • the determining the weight value corresponding to each prediction point based on the geometric space position relationship between the point to be encoded and each prediction point includes:
  • Numerical conversion is performed on the distance value to obtain a weight value corresponding to the predicted point.
  • the distance value between the predicted point and the point to be coded is calculated, and in an optional embodiment, the distance value between the predicted point and the point to be coded may be obtained directly.
  • the length of a space vector between the prediction point and the point to be encoded may be calculated, and then based on the length of the space vector, the weight value corresponding to the prediction point is obtained.
  • the essence of performing numerical conversion on the distance value above is to determine the first norm corresponding to the distance value as the weight value corresponding to the prediction point, or determine the second norm corresponding to the distance value as the weight value corresponding to the prediction point, Alternatively, the weight value corresponding to the prediction point may be determined in other numerical representations corresponding to the distance value.
  • FIG. 5 is a flow chart of the point cloud attribute decoding method provided by the present application.
  • the point cloud attribute decoding method provided in this embodiment includes the following steps:
  • the target code stream is directly sent to the point cloud attribute decoding device, and the above target code stream includes the point cloud to be decoded.
  • the number of prediction points corresponding to a point to be decoded is greater than or equal to 1.
  • the preset attribute information corresponding to the point to be decoded may be directly decoded.
  • the decoder performs inverse quantization processing on the prediction residual of the first attribute in the target code stream to obtain the prediction residual of the second attribute.
  • the first attribute prediction residual of the point to be decoded and the reconstructed attribute value of the prediction point may be read directly, and the weight value corresponding to the prediction point is calculated.
  • the weight value corresponding to the prediction point please refer to the subsequent embodiments.
  • a correlation calculation is performed on the prediction residual of the second attribute, the reconstructed attribute value and the weight value to obtain the reconstructed attribute value corresponding to the point cloud to be decoded.
  • the target code stream is obtained, and the target code stream includes the point cloud to be decoded, and at least one prediction point corresponding to the point to be decoded is determined based on the position of the point to be decoded in the point cloud to be decoded; based on the second
  • the attribute prediction residual, the reconstructed attribute value of the predicted point, and the weight value corresponding to the predicted point are used to obtain the reconstructed attribute value of the point to be decoded.
  • the method includes:
  • the point cloud to be decoded satisfies a first condition.
  • the point cloud attribute encoding device if the point cloud to be encoded satisfies the first condition, the point cloud attribute encoding device will set the target parameter in the attribute header information of the point cloud to be encoded as a preset value. In this way, during the decoding process, the point cloud attribute decoding device can directly read the target parameter in the attribute header information of the point cloud to be decoded, and if the value corresponding to the target parameter is a preset value, it can directly determine that the point cloud to be decoded satisfies the first One condition.
  • the determining at least one prediction point corresponding to the point to be decoded based on the position of each point to be decoded in the point cloud to be decoded includes:
  • the preceding and following decoding points in the same decoding block are usually the nearest neighbor points.
  • the position of the point to be decoded can be used as the search center, the decoded point can be searched within the preset range of the point to be decoded, and the searched decoded point can be determined as the prediction point.
  • the aforementioned preset range refers to the decoded range corresponding to the point to be decoded.
  • the reconstruction attribute value corresponding to the point cloud to be decoded is obtained based on the second attribute prediction residual of the point to be decoded, the reconstruction attribute value of the prediction point and the weight value corresponding to the prediction point include:
  • a sum value result between the second attribute prediction residual and the predicted attribute value is determined as the reconstructed attribute value.
  • the reconstructed attribute value of the above prediction point is the attribute value corresponding to the decoded prediction point.
  • the weight value of the above prediction point is related to the geometric space position relationship between the prediction point and the point to be decoded. Specifically, how to determine the weight value corresponding to the prediction point For the technical solution, please refer to the following examples.
  • the predicted attribute value of the point to be decoded is determined according to the reconstructed attribute value and weight value, and the predicted attribute value is the attribute value obtained by performing attribute prediction decoding on the point to be decoded using the predicted point.
  • An optional implementation manner is to read the second attribute prediction residual of the point to be decoded, and use the sum of the second attribute prediction residual and the predicted attribute value as the reconstructed attribute value.
  • the determining the predicted attribute value of the point to be decoded according to the reconstructed attribute value of the predicted point and the weight value corresponding to the predicted point includes:
  • the product result of the reconstruction attribute value of the prediction point and the weight value corresponding to other prediction points is determined as the second target value corresponding to the prediction point;
  • the sum of the second target values corresponding to each predicted point is determined as the predicted attribute value of the point to be decoded.
  • the weight value corresponding to each prediction point may be determined based on the geometric spatial position relationship between the point to be decoded and each prediction point.
  • the weight value corresponding to each prediction point may be determined based on the geometric spatial position relationship between the point to be decoded and each prediction point.
  • the reconstruction attribute value of the prediction point is multiplied by the weight values corresponding to other prediction points, and the obtained multiplication result is used as the second target value corresponding to the prediction point. Further, the second target values of all prediction points corresponding to the point to be decoded are accumulated to obtain the predicted attribute value of the point to be decoded.
  • the determining the weight value corresponding to each prediction point based on the geometric spatial position relationship between the point to be decoded and each prediction point includes:
  • Numerical conversion is performed on the distance value to obtain a weight value corresponding to the predicted point.
  • For a predicted point calculate the distance between the predicted point and the point to be decoded. After obtaining the distance value between the prediction point and the point to be decoded, the distance value is numerically converted, and then the first norm corresponding to the distance value can be determined as the weight value corresponding to the prediction point, or the second norm corresponding to the distance value The number is determined as the weight value corresponding to the prediction point, or other numerical forms corresponding to the distance value are determined as the weight value corresponding to the prediction point.
  • the length of the space vector between the prediction point and the point to be decoded may be calculated, and based on the length, the weight value corresponding to the prediction point is obtained.
  • the point cloud attribute encoding method provided in the embodiment of the present application may be executed by a point cloud attribute encoding device.
  • the point cloud attribute encoding device provided in the embodiment of the present application is described by taking the point cloud attribute encoding device executing the point cloud attribute encoding method as an example.
  • the point cloud attribute encoding device 600 includes:
  • the first obtaining module 601 is used to obtain the point cloud to be coded
  • the first determination module 602 is configured to determine at least one prediction point corresponding to the point to be encoded based on the position of the point to be encoded in the point cloud to be encoded;
  • the second determination module 603 is configured to determine the predicted attribute value of the point to be encoded based on the reconstructed attribute value of the predicted point and the weight value corresponding to the predicted point;
  • the encoding module 604 is configured to perform quantization and entropy encoding on the prediction residual of the first attribute to obtain a target code stream.
  • the point cloud attribute encoding device 600 also includes:
  • a first determining unit configured to determine a first proportional value
  • the second determining unit is configured to set the target parameter in the attribute header information of the point cloud to be encoded as a preset value when the first ratio value is greater than or equal to a preset threshold.
  • the first determining module 602 is specifically configured to:
  • the second determining module 603 is also specifically configured to:
  • the product result of the reconstruction attribute value of the prediction point and the weight value corresponding to other prediction points is determined as the first target value corresponding to the prediction point;
  • the sum of the first target values corresponding to each predicted point is determined as the predicted attribute value of the point to be encoded.
  • the second determining module 603 is also specifically configured to:
  • Numerical conversion is performed on the distance value to obtain a weight value corresponding to the predicted point.
  • the point cloud to be encoded is obtained, and at least one predicted point corresponding to the point to be encoded is determined based on the position of the point to be encoded in the point cloud to be encoded; based on the reconstruction attribute value of the predicted point and the weight value corresponding to the predicted point, Determine the predicted attribute value of the point cloud to be encoded; perform quantization and entropy encoding on the first attribute prediction residual to obtain the target code stream.
  • point cloud attribute encoding process no reordering of the point cloud to be encoded is involved, so the geometric encoding order of the point cloud after geometric encoding will not be disrupted, and the nearest neighbor point of the point to be encoded can be determined based on the geometric encoding order. Attribute predictive coding is performed to improve coding efficiency.
  • the point cloud attribute encoding device provided by the embodiment of the present application can realize each process realized by the method embodiment in FIG. 3 and achieve the same technical effect. To avoid repetition, details are not repeated here.
  • the point cloud attribute decoding method provided in the embodiment of the present application may be executed by a point cloud attribute decoding device.
  • the point cloud attribute decoding device provided in the embodiment of the present application is described by taking the point cloud attribute decoding device executing the point cloud attribute decoding method as an example.
  • the point cloud attribute decoding device 700 includes:
  • the second obtaining module 701 is used to obtain the target code stream
  • the third determination module 702 is configured to determine at least one prediction point corresponding to the point to be decoded based on the position of the point to be decoded in the point cloud to be decoded;
  • the decoding module 703 is configured to obtain the reconstructed attribute value of the point to be encoded based on the second attribute prediction residual of the point to be decoded, the reconstructed attribute value of the predicted point, and the weight value corresponding to the predicted point.
  • the point cloud attribute decoding device 700 also includes:
  • An acquisition unit configured to acquire the target parameters in the attribute header information of the point cloud to be decoded
  • the third determination unit is configured to determine that the point cloud to be decoded satisfies the first condition when the value corresponding to the target parameter is a preset value.
  • the third determining module 702 is specifically configured to:
  • the decoding module 703 is specifically configured to:
  • a sum value result between the second attribute prediction residual and the predicted attribute value is determined as the reconstructed attribute value.
  • the decoding module 703 is specifically configured to:
  • the product result of the reconstruction attribute value of the prediction point and the weight value corresponding to other prediction points is determined as the second target value corresponding to the prediction point;
  • the sum of the second target values corresponding to each predicted point is determined as the predicted attribute value of the point to be decoded.
  • the decoding module 703 is specifically configured to:
  • Numerical conversion is performed on the distance value to obtain a weight value corresponding to the predicted point.
  • the target code stream is obtained, and the target code stream includes the point cloud to be decoded, and at least one prediction point corresponding to the point to be decoded is determined based on the position of the point to be decoded in the point cloud to be decoded; based on the second
  • the attribute prediction residual, the reconstructed attribute value of the predicted point, and the weight value corresponding to the predicted point are used to obtain the reconstructed attribute value of the point to be decoded.
  • the device for encoding point cloud attributes and the device for decoding point cloud attributes in the embodiments of the present application may be electronic equipment, or components in electronic equipment, such as integrated circuits or chips.
  • the electronic device may be a terminal, or other devices other than the terminal.
  • the terminal may include but not limited to the types of terminals listed above, and other devices may be servers, network attached storage (Network Attached Storage, NAS), etc., which are not specifically limited in this embodiment of the present application.
  • NAS Network Attached Storage
  • the point cloud attribute encoding device provided by the embodiment of the present application can realize each process realized by the method embodiment in FIG. 3 and achieve the same technical effect. To avoid repetition, details are not repeated here.
  • the point cloud attribute decoding device provided by the embodiment of the present application can realize each process realized by the method embodiment in FIG. 5 and achieve the same technical effect. To avoid repetition, details are not repeated here.
  • the embodiment of the present application also provides a communication device 800, including a processor 801 and a memory 802, and the memory 802 stores programs or instructions that can run on the processor 801, such as , when the communication device 800 is a terminal, when the program or instruction is executed by the processor 801, each step of the above-mentioned point cloud attribute encoding method embodiment can be realized, and the same technical effect can be achieved, or the above-mentioned point cloud attribute decoding method embodiment can be realized each step, and can achieve the same technical effect.
  • the embodiment of the present application also provides a terminal, including a processor and a communication interface, and the processor is configured to perform the following operations:
  • Quantization and entropy coding are performed on the prediction residual of the first attribute to obtain a target code stream.
  • the processor is used to:
  • the reconstructed attribute value of the predicted point, and the weight value corresponding to the predicted point is obtained.
  • FIG. 9 is a schematic diagram of a hardware structure of a terminal implementing an embodiment of the present application.
  • the terminal 900 includes, but is not limited to: a radio frequency unit 901, a network module 902, an audio output unit 903, an input unit 904, a sensor 905, a display unit 906, a user input unit 907, an interface unit 908, a memory 909, and a processor 910.
  • the terminal 900 can also include a power supply (such as a battery) for supplying power to various components, and the power supply can be logically connected to the processor 910 through the power management system, so as to manage charging, discharging, and power consumption through the power management system. Management and other functions.
  • a power supply such as a battery
  • the terminal structure shown in FIG. 9 does not constitute a limitation on the terminal, and the terminal may include more or fewer components than shown in the figure, or combine some components, or arrange different components, which will not be repeated here.
  • the input unit 904 may include a graphics processing unit (Graphics Processing Unit, GPU) 9041 and a microphone 9042, and the graphics processor 9041 is used in a video capture mode or an image capture mode by an image capture device (such as the image data of the still picture or video obtained by the camera) for processing.
  • the display unit 906 may include a display panel 9061, and the display panel 9061 may be configured in the form of a liquid crystal display, an organic light emitting diode, or the like.
  • the user input unit 907 includes at least one of a touch panel 9071 and other input devices 9072 .
  • the touch panel 9071 is also called a touch screen.
  • the touch panel 9071 may include two parts, a touch detection device and a touch controller.
  • Other input devices 9072 may include, but are not limited to, physical keyboards, function keys (such as volume control buttons, switch buttons, etc.), trackballs, mice, and joysticks, which will not be repeated here.
  • the radio frequency unit 901 may transmit the downlink data from the network side device to the processor 910 for processing after receiving the downlink data; in addition, the radio frequency unit 901 may send the uplink data to the network side device.
  • the radio frequency unit 901 includes, but is not limited to, an antenna, an amplifier, a transceiver, a coupler, a low noise amplifier, a duplexer, and the like.
  • the memory 909 can be used to store software programs or instructions as well as various data.
  • the memory 909 may mainly include a first storage area for storing programs or instructions and a second storage area for storing data, wherein the first storage area may store an operating system, an application program or instructions required by at least one function (such as a sound playing function, image playback function, etc.), etc.
  • memory 909 may include volatile memory or nonvolatile memory, or memory x09 may include both volatile and nonvolatile memory.
  • the non-volatile memory can be read-only memory (Read-Only Memory, ROM), programmable read-only memory (Programmable ROM, PROM), erasable programmable read-only memory (Erasable PROM, EPROM), electronically programmable Erase Programmable Read-Only Memory (Electrically EPROM, EEPROM) or Flash.
  • ROM Read-Only Memory
  • PROM programmable read-only memory
  • Erasable PROM Erasable PROM
  • EPROM erasable programmable read-only memory
  • Electrical EPROM Electrical EPROM
  • EEPROM electronically programmable Erase Programmable Read-Only Memory
  • Volatile memory can be random access memory (Random Access Memory, RAM), static random access memory (Static RAM, SRAM), dynamic random access memory (Dynamic RAM, DRAM), synchronous dynamic random access memory (Synchronous DRAM, SDRAM), double data rate synchronous dynamic random access memory (Double Data Rate SDRAM, DDRSDRAM), enhanced synchronous dynamic random access memory (Enhanced SDRAM, ESDRAM), synchronous connection dynamic random access memory (Synch link DRAM , SLDRAM) and Direct Memory Bus Random Access Memory (Direct Rambus RAM, DRRAM).
  • RAM Random Access Memory
  • SRAM static random access memory
  • DRAM dynamic random access memory
  • DRAM synchronous dynamic random access memory
  • SDRAM double data rate synchronous dynamic random access memory
  • Double Data Rate SDRAM Double Data Rate SDRAM
  • DDRSDRAM double data rate synchronous dynamic random access memory
  • Enhanced SDRAM, ESDRAM enhanced synchronous dynamic random access memory
  • Synch link DRAM , SLDRAM
  • Direct Memory Bus Random Access Memory Direct Rambus
  • the processor 910 may include one or more processing units; optionally, the processor 910 integrates an application processor and a modem processor, wherein the application processor is mainly involved in the operation of the operating system, user interface, and application programs, and the modulation and demodulation processor
  • the demodulation processor mainly processes wireless communication signals, such as a baseband processor. It can be understood that the foregoing modem processor may not be integrated into the processor 910 .
  • the processor is used to perform the following operations:
  • Quantization and entropy coding are performed on the prediction residual of the first attribute to obtain a target code stream.
  • the point cloud to be encoded is obtained, and at least one predicted point corresponding to the point to be encoded is determined based on the position of the point to be encoded in the point cloud to be encoded; based on the reconstruction attribute value of the predicted point and the weight value corresponding to the predicted point, Determine the predicted attribute value of the point cloud to be encoded; perform quantization and entropy encoding on the first attribute prediction residual to obtain the target code stream.
  • point cloud attribute encoding process no reordering of the point cloud to be encoded is involved, so the geometric encoding order of the point cloud after geometric encoding will not be disrupted, and the nearest neighbor point of the point to be encoded can be determined based on the geometric encoding order. Attribute predictive coding is performed to improve coding efficiency.
  • processor 910 is configured to perform the following operations:
  • the reconstructed attribute value of the predicted point, and the weight value corresponding to the predicted point is obtained.
  • the target code stream is obtained, and the target code stream includes the point cloud to be decoded, and at least one prediction point corresponding to the point to be decoded is determined based on the position of the point to be decoded in the point cloud to be decoded; based on the second
  • the attribute prediction residual, the reconstructed attribute value of the predicted point, and the weight value corresponding to the predicted point are used to obtain the reconstructed attribute value of the point to be decoded.
  • the embodiment of the present application also provides a readable storage medium, the readable storage medium stores a program or an instruction, and when the program or instruction is executed by a processor, each process of the above-mentioned point cloud attribute encoding method embodiment is realized, or,
  • the various processes of the above-mentioned embodiment of the point cloud attribute decoding method can achieve the same technical effect, so in order to avoid repetition, details are not repeated here.
  • the processor is the processor in the terminal described in the foregoing embodiments.
  • the readable storage medium includes a computer readable storage medium, such as a computer read-only memory ROM, a random access memory RAM, a magnetic disk or an optical disk, and the like.
  • the embodiment of the present application further provides a chip, the chip includes a processor and a communication interface, the communication interface is coupled to the processor, and the processor is used to run programs or instructions to realize the implementation of the above point cloud attribute encoding method
  • the chip includes a processor and a communication interface
  • the communication interface is coupled to the processor
  • the processor is used to run programs or instructions to realize the implementation of the above point cloud attribute encoding method
  • the chip mentioned in the embodiment of the present application may also be called a system-on-chip, a system-on-chip, a system-on-a-chip, or a system-on-a-chip.
  • the embodiment of the present application further provides a computer program product, the computer program product is stored in a storage medium, and the computer program product is executed by at least one processor to implement the various processes in the above embodiment of the point cloud attribute encoding method, Or realize each process of the above-mentioned point cloud attribute decoding method embodiment, and can achieve the same technical effect, in order to avoid repetition, no more details here.
  • the term “comprising”, “comprising” or any other variation thereof is intended to cover a non-exclusive inclusion such that a process, method, article or apparatus comprising a set of elements includes not only those elements, It also includes other elements not expressly listed, or elements inherent in the process, method, article, or device. Without further limitations, an element defined by the phrase “comprising a " does not preclude the presence of additional identical elements in the process, method, article, or apparatus comprising that element.
  • the scope of the methods and devices in the embodiments of the present application is not limited to performing functions in the order shown or discussed, and may also include performing functions in a substantially simultaneous manner or in reverse order according to the functions involved. Functions are performed, for example, the described methods may be performed in an order different from that described, and various steps may also be added, omitted, or combined. Additionally, features described with reference to certain examples may be combined in other examples.
  • the methods of the above embodiments can be implemented by means of software plus a necessary general-purpose hardware platform, and of course also by hardware, but in many cases the former is better implementation.
  • the technical solution of the present application can be embodied in the form of computer software products, which are stored in a storage medium (such as ROM/RAM, magnetic disk, etc.) , CD-ROM), including several instructions to make a terminal (which may be a mobile phone, a computer, a server, an air conditioner, or a network device, etc.) execute the methods described in the various embodiments of the present application.

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Abstract

本申请公开了一种点云属性编码方法、点云属性解码方法及终端，属于点云处理技术领域，本申请实施例的点云属性编码方法包括：获取待编码点云（S101）；基于待编码点云中待编码点的位置，确定待编码点对应的至少一个预测点（S102）；基于预测点的重建属性值和预测点对应的权重值，确定待编码点的预测属性值（S103）；对第一属性预测残差进行量化和熵编码，得到目标码流（S104），第一属性预测残差基于待编码点的真实属性值和预测属性值确定。

Description

点云属性编码方法、点云属性解码方法及终端
相关申请的交叉引用
本申请主张在2021年12月03日在中国提交的中国专利申请No.202111465642.5的优先权，其全部内容通过引用包含于此。
技术领域
本申请属于点云处理技术领域，具体涉及一种点云属性编码方法、点云属性解码方法及终端。
背景技术
点云是空间中一组无规则分布的、表达三维物体或场景的空间结构及表面属性的离散点集。
在对点云进行几何编码之后，对点云进行属性编码，其中，点云的属性编码包括属性预测编码。在属性预测编码过程中，需要对点云的几何信息进行重排序，打乱了对点云进行几何编码后的几何编码顺序，需要通过较为复杂的方式确定待编码点对应的最近邻居点以进行属性预测编码，降低了编码效率。此外，点云的属性解码过程与点云的属性编码过程对应，在属性解码过程中对点云进行的重排序也降低了点云的属性解码效率。
发明内容
本申请实施例提供一种点云属性编码方法、点云属性解码方法及终端，能够解决在属性预测编码过程中，对点云的几何信息进行重排序的操作降低了点云的属性编解码效率的技术问题。
第一方面，提供了一种点云属性编码方法，该方法包括：
编码端获取待编码点云；
所述编码端基于所述待编码点云中待编码点的位置，确定所述待编码点对应的至少一个预测点；
所述编码端基于所述预测点的重建属性值和所述预测点对应的权重值，确定所述待编码点云的预测属性值；
所述编码端对第一属性预测残差进行量化和熵编码，得到目标码流，所述第一属性预测残差基于所述待编码点的真实属性值和所述预测属性值确定。
第二方面，提供了一种点云属性编码装置，包括：
第一获取模块，用于获取待编码点云；
第一确定模块，用于基于所述待编码点云中每个待编码点的位置，确定所述待编码点对应的至少一个预测点；
第二确定模块，用于基于所述预测点的重建属性值和所述预测点对应的权重值，确定所述待编码点云的预测属性值；
编码模块，用于对第一属性预测残差进行量化和熵编码，得到目标码流，所述第一属性预测残差基于所述待编码点的真实属性值和所述预测属性值确定。
第三方面，提供了一种点云属性解码方法，该方法包括：
解码端获取目标码流，所述目标码流包括待解码点云；
所述解码端基于所述待解码点云中待解码点的位置，确定所述待解码点对应的至少一个预测点；
解码端基于所述待解码点的第二属性预测残差、所述预测点的重建属性值和所述预测点对应的权重值，得到所述待解码点的重建属性值。
第四方面，提供了一种点云属性解码装置，包括：
第二获取模块，用于获取目标码流，所述目标码流包括待解码点云；
第三确定模块，用于基于所述待解码点云中待解码点的位置，确定所述待解码点对应的至少一个预测点；
解码模块，用于基于所述待解码点的第二属性预测残差、所述预测点的重建属性值和所述预测点对应的权重值，得到所述待解码点的重建属性值。
第五方面，提供了一种通信设备，该通信设备包括处理器和存储器，所述存储器存储可在所述处理器上运行的程序或指令，所述程序或指令被所述 处理器执行时实现如第一方面所述的点云属性编码方法的步骤，或者实现如第三方面所述的点云属性解码方法的步骤。
第六方面，提供了一种可读存储介质，所述可读存储介质上存储程序或指令，所述程序或指令被处理器执行时实现如第一方面所述的点云属性编码方法的步骤，或者实现如第三方面所述的点云属性解码方法的步骤。
第七方面，提供了一种芯片，所述芯片包括处理器和通信接口，所述通信接口和所述处理器耦合，所述处理器用于运行程序或指令，实现如第一方面所述的点云属性编码方法，或者实现如第三方面所述的点云属性解码方法的步骤。
第八方面，提供了一种计算机程序产品，所述计算机程序产品被存储在存储介质中，所述计算机程序产品被至少一个处理器执行以实现如第一方面所述的点云属性编码方法的步骤，或者实现如第三方面所述的点云属性解码方法的步骤。
第九方面，提供了一种电子设备，被配置为执行以实现如第一方面所述的方法的步骤。
第十方面，提供了一种电子设备，被配置为执行以实现如第三方面所述的方法的步骤。
本申请实施例中，获取待编码点云，基于待编码点云中待编码点的位置，确定待编码点对应的至少一个预测点；基于预测点的重建属性值和预测点对应的权重值，确定待编码点云的预测属性值；对第一属性预测残差进行量化和熵编码，得到目标码流。上述点云属性编码过程中，不涉及对待编码点云进行重排序，因此不会打乱点云进行几何编码后的几何编码顺序，进而可以基于该几何编码顺序确定待编码点的最近邻居点以进行属性预测编码，以此提高了编码效率。
附图说明
图1是点云AVS点云属性编码装置框架示意图；
图2是点云AVS点云属性解码装置框架示意图；
图3是本申请实施例提供的点云属性编码方法的流程图；
图4是本申请实施例提供的点云属性编码方法的应用场景图；
图5是本申请实施例提供的点云属性解码方法的流程图；
图6是本申请实施例提供的点云属性编码装置的结构图；
图7是本申请实施例提供的点云属性解码装置的结构图；
图8是本申请实施例提供的通信设备的结构图；
图9是本申请实施例提供的终端的硬件结构示意图。
具体实施方式
下面将结合本申请实施例中的附图，对本申请实施例中的技术方案进行清楚描述，显然，所描述的实施例是本申请一部分实施例，而不是全部的实施例。基于本申请中的实施例，本领域普通技术人员所获得的所有其他实施例，都属于本申请保护的范围。
本申请的说明书和权利要求书中的术语“第一”、“第二”等是用于区别类似的对象，而不用于描述特定的顺序或先后次序。应该理解这样使用的术语在适当情况下可以互换，以便本申请的实施例能够以除了在这里图示或描述的那些以外的顺序实施，且“第一”、“第二”所区别的对象通常为一类，并不限定对象的个数，例如第一对象可以是一个，也可以是多个。此外，说明书以及权利要求中“和/或”表示所连接对象的至少其中之一，字符“/”一般表示前后关联对象是一种“或”的关系。
本申请实施例中的点云属性编码方法对应的点云属性编码装置和点云属性解码方法对应的点云属性解码装置均可以为终端，该终端也可以称作终端设备或者用户终端(User Equipment，UE)，终端可以是手机、平板电脑(Tablet Personal Computer)、膝上型电脑(Laptop Computer)或称为笔记本电脑、个人数字助理(Personal Digital Assistant，PDA)、掌上电脑、上网本、超级移动个人计算机(ultra-mobile personal computer，UMPC)、移动上网装置(Mobile  Internet Device，MID)、增强现实(augmented reality，AR)/虚拟现实(virtual reality，VR)设备、机器人、可穿戴式设备(Wearable Device)或车载设备(Vehicle User Equipment，VUE)、行人终端(Pedestrian User Equipment，PUE)、智能家居(具有无线通信功能的家居设备，如冰箱、电视、洗衣机或者家具等)、游戏机、个人计算机(personal computer，PC)、柜员机或者自助机等终端侧设备，可穿戴式设备包括：智能手表、智能手环、智能耳机、智能眼镜、智能首饰(智能手镯、智能手链、智能戒指、智能项链、智能脚镯、智能脚链等)、智能腕带、智能服装等。需要说明的是，在本申请实施例并不限定终端11的具体类型。
为了方便理解，以下对本申请实施例涉及的一些内容进行说明：
请参阅图1，如图1所示，目前，在数字音视频编解码技术标准中，使用点云信源编码标准(Audio Video coding Standard，AVS)点云属性编码装置对点云的几何信息和属性信息是分开编码的。首先对几何信息进行坐标转换，使点云全部包含在一个包围盒(bounding box)中，然后再进行坐标量化。量化主要起到缩放的作用，由于量化会对几何坐标取整，使得一部分点的几何信息相同，称为重复点，根据参数来决定是否移除重复点，量化和移除重复点这两个步骤又被称为体素化过程。接下来，对包围盒进行多叉树划分，例如八叉树、四叉树或二叉树划分。在基于多叉树的几何信息编码框架中，将包围盒八等分为8个子立方体，对非空的子立方体继续进行划分，直到划分得到叶子节点为1x1x1的单位立方体时停止划分，对叶子结点中的点数进行编码，生成二进制码流。
在对点云进行基于多叉树的几何编码中，待编码点需要存储邻居节点的占位信息来为待编码点的占位信息进行预测编码，这样，对于接近叶子节点的待编码点而言，需要存储大量的占位信息，占用了大量的内存空间。
几何编码完成后，对几何信息进行重建，用于后面的重着色。属性编码主要针对的是颜色和反射率信息。首先根据参数判断是否进行颜色空间转换，若进行颜色空间转换，则将颜色信息从红绿蓝(Red Green Blue，RGB)颜色 空间转换到亮度色彩(YUV)颜色空间。然后，利用原始点云对几何重建点云进行重着色，使得未编码的属性信息与重建的几何信息对应起来。在颜色信息编码中，通过莫顿码或希尔伯特码对点云进行排序后，利用几何空间关系搜索待预测点的最近邻，并利用所找到邻居的重建属性值对待预测点进行预测得到预测属性值，然后将真实属性值和预测属性值进行差分得到预测残差，最后对预测残差进行量化并编码，生成二进制码流。
应理解，数字音视频编解码技术标准中的解码流程与上述编码流程对应，具体的，AVS点云属性解码装置框架如图2所示。
目前，通用点云技术标准具有以下技术问题：
在通用点云技术标准中，对点云进行几何编码之后，再对点云进行属性编码。其中，在对点云进行属性预测编码的过程中，需要对点云的几何信息进行重排序，例如对点云进行希尔伯特码重排序，打乱了对点云的几何编码顺序，进而降低了编码效率。
基于上述情况，如何提高点云在属性预测编码的过程中的编码效率，是一个待以解决的技术问题。
为了解决上述可能存在的技术问题，本申请实施例提供了一种点云属性编码方法。下面结合附图，通过一些实施例及其应用场景对本申请实施例提供的点云属性编码方法进行详细地说明。
请参阅图3，图3是本申请提供的点云属性编码方法的流程图。本实施例提供的点云属性编码方法包括以下步骤：
S101，获取待编码点云。
本步骤中的待编码点云为进行过几何编码，具有几何编码顺序的点云；本步骤中的待编码点云为未进行重排序的点云，且本步骤中的待编码点云为经过重着色和颜色空间转换的点云。
上述重着色是指利用原始点云对几何重建点云进行重着色，使得未编码的属性信息与重建的几何信息对应起来，得到重着色后的点云；上述颜色空间转换是指将点云的颜色信息从RGB空间转换到YUV空间。
S102，在基于所述待编码点云中待编码点的位置，确定所述待编码点对应的至少一个预测点。
本步骤中，在待编码点云中的大部分编码块使用预测树编码的方式进行编码的情况下，由于在预测树编码中，同一编码块中的前后节点通常为最近邻居点，因此可以基于上述预测树编码的特性，根据待编码点云中待编码点的位置，确定待编码点对应的预测点，上述预测点即最近邻居点，且预测点为已编码点。
应理解，在待编码点云满足第一条件时，确定待编码点云中的大部分编码块使用预测树编码。具体如何确定待编码点云是否满足第一条件的技术方案，请参阅后续实施例。
其中，一个待编码点对应的预测点的数量大于或等于1。当待编码点为第一个编码点时，可以直接对该待编码点对应的预设的属性信息进行编码。
本步骤中，若待编码点云中的大部分编码块不使用预测树编码的方式进行编码，例如大部分编码块使用八叉树编码的方式进行编码，则使用通用点云技术标准中的属性预测编码方式，对待编码点云进行属性预测编码。
S103，基于所述预测点的重建属性值和所述预测点对应的权重值，确定所述待编码点云对应的预测预测值。
上述重建属性值为已编码的预测点对应的属性值。本步骤中，可以直接读取预测点的重建属性值，并计算预测点对应的权重值。具体的如何计算预测点对应的权重值的技术方案，请参阅后续实施例。
进一步的，对上述重建属性值和权重值进行相关计算，得到待编码点云对应的预测预测值。具体的如何确定待编码点云对应的预测预测值的技术方案，请参阅后续实施例。
S104，对第一属性预测残差进行量化和熵编码，得到目标码流。
所述第一属性预测残差基于所述待编码点的真实属性值和所述预测属性值确定
本步骤中，在得到待编码点的预测属性值后，可以将待编码点的真实属 性值与上述预测属性值的差值，确定为上述第一属性预测残差，对第一属性预测残差进行量化和熵编码，得到目标码流。其中，熵编码是在编码过程中按熵原理不丢失任何信息的一种编码方式，上述熵编码可以是香农编码、哈夫曼编码或者其他类型的编码，本实施例在此不做具体限定。上述目标码流为二进制码流。
本申请实施例中，获取待编码点云，基于待编码点云中待编码点的位置，确定待编码点对应的至少一个预测点；基于预测点的重建属性值和预测点对应的权重值，确定待编码点云对应的预测属性值；对第一属性预测残差进行量化和熵编码，得到目标码流。上述点云属性编码过程中，不涉及对待编码点云进行重排序，因此不会打乱点云进行几何编码后的几何编码顺序，进而可以基于该几何编码顺序确定待编码点的最近邻居点以进行属性预测编码，以此提高了编码效率。
在无损编码的条件下，本申请实施例提供的点云属性编码对点云的属性压缩效率，高于传统的点云属性编码对点云的属性压缩效率。为便于理解本申请产生的技术效果，请参阅表一。
表一：
Figure PCTCN2022135874-appb-000001
其中，表一中的“bpip ratio”又称为编码点的平均比特数比例，应理解，在无损编码的条件下，编码点的平均比特数比例的数值越低，说明编码方式 的属性压缩效率越高，编码性能越好。编码点的平均比特数比例包括总体比例、几何信息比例和属性信息比例，其中，表一中的“Total”也可以表示为总体比例，“Geometry”也可以表示为几何信息比例，“Colour”也可以表示为属性信息比例。
表一所示的码流1和码流2是应用本申请实施例提供的点云属性编码得到的码流，表一示出的码流3、码流4、码流5、码流6和码流7是传统的点云属性编码得到的码流。其中，本申请实施例提供的点云属性编码得到的码流对应的属性信息比例低于传统的点云属性编码得到的码流的属性信息比例。
可选地，所述获取待编码点云之后，所述方法包括：
确定第一比例值；
在所述第一比例值大于预设阈值的情况下，将待编码点云的属性头信息中的目标参数设置为预设值。
应理解，在点云的属性编码过程中，会对点云进行八叉树划分，得到多个编码块，判断点云对应的八叉树层级是否采用宏块编码，当采用宏块编码时，对点云采用宏块编码，并将点云对应的编码块个数确定为待编码点云采用宏块编码方法的宏块个数。本实施例中，可以将上述待编码点云采用宏块编码方法的宏块个数，确定为第二数值。
其中，点云包括参数lcu_node_depth，当对点云进行八叉树划分后，获取点云对应的八叉树层级octree_node_depth，在octree_node_depth小于lcu_node_depth的情况下，确定点云对应的八叉树层级采用宏块编码方法。
应理解，在点云采用宏块编码之后，判断每一个编码块是继续采用八叉树编码，还是采用预测树编码，并将点云中采用预测树方法编码的宏块个数，确定为第一数值。也就是说，将点云中采用预测树方法编码的编码块的个数，确定为第一数值。
其中，点云包括高密度系数geomTreeDensityHigh、低密度系数geomTreeDensityLow和最小点数限制参数geomPointTh。判断编码块是否采用预测树方法编码的方式为，计算编码块的密度，若该编码块的密度大于低 密度系数对应的数值且小于高密度系数对应的数值，且该编码块包括的编码点的数量大于最小点数限制参数对应的数值，则可以确定该编码块采用预测树方法编码。
进一步的，将第一数值与第二数值的商，确定为第一比例数值。
例如，点云达到八叉树划分的第二层级时，满足开启宏块编码的条件，由于在八叉树划分的第二层级将点云划分为8个编码块，因此可以确定第二数值为8，若上述8个编码块中有4个编码块采用了预测树编码，那么可以确定第一数值为4，第一比例数值为0.5。
本实施例中，设置有阈值，在所述第一比例值大于或等于阈值的情况下，表示点云中的大部分编码块使用预测树编码，将待编码点云的属性头信息中的目标参数设置为预设值。其中，上述阈值可以是一个经验阈值，也可以是用户自定义设置的一个阈值。
应理解，上述预设值用于指示待编码点云满足第一条件，即待编码点云中的大部分编码块使用预测树编码。其中，在待编码点云满足第一条件的情况下，待编码点对应的预测点基于待编码点云中待编码点的位置确定。
本实施例中，可以在待编码点云的属性头信息(Attribute Brick Header，ABH)中设置目标参数，该目标参数可以表示为use_pred_tree_code_attr，其中，上述预设值可以是1。应理解，在目标参数的赋值不为预设值，例如目标参数的赋值为0时，表示待编码点云中的大部分编码块不使用预测树编码。
可选地，所述基于所述待编码点云中每个待编码点的位置，确定所述待编码点对应的至少一个预测点包括：
以所述待编码点的位置为搜索中心，在所述待编码点的预设范围内搜索已编码点；
将搜索到的K个已编码点确定为所述待编码点对应的预测点。
如上所述，在大部分编码块使用预测树编码的情况下，同一编码块中的前后编码点通常为最近邻居点。这样，可以以待编码点的位置为搜索中心，在所述待编码点的预设范围内搜索已编码点，将搜索到的已编码点确定为预 测点。其中，上述预设范围是指待编码点对应的已编码范围。
示例性的，请参阅图4，图4中的编码点基于KD树的单链结构形成几何编码顺序，其中P0点为待编码点，在P0点对应的预设范围内搜索到P1、P2和P3 3个已编码点，将上述3个已编码点确定为预测点。
本实施例中，在待编码点云中的大部分编码块使用预测树编码的情况下，基于编码块不对编码块中的编码点进行重排序，而是基于编码块的几何编码顺序，确定预测点，以此提高编码效率。
可选地，所述根据所述预测点的重建属性值和所述预测点对应的权重值，确定所述待编码点的预测属性值包括：
基于所述待编码点与每个预测点之间的几何空间位置关系，确定所述每个预测点对应的权重值；
对于任一预测点，将所述预测点的重建属性值与其他预测点对应的权重值的乘积结果，确定为所述预测点对应的第一目标数值；
将每个预测点对应的第一目标数值的和值，确定为所述待编码点的预测属性值。
本实施例中，可以基于待编码点与每个预测点之间的几何空间位置关系，确定每个预测点对应的权重值，具体的技术方案请参阅后续实施例。
以下，具体阐述如何确定待编码点的预测属性值：
对于待编码点对应的每一个预测点，将该预测点的重建属性值与其他预测点对应的权重值做乘法运算，将得到的乘法结果作为该预测点对应的第一目标数值。进一步的，将该待编码点对应的所有预测点的第一目标数值累加，得到该待编码点的预测属性值。
为便于理解，以图4示出的实施场景为例进行说明。在图4示出的实施场景中，待编码点P0对应P1、P2和P3三个预测点，请参阅以下公式：
predAttr＝W2*W3*Attr1+W1*W3*Attr2+W1*W2*Attr3
其中，predAttr为待编码点的预测属性值，即P0的预测属性值；W1、W2和W3为预测点对应的权重值，其中，W1为P1对应的权重值，W2为 P2对应的权重值，W3为P3对应的权重值；Attr1、Attr2和Attr3为预测点对应的重建属性值，其中，Attr1为P1对应的重建属性值，Attr 2为P2对应的重建属性值，Attr 3为P3对应的重建属性值。可以通过上述公式，得到待编码点P0对应的预测属性值。
以下，具体说明如何确定每个预测点对应的权重值：
可选地，所述基于所述待编码点与每个预测点之间的几何空间位置关系，确定所述每个预测点对应的权重值包括：
对于任一预测点，计算所述预测点与所述待编码点之间的距离值；
对所述距离值进行数值转换，得到所述预测点对应的权重值。
对于一个预测点而言，计算预测点与所述待编码点之间的距离值，在可选地实施方式中，可以直接获取预测点与所述待编码点之间的距离值。在另一种可选地实施方式中，可以计算预测点与所述待编码点之间的空间向量的长度，进而基于该空间向量的长度，得到预测点对应的权重值。
在得到预测点与待编码点之间的距离值之后，对所述距离值进行数值转换，得到预测点对应的权重值。其中，上述对距离值进行数值转换的实质为，将该距离值对应的一范数确定为预测点对应的权重值，或者将该距离值对应的二范数确定为预测点对应的权重值，或者以距离值对应的其他数值表现形式确定为预测点对应的权重值。
下面结合附图，通过一些实施例及其应用场景对本申请实施例提供的点云属性解码方法进行详细地说明。
请参阅图5，图5是本申请提供的点云属性解码方法的流程图。本实施例提供的点云属性解码方法包括以下步骤：
S201，获取目标码流。
在属性预测解码过程中，直接将目标码流发送至点云属性解码装置，上述目标码流包括待解码点云。
S202，基于所述待解码点云中待解码点的位置，确定所述待解码点对应的至少一个预测点。
本步骤中，在待解码点云中的大部分解码块使用预测树解码的方式进行解码的情况下，可以基于上述预测树解码的特性，基于待解码点云中每个待解码点的位置，确定待解码点对应的预测点，上述预测点即最近邻居点，且预测点为已解码点。
应理解，在待解码点云满足第一条件时，确定待解码点云中的大部分解码块使用预测树解码。具体如何确定待解码点云是否满足第一条件的技术方案，请参阅后续实施例。
其中，一个待解码点对应的预测点的数量大于或等于1。当待解码点为第一个解码点时，可以直接对该待解码点对应的预设的属性信息进行解码。
本步骤中，若待解码点云中的大部分解码块不使用预测树解码的方式进行解码，这种情况下，使用通用点云技术标准中的属性预测解码方式，对待解码点云进行属性预测解码。
S203，基于所述待解码点的第二属性预测残差、所述预测点的重建属性值和所述预测点对应的权重值，得到所述待解码点云对应的重建属性值。
本步骤中，解码端对目标码流中的第一属性预测残差进行反量化处理，得到第二属性预测残差。可选地，可以直接读取待解码点的第一属性预测残差和预测点的重建属性值，并计算预测点对应的权重值。具体的如何计算预测点对应的权重值的技术方案，请参阅后续实施例。
进一步的，对上述第二属性预测残差、重建属性值和权重值进行相关计算，得到待解码点云对应的重建属性值。具体的如何确定待解码点云对应的重建属性值的技术方案，请参阅后续实施例。
本申请实施例中，获取目标码流，目标码流包括待解码点云，基于待解码点云中待解码点的位置，确定待解码点对应的至少一个预测点；基于待解码点的第二属性预测残差、预测点的重建属性值和预测点对应的权重值，得到待解码点的重建属性值。上述点云属性解码过程中，不涉及对待解码点云进行重排序，因此不会打乱点云进行几何解码后的几何解码顺序，进而可以基于该几何解码顺序确定待解码点的最近邻居点以进行属性预测解码，以此 提高解码效率。
可选地，所述获取待解码点云之后，所述方法包括：
获取所述待解码点云的属性头信息中的目标参数；
在所述目标参数对应的数值为预设值的情况下，确定所述待解码点云满足第一条件。
在上述编码过程中，若待编码点云满足第一条件，则点云属性编码装置会将待编码点云的属性头信息中的目标参数设置为预设值。这样，在解码过程中，点云属性解码装置可以直接读取待解码点云的属性头信息中的目标参数，若目标参数对应的数值为预设值，则可以直接确定待解码点云满足第一条件。
可选地，所述基于所述待解码点云中每个待解码点的位置，确定所述待解码点对应的至少一个预测点包括：
以所述待解码点的位置为搜索中心，在所述待解码点的预设范围内搜索已解码点；
将搜索到的K个已解码点确定为所述待解码点对应的预测点。
如上所述，在待解码点云中大部分解码块使用预测树解码的情况下，同一解码块中的前后解码点通常为最近邻居点。这样，可以以待解码点的位置为搜索中心，在所述待解码点的预设范围内搜索已解码点，将搜索到的已解码点确定为预测点。其中，上述预设范围是指待解码点对应的已解码范围。
可选地，所述基于所述待解码点的第二属性预测残差、所述预测点的重建属性值和所述预测点对应的权重值，得到所述待解码点云对应的重建属性值包括：
根据所述预测点的重建属性值和所述预测点对应的权重值，确定所述待解码点的预测属性值；
将所述第二属性预测残差与所述预测属性值之间的和值结果，确定为所述重建属性值。
上述预测点的重建属性值即已解码的预测点对应的属性值，上述预测点 的权重值与预测点与待解码点之间的几何空间位置关系相关，具体的如何确定预测点对应的权重值的技术方案，请参阅后续实施例。本实施例中，根据上述重建属性值和权重值，确定待解码点的预测属性值，上述预测属性值即使用预测点对待解码点进行属性预测解码得到的属性值。
可选地实施方式为，读取待解码点的第二属性预测残差，将第二属性预测残差与预测属性值的和值作为重建属性值。
可选地，所述根据所述预测点的重建属性值和所述预测点对应的权重值，确定所述待解码点的预测属性值包括：
基于所述待解码点与每个预测点之间的几何空间位置关系，确定所述每个预测点对应的权重值；
对于任一预测点，将所述预测点的重建属性值与其他预测点对应的权重值的乘积结果，确定为所述预测点对应的第二目标数值；
将每个预测点对应的第二目标数值的和值，确定为所述待解码点的预测属性值。
本实施例中，可以基于待解码点与每个预测点之间的几何空间位置关系，确定每个预测点对应的权重值，具体的技术方案请参阅后续实施例。
以下，具体阐述如何确定待解码点的预测属性值：
对于待解码点对应的每一个预测点，将该预测点的重建属性值与其他预测点对应的权重值做乘法运算，将得到的乘法结果作为该预测点对应的第二目标数值。进一步的，将该待解码点对应的所有预测点的第二目标数值累加，得到该待解码点的预测属性值。
可选地，所述基于所述待解码点与每个预测点之间的几何空间位置关系，确定所述每个预测点对应的权重值包括：
对于任一预测点，计算所述预测点与所述待解码点之间的距离值；
对所述距离值进行数值转换，得到所述预测点对应的权重值。
对于一个预测点而言，计算预测点与待解码点之间的距离值。在得到预测点与待解码点之间的距离值之后，对该距离值进行数值转换，进而可以将 距离值对应的一范数确定为预测点对应的权重值，或者将距离值对应的二范数确定为预测点对应的权重值，或者距离值对应的其他数值形式确定为预测点对应的权重值。
在另一可选地实施方式中，可以计算预测点与待解码点之间的空间向量的长度，基于该长度，得到预测点对应的权重值。
本申请实施例提供的点云属性编码方法，执行主体可以为点云属性编码装置。本申请实施例中以点云属性编码装置执行点云属性编码方法为例，说明本申请实施例提供的点云属性编码装置。
如图6所示，点云属性编码装置600包括：
第一获取模块601，用于获取待编码点云；
第一确定模块602，用于基于所述待编码点云中待编码点的位置，确定所述待编码点对应的至少一个预测点；
第二确定模块603，用于基于所述预测点的重建属性值和所述预测点对应的权重值，确定所述待编码点的预测属性值；
编码模块604，用于对第一属性预测残差进行量化和熵编码，得到目标码流。
可选地，所述点云属性编码装置600，还包括：
第一确定单元，用于确定第一比例值；
第二确定单元，用于在所述第一比例值大于或等于预设阈值的情况下，将待编码点云的属性头信息中的目标参数设置为预设值。
可选地，所述第一确定模块602，具体用于：
以所述待编码点的位置为搜索中心，在所述待编码点的预设范围内搜索已编码点；
将搜索到的K个已编码点确定为所述待编码点对应的预测点。
可选地，所述第二确定模块603，还具体用于：
基于所述待编码点与每个预测点之间的几何空间位置关系，确定所述每个预测点对应的权重值；
对于任一预测点，将所述预测点的重建属性值与其他预测点对应的权重值的乘积结果，确定为所述预测点对应的第一目标数值；
将每个预测点对应的第一目标数值的和值，确定为所述待编码点的预测属性值。
可选地，所述第二确定模块603，还具体用于：
对于任一预测点，计算所述预测点与所述待编码点之间的距离值；
对所述距离值进行数值转换，得到所述预测点对应的权重值。
本申请实施例中，获取待编码点云，基于待编码点云中待编码点的位置，确定待编码点对应的至少一个预测点；基于预测点的重建属性值和预测点对应的权重值，确定待编码点云的预测属性值；对第一属性预测残差进行量化和熵编码，得到目标码流。上述点云属性编码过程中，不涉及对待编码点云进行重排序，因此不会打乱点云进行几何编码后的几何编码顺序，进而可以基于该几何编码顺序确定待编码点的最近邻居点以进行属性预测编码，以此提高了编码效率。
本申请实施例提供的点云属性编码装置能够实现图3的方法实施例实现的各个过程，并达到相同的技术效果，为避免重复，这里不再赘述。
需要说明的是，本申请实施例提供的点云属性解码方法，执行主体可以为点云属性解码装置。本申请实施例中以点云属性解码装置执行点云属性解码方法为例，说明本申请实施例提供的点云属性解码装置。
如图7所示，点云属性解码装置700包括：
第二获取模块701，用于获取目标码流；
第三确定模块702，用于基于待解码点云中待解码点的位置，确定所述待解码点对应的至少一个预测点；
解码模块703，用于基于所述待解码点的第二属性预测残差、所述预测点的重建属性值和所述预测点对应的权重值，得到所述待编码点的重建属性值。
可选地，所述点云属性解码装置700，还包括：
获取单元，用于获取所述待解码点云的属性头信息中的目标参数；
第三确定单元，用于在所述目标参数对应的数值为预设值的情况下，确定所述待解码点云满足第一条件。
可选地，所述第三确定模块702，具体用于：
以所述待解码点的位置为搜索中心，在所述待解码点的预设范围内搜索已解码点；
将搜索到的K个已解码点确定为所述待解码点对应的预测点。
可选地，所述解码模块703，具体用于：
根据所述预测点的重建属性值和所述预测点对应的权重值，确定所述待解码点的预测属性值；
将所述第二属性预测残差与所述预测属性值之间的和值结果，确定为所述重建属性值。
可选地，所述解码模块703，具体用于：
基于所述待解码点与每个预测点之间的几何空间位置关系，确定所述每个预测点对应的权重值；
对于任一预测点，将所述预测点的重建属性值与其他预测点对应的权重值的乘积结果，确定为所述预测点对应的第二目标数值；
将每个预测点对应的第二目标数值的和值，确定为所述待解码点的预测属性值。
可选地，所述解码模块703，具体用于：
对于任一预测点，计算所述预测点与所述待解码点之间的距离值；
对所述距离值进行数值转换，得到所述预测点对应的权重值。
本申请实施例中，获取目标码流，目标码流包括待解码点云，基于待解码点云中待解码点的位置，确定待解码点对应的至少一个预测点；基于待解码点的第二属性预测残差、预测点的重建属性值和预测点对应的权重值，得到待解码点的重建属性值。上述点云属性解码过程中，不涉及对待解码点云进行重排序，因此不会打乱点云进行几何解码后的几何解码顺序，进而可以 基于该几何解码顺序确定待解码点的最近邻居点以进行属性预测解码，以此提高解码效率。
本申请实施例中的点云属性编码装置和点云属性解码装置可以是电子设备，也可以是电子设备中的部件，例如集成电路或芯片。该电子设备可以是终端，也可以为除终端之外的其他设备。示例性的，终端可以包括但不限于上述所列举的终端的类型，其他设备可以为服务器、网络附属存储器(Network Attached Storage，NAS)等，本申请实施例不作具体限定。
本申请实施例提供的点云属性编码装置能够实现图3的方法实施例实现的各个过程，并达到相同的技术效果，为避免重复，这里不再赘述。
本申请实施例提供的点云属性解码装置能够实现图5的方法实施例实现的各个过程，并达到相同的技术效果，为避免重复，这里不再赘述。
可选地，如图8所示，本申请实施例还提供一种通信设备800，包括处理器801，存储器802，存储器802上存储有可在所述处理器801上运行的程序或指令，例如，该通信设备800为终端时，该程序或指令被处理器801执行时实现上述点云属性编码方法实施例的各个步骤，且能达到相同的技术效果，或实现上述点云属性解码方法实施例的各个步骤，且能达到相同的技术效果。
本申请实施例还提供一种终端，包括处理器和通信接口，处理器用于执行以下操作：
获取待编码点云；
基于所述待编码点云中待编码点的位置，确定所述待编码点对应的至少一个预测点；
基于所述预测点的重建属性值和所述预测点对应的权重值，确定所述待编码点的预测属性值；
对第一属性预测残差进行量化和熵编码，得到目标码流。
或者，处理器用于执行以下操作：
获取目标码流；
基于待解码点云中待解码点的位置，确定所述待解码点对应的至少一个预测点；
基于所述待解码点的第二属性预测残差、所述预测点的重建属性值和所述预测点对应的权重值，得到所述待解码点的重建属性值。
该终端实施例与上述终端侧方法实施例对应，上述方法实施例的各个实施过程和实现方式均可适用于该终端实施例中，且能达到相同的技术效果。具体地，图9为实现本申请实施例的一种终端的硬件结构示意图。
该终端900包括但不限于：射频单元901、网络模块902、音频输出单元903、输入单元904、传感器905、显示单元906、用户输入单元907、接口单元908、存储器909以及处理器910等部件。
本领域技术人员可以理解，终端900还可以包括给各个部件供电的电源(比如电池)，电源可以通过电源管理系统与处理器910逻辑相连，从而通过电源管理系统实现管理充电、放电、以及功耗管理等功能。图9中示出的终端结构并不构成对终端的限定，终端可以包括比图示更多或更少的部件，或者组合某些部件，或者不同的部件布置，在此不再赘述。
应理解的是，本申请实施例中，输入单元904可以包括图形处理单元(Graphics Processing Unit，GPU)9041和麦克风9042，图形处理器9041对在视频捕获模式或图像捕获模式中由图像捕获装置(如摄像头)获得的静态图片或视频的图像数据进行处理。显示单元906可包括显示面板9061，可以采用液晶显示器、有机发光二极管等形式来配置显示面板9061。用户输入单元907包括触控面板9071以及其他输入设备9072中的至少一种。触控面板9071，也称为触摸屏。触控面板9071可包括触摸检测装置和触摸控制器两个部分。其他输入设备9072可以包括但不限于物理键盘、功能键(比如音量控制按键、开关按键等)、轨迹球、鼠标、操作杆，在此不再赘述。
本申请实施例中，射频单元901接收来自网络侧设备的下行数据后，可以传输给处理器910进行处理；另外，射频单元901可以向网络侧设备发送上行数据。通常，射频单元901包括但不限于天线、放大器、收发信机、耦 合器、低噪声放大器、双工器等。
存储器909可用于存储软件程序或指令以及各种数据。存储器909可主要包括存储程序或指令的第一存储区和存储数据的第二存储区，其中，第一存储区可存储操作系统、至少一个功能所需的应用程序或指令(比如声音播放功能、图像播放功能等)等。此外，存储器909可以包括易失性存储器或非易失性存储器，或者，存储器x09可以包括易失性和非易失性存储器两者。其中，非易失性存储器可以是只读存储器(Read-Only Memory，ROM)、可编程只读存储器(Programmable ROM，PROM)、可擦除可编程只读存储器(Erasable PROM，EPROM)、电可擦除可编程只读存储器(Electrically EPROM，EEPROM)或闪存。易失性存储器可以是随机存取存储器(Random Access Memory，RAM)，静态随机存取存储器(Static RAM，SRAM)、动态随机存取存储器(Dynamic RAM，DRAM)、同步动态随机存取存储器(Synchronous DRAM，SDRAM)、双倍数据速率同步动态随机存取存储器(Double Data Rate SDRAM，DDRSDRAM)、增强型同步动态随机存取存储器(Enhanced SDRAM，ESDRAM)、同步连接动态随机存取存储器(Synch link DRAM，SLDRAM)和直接内存总线随机存取存储器(Direct Rambus RAM，DRRAM)。本申请实施例中的存储器x09包括但不限于这些和任意其它适合类型的存储器。
处理器910可包括一个或多个处理单元；可选地，处理器910集成应用处理器和调制解调处理器，其中，应用处理器主要涉及操作系统、用户界面和应用程序等的操作，调制解调处理器主要处理无线通信信号，如基带处理器。可以理解的是，上述调制解调处理器也可以不集成到处理器910中。
其中，处理器用于执行以下操作：
获取待编码点云；
基于所述待编码点云中待编码点的位置，确定所述待编码点对应的至少一个预测点；
基于所述预测点的重建属性值和所述预测点对应的权重值，确定所述待编码点的预测属性值；
对第一属性预测残差进行量化和熵编码，得到目标码流。
本申请实施例中，获取待编码点云，基于待编码点云中待编码点的位置，确定待编码点对应的至少一个预测点；基于预测点的重建属性值和预测点对应的权重值，确定待编码点云的预测属性值；对第一属性预测残差进行量化和熵编码，得到目标码流。上述点云属性编码过程中，不涉及对待编码点云进行重排序，因此不会打乱点云进行几何编码后的几何编码顺序，进而可以基于该几何编码顺序确定待编码点的最近邻居点以进行属性预测编码，以此提高了编码效率。
或者，处理器910用于执行以下操作：
获取目标码流；
基于待解码点云中每个待解码点的位置，确定所述待解码点对应的至少一个预测点；
基于所述待解码点的第二属性预测残差、所述预测点的重建属性值和所述预测点对应的权重值，得到所述待解码点的重建属性值。
本申请实施例中，获取目标码流，目标码流包括待解码点云，基于待解码点云中待解码点的位置，确定待解码点对应的至少一个预测点；基于待解码点的第二属性预测残差、预测点的重建属性值和预测点对应的权重值，得到待解码点的重建属性值。上述点云属性解码过程中，不涉及对待解码点云进行重排序，因此不会打乱点云进行几何解码后的几何解码顺序，进而可以基于该几何解码顺序确定待解码点的最近邻居点以进行属性预测解码，以此提高了解码效率。
本申请实施例还提供一种可读存储介质，所述可读存储介质上存储有程序或指令，该程序或指令被处理器执行时实现上述点云属性编码方法实施例的各个过程，或者，实现上述点云属性解码方法实施例的各个过程且能达到相同的技术效果，为避免重复，这里不再赘述。
其中，所述处理器为上述实施例中所述的终端中的处理器。所述可读存储介质，包括计算机可读存储介质，如计算机只读存储器ROM、随机存取存 储器RAM、磁碟或者光盘等。
本申请实施例另提供了一种芯片，所述芯片包括处理器和通信接口，所述通信接口和所述处理器耦合，所述处理器用于运行程序或指令，实现上述点云属性编码方法实施例的各个过程，或实现上述点云属性解码方法实施例的各个过程，且能达到相同的技术效果，为避免重复，这里不再赘述。
应理解，本申请实施例提到的芯片还可以称为系统级芯片，系统芯片，芯片系统或片上系统芯片等。
本申请实施例另提供了一种计算机程序产品，所述计算机程序产品被存储在存储介质中，所述计算机程序产品被至少一个处理器执行以实现上述点云属性编码方法实施例的各个过程，或实现上述点云属性解码方法实施例的各个过程，且能达到相同的技术效果，为避免重复，这里不再赘述。
需要说明的是，在本文中，术语“包括”、“包含”或者其任何其他变体意在涵盖非排他性的包含，从而使得包括一系列要素的过程、方法、物品或者装置不仅包括那些要素，而且还包括没有明确列出的其他要素，或者是还包括为这种过程、方法、物品或者装置所固有的要素。在没有更多限制的情况下，由语句“包括一个……”限定的要素，并不排除在包括该要素的过程、方法、物品或者装置中还存在另外的相同要素。此外，需要指出的是，本申请实施方式中的方法和装置的范围不限按示出或讨论的顺序来执行功能，还可包括根据所涉及的功能按基本同时的方式或按相反的顺序来执行功能，例如，可以按不同于所描述的次序来执行所描述的方法，并且还可以添加、省去、或组合各种步骤。另外，参照某些示例所描述的特征可在其他示例中被组合。
通过以上的实施方式的描述，本领域的技术人员可以清楚地了解到上述实施例方法可借助软件加必需的通用硬件平台的方式来实现，当然也可以通过硬件，但很多情况下前者是更佳的实施方式。基于这样的理解，本申请的技术方案本质上或者说对现有技术做出贡献的部分可以以计算机软件产品的形式体现出来，该计算机软件产品存储在一个存储介质(如ROM/RAM、磁 碟、光盘)中，包括若干指令用以使得一台终端(可以是手机，计算机，服务器，空调器，或者网络设备等)执行本申请各个实施例所述的方法。
上面结合附图对本申请的实施例进行了描述，但是本申请并不局限于上述的具体实施方式，上述的具体实施方式仅仅是示意性的，而不是限制性的，本领域的普通技术人员在本申请的启示下，在不脱离本申请宗旨和权利要求所保护的范围情况下，还可做出很多形式，均属于本申请的保护之内。

Claims (19)

  1. 一种点云属性编码方法，包括：
    编码端获取待编码点云；
    所述编码端基于所述待编码点云中待编码点的位置，确定所述待编码点对应的至少一个预测点；
    所述编码端基于所述预测点的重建属性值和所述预测点对应的权重值，确定所述待编码点的预测属性值；
    所述编码端对第一属性预测残差进行量化和熵编码，得到目标码流，所述第一属性预测残差基于所述待编码点的真实属性值和所述预测属性值确定。
  2. 根据权利要求1所述的方法，其中，所述获取待编码点云之后，所述方法还包括：
    所述编码端确定第一比例值，所述第一比例值根据第一数值与第二数值的比值得到，所述第一数值用于表征所述待编码点云中采用预测树方法编码的宏块个数，所述第二数值用于表征所述待编码点云中采用宏块编码方法的宏块个数；
    所述编码端在所述第一比例值大于或等于阈值的情况下，将待编码点云的属性头信息中的目标参数设置为预设值，所述预设值用于指示所述待编码点云满足第一条件；
    其中，在所述待编码点云满足第一条件的情况下，所述待编码点对应的预测点基于所述待编码点云中待编码点的位置确定。
  3. 根据权利要求1所述的方法，其中，所述基于所述待编码点云中待编码点的位置，确定所述待编码点对应的至少一个预测点包括：
    所述编码端以所述待编码点的位置为搜索中心，在所述待编码点的预设范围内搜索已编码点；
    所述编码端将搜索到的K个已编码点确定为所述待编码点对应的预测点，K为正整数。
  4. 根据权利要求1所述的方法，其中，所述基于所述预测点的重建属性值和所述预测点对应的权重值，确定所述待编码点的预测属性值包括：
    所述编码端基于所述待编码点与每个预测点之间的几何空间位置关系，确定所述每个预测点对应的权重值；
    所述编码端对于任一预测点，将所述预测点的重建属性值与其他预测点对应的权重值的乘积结果，确定为所述预测点对应的第一目标数值；
    所述编码端将每个预测点对应的第一目标数值的和值，确定为所述待编码点的预测属性值。
  5. 根据权利要求4所述的方法，其中，所述基于所述待编码点与每个预测点之间的几何空间位置关系，确定所述每个预测点对应的权重值包括：
    所述编码端对于任一预测点，计算所述预测点与所述待编码点之间的距离值；
    所述编码端对所述距离值进行数值转换，得到所述预测点对应的权重值。
  6. 一种点云属性编码装置，包括：
    第一获取模块，用于获取待编码点云；
    第一确定模块，用于基于所述待编码点云中待编码点的位置，确定所述待编码点对应的至少一个预测点；
    第二确定模块，用于基于所述预测点的重建属性值和所述预测点对应的权重值，确定所述待编码点的预测属性值；
    编码模块，用于对第一属性预测残差进行量化和熵编码，得到目标码流，所述第一属性预测残差基于所述待编码点的真实属性值和所述预测属性值确定。
  7. 一种点云属性解码方法，包括：
    解码端获取目标码流，所述目标码流包括待解码点云；
    所述解码端基于所述待解码点云中待解码点的位置，确定所述待解码点对应的至少一个预测点；
    所述解码端基于所述待解码点的第二属性预测残差、所述预测点的重建 属性值和所述预测点对应的权重值，得到所述待解码点的重建属性值。
  8. 根据权利要求7所述的方法，其中，所述获取目标码流之后，所述方法还包括：
    所述解码端获取所述待解码点云的属性头信息中的目标参数；
    所述解码端在所述目标参数对应的数值为预设值的情况下，确定所述待解码点云满足第一条件；
    其中，在所述待解码点云满足第一条件的情况下，所述待解码点对应的预测点基于所述待解码点云中待解码点的位置确定。
  9. 根据权利要求7所述的方法，其中，所述基于所述待解码点云中待解码点的位置，确定所述待解码点对应的至少一个预测点包括：
    所述解码端以所述待解码点的位置为搜索中心，在所述待解码点的预设范围内搜索已解码点；
    所述解码端将搜索到的K个已解码点确定为所述待解码点对应的预测点，K为正整数。
  10. 根据权利要求7所述的方法，其中，所述基于所述待解码点的第二属性预测残差、所述预测点的重建属性值和所述预测点对应的权重值，得到所述待解码点的重建属性值包括：
    所述解码端根据所述预测点的重建属性值和所述预测点对应的权重值，确定所述待解码点的预测属性值；
    所述解码端将所述第二属性预测残差与所述预测属性值之间的和值结果，确定为所述重建属性值。
  11. 根据权利要求10所述的方法，其中，所述根据所述预测点的重建属性值和所述预测点对应的权重值，确定所述待解码点的预测属性值包括：
    所述解码端基于所述待解码点与每个预测点之间的几何空间位置关系，确定所述每个预测点对应的权重值；
    所述解码端对于任一预测点，将所述预测点的重建属性值与其他预测点对应的权重值的乘积结果，确定为所述预测点对应的第二目标数值；
    所述解码端将每个预测点对应的第二目标数值的和值，确定为所述待解码点的预测属性值。
  12. 根据权利要求11所述的方法，其中，所述基于所述待解码点与每个预测点之间的几何空间位置关系，确定所述每个预测点对应的权重值包括：
    所述解码端对于任一预测点，计算所述预测点与所述待解码点之间的距离值；
    所述解码端对所述距离值进行数值转换，得到所述预测点对应的权重值。
  13. 一种点云属性解码装置，包括：
    第二获取模块，用于获取目标码流，所述目标码流包括待解码点云；
    第三确定模块，用于基于所述待解码点云中待解码点的位置，确定所述待解码点对应的至少一个预测点；
    解码模块，用于基于所述待解码点的第二属性预测残差、所述预测点的重建属性值和所述预测点对应的权重值，得到所述待解码点的重建属性值。
  14. 一种通信设备，包括处理器和存储器，所述存储器存储可在所述处理器上运行的程序或指令，所述程序或指令被所述处理器执行时实现如权利要求1-5任一项所述的点云属性编码方法的步骤，或者实现如权利要求7-12所述的点云属性解码方法的步骤。
  15. 一种可读存储介质，所述可读存储介质上存储程序或指令，所述程序或指令被处理器执行时实现如权利要求1-5任一项所述的点云属性编码方法的步骤，或者实现如权利要求7-12所述的点云属性解码方法的步骤。
  16. 一种芯片，所述芯片包括处理器和通信接口，所述通信接口和所述处理器耦合，所述处理器用于运行程序或指令，实现如权利要求1-5任一项所述的点云属性编码方法的步骤，或者实现如权利要求7-12任一项所述的点云属性解码方法的步骤。
  17. 一种计算机程序产品，所述计算机程序产品被存储在存储介质中，所述计算机程序产品被至少一个处理器执行以实现如权利要求1-5任一项所述的点云属性编码方法的步骤，或者实现如权利要求7-12任一项所述的点云属 性解码方法的步骤。
  18. 一种电子设备，被配置为执行以实现如权利要求1-5任一项所述的点云属性编码方法的步骤。
  19. 一种电子设备，被配置为执行以实现如权利要求7-12任一项所述的点云属性解码方法的步骤。
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