Point cloud encoding processing method, decoding processing method and device

Abstract

The present application discloses a point cloud encoding and decoding method and device. The point cloud encoding method of an embodiment of the present application includes: determining the density information of the target point cloud; when the density information indicates that the target point cloud is a sparse point cloud and the nodes to be encoded corresponding to the target point cloud meet the isolated point encoding conditions, starting the isolated point encoding mode.

Description

Technical Field

[0001] The present invention relates to the technical field of point cloud processing, and in particular to a point cloud encoding processing method, a decoding processing method and a device. Background Art

[0002] In the Point cloud Reference Software Model (PCRM) V3.0 of the Audio Video coding Standard (AVS), when certain judgment conditions are met, the isolated point coding mode is directly entered. Specifically, when the isolated point coding mode identifier in the geometry header information is 1, and the sum of the number of Morton code bits to be encoded in the current node is greater than twice the number of directions that do not reach the minimum side length, and there is only one point in the current node, the isolated point identifier singlePointFlag takes a value of 1, indicating that the isolated point coding mode is directly entered. For dense point clouds, there are very few nodes that satisfy the condition of having only one point, and a large number of singlePointFlags of 0 are encoded. These code streams have no effect on the compression of the point cloud, resulting in poor performance. Summary of the invention

[0003] The embodiments of the present application provide a point cloud encoding processing method, a decoding processing method and a device, which can solve the problem that the existing isolated point encoding mode is difficult to quickly and effectively screen out suitable nodes to be encoded.

[0004] In a first aspect, a point cloud coding processing method is provided, comprising:

[0005] Determine the density information of the target point cloud;

[0006] When the density information indicates that the target point cloud is a sparse point cloud and the to-be-encoded nodes corresponding to the target point cloud meet the isolated point encoding condition, the isolated point encoding mode is turned on.

[0007] In a second aspect, a point cloud decoding processing method is provided, comprising:

[0008] Determine the density information of the target point cloud;

[0009] When the density information indicates that the target point cloud is a sparse point cloud and the to-be-decoded nodes corresponding to the target point cloud meet the isolated point decoding condition, the isolated point decoding mode is started.

[0010] In a third aspect, a point cloud coding processing device is provided, comprising:

[0011] The first determination module is used to determine the density information of the target point cloud;

[0012] The first processing module is used to start the isolated point coding mode when the density information indicates that the target point cloud is a sparse point cloud and the to-be-coded nodes corresponding to the target point cloud meet the isolated point coding condition.

[0013] In a fourth aspect, a point cloud decoding processing device is provided, comprising:

[0014] The second determination module is used to determine the density information of the target point cloud;

[0015] The second processing module is used to start the isolated point decoding mode when the density information indicates that the target point cloud is a sparse point cloud and the to-be-decoded node corresponding to the target point cloud meets the isolated point decoding condition.

[0016] In a fifth aspect, a point cloud coding processing device is provided, comprising a processor, a memory, and a program or instruction stored in the memory and executable on the processor, wherein the program or instruction, when executed by the processor, implements the steps of the method described in the first aspect.

[0017] In a sixth aspect, a point cloud coding processing device is provided, comprising a processor and a communication interface, wherein the processor is used to determine density information of a target point cloud; when the density information indicates that the target point cloud is a sparse point cloud, and the nodes to be encoded corresponding to the target point cloud meet isolated point coding conditions, an isolated point coding mode is turned on.

[0018] In the seventh aspect, a point cloud decoding processing device is provided, comprising a processor, a memory, and a program or instruction stored in the memory and executable on the processor, wherein the program or instruction, when executed by the processor, implements the steps of the method described in the second aspect.

[0019] In an eighth aspect, a point cloud decoding processing device is provided, comprising a processor and a communication interface, wherein the processor is used to determine density information of a target point cloud; when the density information indicates that the target point cloud is a sparse point cloud, and the node to be decoded corresponding to the target point cloud meets an isolated point decoding condition, an isolated point decoding mode is started.

[0020] In a ninth aspect, a readable storage medium is provided, on which a program or instruction is stored. When the program or instruction is executed by a processor, the steps of the method described in the first aspect are implemented, or the steps of the method described in the second aspect are implemented.

[0021] In the tenth aspect, a chip is provided, comprising a processor and a communication interface, wherein the communication interface is coupled to the processor, and the processor is used to run a program or instruction to implement the method described in the first aspect, or to implement the method described in the second aspect.

[0022] In the eleventh aspect, a computer program / program product is provided, wherein the computer program / program product is stored in a non-volatile storage medium, and the program / program product is executed by at least one processor to implement the steps of the method described in the first aspect or the second aspect.

[0023] In the embodiment of the present application, the density information of the target point cloud is first determined, and then when the density information indicates that the target point cloud is a sparse point cloud and the node to be encoded corresponding to the target point cloud meets the isolated point encoding condition, the isolated point encoding mode is turned on. In this way, suitable nodes for isolated point encoding can be quickly screened out through the above density information and the above isolated point encoding condition, thereby effectively ensuring the encoding performance. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Figure 1 A schematic diagram showing the structure of the point cloud AVS encoder framework;

[0025] Figure 2 A schematic diagram showing a flow chart of a point cloud coding processing method according to an embodiment of the present application;

[0026] Figure 3 A second flowchart of the point cloud coding processing method according to an embodiment of the present application is shown;

[0027] Figure 4 A schematic diagram showing a flow chart of a point cloud decoding processing method according to an embodiment of the present application;

[0028] Figure 5 A schematic diagram showing a module of a point cloud coding processing device according to an embodiment of the present application;

[0029] Figure 6 One of the structural block diagrams of the point cloud coding processing device according to an embodiment of the present application;

[0030] Figure 7 A second structural block diagram showing the point cloud coding processing device according to an embodiment of the present application;

[0031] Figure 8 A schematic diagram showing a module of a point cloud decoding and processing device according to an embodiment of the present application. DETAILED DESCRIPTION

[0032] The following will be combined with the drawings in the embodiments of the present application to clearly describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field belong to the scope of protection of this application.

[0033] The terms "first", "second", etc. in the specification and claims of the present application are used to distinguish similar objects, and are not used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable under appropriate circumstances, so that the embodiments of the present application can be implemented in an order other than those illustrated or described here, and the objects distinguished by "first" and "second" are generally of the same type, and the number of objects is not limited. For example, the first object can be one or more. In addition, "and / or" in the specification and claims represents at least one of the connected objects, and the character " / " generally represents that the objects associated with each other are in an "or" relationship.

[0034] The encoder corresponding to the point cloud encoding method and the decoder corresponding to the point cloud decoding method in the embodiment of the present application can both be terminals, which can also be called terminal equipment or user equipment (UE). 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, an ultra-mobile personal computer (ultra-mobile personal computer, UMPC), a mobile Internet device (Mobile Internet Device, MID), augmented reality (augmented reality, AR) / virtual reality (virtual reality, VR) equipment, a robot, a wearable device (Wearable Device) or a vehicle-mounted equipment (VUE), a pedestrian terminal (PUE) and other terminal-side devices, and the wearable device includes: smart watches, bracelets, headphones, glasses, etc. It should be noted that the specific type of the terminal is not limited in the embodiment of the present application.

[0035] In order to enable those skilled in the art to better understand the embodiments of the present application, the AVS codec framework is first described as follows.

[0036] like Figure 1As shown in the figure, in the point cloud AVS encoder framework, the geometric information of the point cloud and the attribute information corresponding to each point are encoded separately. First, the geometric information is transformed so that all the point clouds are contained in a bounding box. Then quantization is performed. This step of quantization mainly plays a role in scaling. Due to the quantization rounding, the geometric information of some points is the same. Whether to remove duplicate points is determined according to the parameters. The process of quantization and removal of duplicate points belongs to the preprocessing process. Next, the bounding box is divided (octree, quadtree or binary tree) in the order of breadth-first traversal, and the placeholder code of each node is encoded. In the octree-based geometric code framework, the bounding box is divided into sub-cubes in turn, and the non-empty (containing points in the point cloud) sub-cubes are divided again until the leaf node obtained by the division is a 1×1×1 unit cube. Then the division is stopped. Secondly, the number of points contained in the leaf node is encoded, and finally the encoding of the geometric octree is completed to generate a binary code stream. In the octree-based geometric decoding process, the decoder obtains the placeholder code of each node by continuously parsing in the order of breadth-first traversal, and continuously divides the nodes in turn until a 1×1×1 unit cube is obtained. The number of points contained in each leaf node is parsed, and finally the geometric reconstructed point cloud information is restored.

[0037] After the geometric coding is completed, the geometric information is reconstructed. At present, attribute coding mainly encodes color and reflectivity information. First, determine whether to perform color space conversion. If color space conversion is performed, the color information is converted from RGB color space to YUV color space. Then, the reconstructed point cloud is recolored using the original point cloud so that the uncoded attribute information corresponds to the reconstructed geometric information. In color information coding, it is divided into two modules: attribute prediction and attribute transformation. The attribute prediction process is as follows: first, the point cloud is reordered, and then differential prediction is performed. There are two reordering methods: Morton reordering and Hilbert reordering. For cat1A sequence and cat2 sequence, Hilbert reordering is performed on them; for cat1B sequence and cat3 sequence, Morton reordering is performed on them. The attribute prediction of the sorted point cloud is performed using a differential method, and finally the prediction residual is quantized and entropy encoded to generate a binary code stream. The attribute transformation process is as follows: first, wavelet transform is performed on the point cloud attributes and the transform coefficients are quantized; secondly, the attribute reconstruction value is obtained through inverse quantization and inverse wavelet transform; then the difference between the original attribute and the attribute reconstruction value is calculated to obtain the attribute residual and quantize it; finally, the quantized transform coefficients and attribute residuals are entropy encoded to generate a binary code stream.

[0038] The point cloud coding processing method provided in the embodiments of the present application is described in detail below through some embodiments in combination with the accompanying drawings.

[0039] like Figure 2 As shown, the embodiment of the present application provides a point cloud coding processing method, including:

[0040] Step 201: Determine the density information of the target point cloud.

[0041] In this step, the target point cloud is a point cloud sequence or a point cloud slice in a point cloud sequence.

[0042] Optionally, the target point cloud refers to a point cloud after preprocessing the target point cloud to be encoded, wherein the preprocessing includes at least one of coordinate translation, quantization processing and removal of duplicate points.

[0043] Here, by determining the density information of the target point cloud, it is convenient to subsequently determine whether to enable the isolated point coding mode according to the density information, that is, to determine whether to enable the isolated point coding mode in combination with the characteristics of the target point cloud itself.

[0044] Step 202: When the density information indicates that the target point cloud is a sparse point cloud and the to-be-encoded nodes corresponding to the target point cloud meet an isolated point encoding condition, an isolated point encoding mode is enabled.

[0045] As an optional implementation, the isolated point encoding condition includes:

[0046] The isolated point direct encoding mode identifier in the geometric header information (the target point cloud is a point cloud sequence) or the geometric slice header information (the target point cloud is a point cloud slice) corresponding to the target point cloud is a first preset value, for example, the first preset value is 1;

[0047] The sum of the number of Morton code bits to be encoded of the first target point is greater than a preset multiple (such as twice) of the second value, the first target point includes a point in the target point cloud that is located in the spatial block corresponding to the current node to be encoded, the second value is the number of first target side lengths in the spatial block corresponding to the current node to be encoded, the first target side length includes the side length in a preset direction, and the first target side length is less than the minimum side length in the preset direction. The preset direction here includes at least one of the X direction, the Y direction, and the Z direction.

[0048] For example, when the density information indicates that the average occupied volume of each point in the target point cloud is greater than a preset threshold, the target point cloud is determined to be a sparse point cloud, and when it is determined that the target point cloud meets the isolated point encoding conditions, the isolated point encoding mode is turned on.

[0049] In this implementation, the isolated point encoding mode includes encoding singlePointFlag. For example, if there is only one point in the spatial block corresponding to the current node to be encoded, the value of singlePointFlag is 1, otherwise, the value of singlePointFlag is 0. When the value of singlePointFlag is 1, the geometric coordinates of the point in the spatial block corresponding to the current node to be encoded are encoded.

[0050] As another optional implementation, the isolated point encoding condition includes:

[0051] The isolated point direct encoding mode identifier in the geometric header information (the target point cloud is a point cloud sequence) or the geometric slice header information (the target point cloud is a point cloud slice) corresponding to the target point cloud is a first preset value, for example, the first preset value is 1;

[0052] The sum of the number of Morton code bits to be encoded of the first target point is greater than a preset multiple (such as twice) of the second value, the first target point includes a point in the target point cloud that is located in the space block corresponding to the current node to be encoded, the second value is the number of first target side lengths in the space block corresponding to the current node to be encoded, the first target side length includes the side length in a preset direction, and the first target side length is less than the minimum side length in the preset direction. The preset direction here includes at least one of the X direction, the Y direction, and the Z direction;

[0053] There is only one point in the space block corresponding to the current node to be encoded.

[0054] In this implementation, the isolated point coding mode includes coding the geometric coordinates of a point in a spatial block corresponding to the current node to be coded.

[0055] The point cloud coding processing method of the embodiment of the present application first determines the density information of the target point cloud, and then turns on the isolated point coding mode when the density information indicates that the target point cloud is a sparse point cloud and the target point cloud satisfies the isolated point coding condition. In this way, the nodes that need to be coded for isolated points can be quickly screened out through the above density information and the above isolated point coding condition, thereby effectively ensuring the coding performance.

[0056] Optionally, determining density information of the target point cloud includes:

[0057] The density information of the target point cloud is determined according to the target volume corresponding to the target point cloud and the number of target points included in the target point cloud.

[0058] The target volume corresponding to the target point cloud may refer to the volume of a space block corresponding to the target point cloud, such as the volume of a bounding box of a point cloud slice.

[0059] Here, according to the target volume corresponding to the target point cloud and the number of target points contained in the target point cloud, the average occupied volume of each point in the target point cloud can be obtained, and then the density information of the target point cloud can be determined according to the average occupied volume of each point in the target point cloud.

[0060] Further optionally, determining the density information of the target point cloud according to the target volume corresponding to the target point cloud and the number of target points included in the target point cloud includes:

[0061] Dividing the target volume by the target number of points to obtain a first value;

[0062] The density information of the target point cloud is determined according to the relationship between the first value and a preset threshold.

[0063] Here, the first value is used to indicate the average occupied volume of each point in the target point cloud, and the density information of the target point cloud is determined according to the relationship between the average occupied volume of each point in the target point cloud and a preset threshold.

[0064] Optionally, determining the density information of the target point cloud according to the relationship between the first value and a preset threshold value includes at least one of the following:

[0065] When the first value is greater than the preset threshold, determining that the density information of the target point cloud is a sparse point cloud;

[0066] When the first value is less than or equal to the preset threshold, the density information of the target point cloud is determined to be a dense point cloud.

[0067] Here, when the average occupied volume of each point in the target point cloud is greater than a preset threshold, the density information of the target point cloud is determined to be a sparse point cloud; when the average occupied volume of each point in the target point cloud is less than or equal to the preset threshold, the density information of the target point cloud is determined to be a dense point cloud.

[0068] In one embodiment of the present application, firstly, the dimensions of the space block (bounding box) corresponding to the target point cloud in the three dimensions of x, y, and z are obtained, and the volume of the bounding box is calculated according to the dimensions in the three dimensions of x, y, and z, that is, the above-mentioned target volume, recorded as V; secondly, the number of points in the target point cloud is obtained, that is, N, assuming that the above-mentioned first value is represented by P, then P = V ÷ N; after obtaining P, P is compared with a preset threshold value Th, and the comparison result is assumed to be represented by a variable S, and the value of S is as follows:

[0069]

[0070] After obtaining the value of S, it is used as a condition to enable the isolated point encoding mode, such as Figure 3 As shown (assuming that the above-mentioned isolated point decoding conditions include three conditions), a first-in-first-out (FIFO) node queue is obtained according to the nodes occupied by the corresponding spatial blocks in the constructed tree structure. When the value of S is 1 and the above-mentioned isolated point encoding conditions are met, the isolated point encoding mode is entered, otherwise, the octree encoding mode is entered.

[0071] Optionally, the method of the embodiment of the present application further includes:

[0072] Adding the preset threshold or the index of the preset threshold to the geometry header information;

[0073] Alternatively, the preset threshold or the index of the preset threshold is added to the geometry header information;

[0074] The index of the preset threshold corresponds to a threshold in a preset threshold list, and the preset threshold list includes at least one threshold.

[0075] In the embodiment of the present application, the above-mentioned preset threshold may be a threshold agreed upon in advance by the encoding end and the decoding end, and the above-mentioned preset threshold may not be encoded in this case. Alternatively, the above-mentioned preset threshold may also be a value preset by the encoding end. In this case, the encoding end encodes the preset threshold and adds it to the geometry header information (the above-mentioned target point cloud is a point cloud sequence) or to the geometry header information (the above-mentioned target point cloud is a point cloud slice). Alternatively, the above-mentioned preset threshold may also be a threshold in a preset threshold list, and the encoding end and the decoding end are provided with the same preset threshold list. The encoding end encodes the index of the preset threshold selected by the user in the preset threshold list, and adds the index of the encoded preset threshold to the geometry header information (the above-mentioned target point cloud is a point cloud sequence) or to the geometry header information (the above-mentioned target point cloud is a point cloud slice), so that the decoding end decodes the index to obtain the corresponding preset threshold, and determines whether the target point cloud is a sparse point cloud based on the preset threshold.

[0076] Optionally, before determining whether the target point cloud is a sparse point cloud, the method further includes:

[0077] The target volume and the number of target points are obtained according to the geometry header information or geometry header information corresponding to the target point cloud.

[0078] When the target point cloud is a point cloud sequence, the target volume and the target number of points are obtained from the corresponding geometry header information; when the target point cloud is a point cloud slice, the target volume and the target number of points are obtained from the corresponding geometry slice header information.

[0079] Optionally, after determining the density information of the target point cloud, the following steps are further included:

[0080] A first identifier is added to the geometry header information or geometry header information corresponding to the target point cloud, where the first identifier is used to indicate density information of the target point cloud.

[0081] Specifically, when the above-mentioned first identifier is a first value (such as 1), it indicates that the density information of the target point cloud is a sparse point cloud, and when the above-mentioned first identifier is a second value (such as 0), it indicates that the density information of the target offset is a dense point cloud.

[0082] Here, after the encoding end determines that the density information of the target point cloud is a sparse point cloud, the first identifier is encoded and the encoded first identifier is added to the geometric header information (the target point cloud is a point cloud sequence) or the geometric slice header information (the target point cloud is a point cloud slice), so that the decoding end does not need to determine the density information and can directly determine the density information of the target point cloud based on the first identifier.

[0083] It should be noted that there are two aspects to evaluate the performance indicators of point cloud compression: one is the degree of distortion of the point cloud. The higher the degree of distortion, the worse the objective quality of point cloud reconstruction; the other is the size of the bit stream after compression. For lossless compression, that is, there is no distortion in the point cloud, so only the size of the bit stream after point cloud compression is considered; for lossy compression, both aspects must be considered. In both, the bit stream size can be measured by the number of bits output after encoding, and for the evaluation of the degree of distortion of the point cloud, PCRM provides two corresponding distortion evaluation algorithms.

[0084] Generally speaking, to evaluate the performance of a compression algorithm, the RD curve is usually used to compare the performance difference between two algorithms. The ideal goal of point cloud compression is that the bit rate becomes smaller and the PSNR, an indicator for measuring objective quality, becomes larger. However, this situation rarely occurs. Generally, the bit rate becomes lower than the original method, but the PSNR, that is, the quality of the point cloud, decreases, or the PSNR becomes higher, but the bit rate increases. In order to measure the quality of a new method in these two situations, an indicator that comprehensively considers the bit rate and PSNR is needed. The AVS Point Cloud Group uses BD-Rate to comprehensively evaluate the bit rate and objective quality of the point cloud compression algorithm, and refines it into two aspects: geometry and attributes: BD-GeomRate and BD-AttrRate. When the BD-Rate value is negative, it means that the performance of the new method is improved compared to the original method; and when the BD-Rate value is positive, it means that the performance of the new method is reduced compared to the original method. Depending on whether the error is calculated using mean square error or Hausdorff distance, there are two ways and results for calculating PSNR, and the corresponding BD-Rate also has two results, the one calculated using mean square error is recorded as D1, and the one calculated using hausdorff is recorded as D1-H. Table 1 is a performance comparison result between the method of the embodiment of the present application and PCRM V3.0 under lossy conditions. It can be seen that both the BD-GeomRate calculated using D1 and the BD-GeomRate calculated using D1-H are negative values, that is, the method of the embodiment of the present application can effectively improve the encoding performance.

[0085] Table 1

[0086]

[0087]

[0088] In the embodiment of the present application, the density information of the target point cloud is first determined, and then when the density information indicates that the target point cloud is a sparse point cloud and the target point cloud satisfies the isolated point coding condition, the isolated point coding mode is turned on. In this way, the nodes that need to be isolated point coded can be quickly screened out through the above density information and the above isolated point coding condition, so that the coding performance can be effectively guaranteed.

[0089] like Figure 4 As shown, the embodiment of the present application also provides a point cloud decoding processing method, including:

[0090] Step 401: Determine the density information of the target point cloud.

[0091] In this step, the target point cloud is a point cloud sequence or a point cloud slice in a point cloud sequence.

[0092] Here, by determining the density information of the target point cloud, it is convenient to subsequently determine whether to start the isolated point decoding mode according to the density information, that is, to determine whether to start the isolated point decoding mode in combination with the characteristics of the target point cloud itself.

[0093] Step 402: When the density information indicates that the target point cloud is a sparse point cloud and the to-be-decoded nodes corresponding to the target point cloud meet an isolated point decoding condition, an isolated point decoding mode is started.

[0094] As an optional implementation, the isolated point decoding condition includes:

[0095] The isolated point direct decoding mode identifier in the geometric header information corresponding to the target point cloud (the target point cloud is a point cloud sequence) or in the geometric slice header information (the target point cloud is a point cloud slice) is a second preset value, for example, the second preset value is 1;

[0096] The sum of the number of Morton code bits to be decoded of the second target point is greater than a preset multiple (such as twice) of the third value, the second target point includes a point in the target point cloud that is located in the spatial block corresponding to the current node to be decoded, the third value is the number of second target side lengths in the spatial block corresponding to the current node to be decoded, the second target side length includes the side length in a preset direction, and the second target side length is less than the minimum side length in the preset direction. The preset direction here includes at least one of the X direction, the Y direction, and the Z direction.

[0097] In this implementation, the isolated point decoding module includes decoding singlePointFlag, and when the value of singlePointFlag is 1, decoding the geometric coordinates of the point in the space block corresponding to the current node to be encoded.

[0098] As another optional implementation, the isolated point decoding condition includes:

[0099] The isolated point direct decoding mode identifier in the geometric header information corresponding to the target point cloud (the target point cloud is a point cloud sequence) or in the geometric slice header information (the target point cloud is a point cloud slice) is a second preset value, for example, the second preset value is 1;

[0100] The sum of the number of Morton codes to be decoded of the second target point is greater than a preset multiple (such as twice) of the third value, the second target point includes a point in the target point cloud that is located in the space block corresponding to the current node to be decoded, the third value is the number of second target side lengths in the space block corresponding to the current node to be decoded, the second target side length includes the side length in a preset direction, and the second target side length is less than the minimum side length in the preset direction. The preset direction here includes at least one of the X direction, the Y direction, and the Z direction;

[0101] There is only one point in the space block corresponding to the current node to be decoded.

[0102] In this implementation, the isolated point decoding mode includes decoding the geometric coordinates of a point within a spatial block corresponding to the current node to be decoded.

[0103] The point cloud decoding processing method of the embodiment of the present application first determines the density information of the target point cloud, and then starts the isolated point decoding mode when the density information indicates that the target point cloud is a sparse point cloud and the node to be decoded corresponding to the target point cloud meets the isolated point decoding condition. In this way, suitable nodes for isolated point decoding can be quickly screened out through the above density information and the above isolated point decoding condition, thereby effectively ensuring the decoding performance.

[0104] As a first optional implementation manner, the determining density information of the target point cloud includes:

[0105] The density information of the target point cloud is determined according to the target volume corresponding to the target point cloud and the number of target points included in the target point cloud.

[0106] The target volume corresponding to the target point cloud may refer to the volume of a space block corresponding to the target point cloud, such as the volume of a bounding box of a point cloud slice.

[0107] Here, according to the target volume corresponding to the target point cloud and the number of target points contained in the target point cloud, the average occupied volume of each point in the target point cloud can be obtained, and then the density information of the target point cloud can be determined according to the average occupied volume of each point in the target point cloud.

[0108] Optionally, determining density information of the target point cloud according to a target volume corresponding to the target point cloud and the number of target points included in the target point cloud includes:

[0109] Dividing the target volume by the target number of points to obtain a first value;

[0110] The density information of the target point cloud is determined according to the relationship between the first value and a preset threshold.

[0111] Here, the first value is used to indicate the average occupied volume of each point in the target point cloud, and the density information of the target point cloud is determined according to the relationship between the average occupied volume of each point in the target point cloud and a preset threshold.

[0112] Optionally, determining the density information of the target point cloud according to the relationship between the first value and a preset threshold value includes at least one of the following:

[0113] When the first value is greater than a preset threshold, determining that the density information of the target point cloud is a sparse point cloud;

[0114] When the first value is less than or equal to a preset threshold, the density information of the target point cloud is determined to be a dense point cloud.

[0115] Here, when the average occupied volume of each point in the target point cloud is greater than a preset threshold, the density information of the target point cloud is determined to be a sparse point cloud; when the average occupied volume of each point in the target point cloud is less than or equal to the preset threshold, the density information of the target point cloud is determined to be a dense point cloud.

[0116] In one embodiment of the present application, firstly, the dimensions of the space block (bounding box) corresponding to the target point cloud in the three dimensions of x, y, and z are obtained, and the volume of the bounding box is calculated according to the dimensions in the three dimensions of x, y, and z, that is, the above-mentioned target volume, recorded as V; secondly, the number of points in the target point cloud is obtained, that is, N, assuming that the above-mentioned first value is represented by P, then P = V ÷ N; after obtaining P, P is compared with a preset threshold value Th, and the comparison result is assumed to be represented by a variable S, and the value of S is as follows:

[0117]

[0118] After obtaining the value of S, it is used as a condition for starting the isolated point decoding mode. The isolated point decoding mode is entered only when the value of S is 1 and the above isolated point decoding conditions are met. Otherwise, the octree decoding mode is accessed.

[0119] Optionally, before determining the density information of the target point cloud according to the relationship between the first value and a preset threshold, the method further includes:

[0120] Obtaining the preset threshold or the index of the preset threshold in the geometry header information corresponding to the target point cloud;

[0121] Alternatively, the preset threshold or the index of the preset threshold is obtained in the geometric header information corresponding to the target point cloud;

[0122] The index of the preset threshold corresponds to a threshold in a preset threshold list, and the preset threshold list includes at least one threshold.

[0123] In the embodiment of the present application, the preset threshold may be a threshold agreed upon in advance by the encoder and the decoder. Alternatively, the preset threshold may also be a value preset by the encoder. In this case, the encoder encodes the preset threshold and adds it to the geometry header information (the target point cloud is a point cloud sequence) or to the geometry header information (the target point cloud is a point cloud slice), and the decoder decodes the geometry header information or the geometry header information to obtain the preset threshold. Alternatively, the preset threshold may also be a threshold in a preset threshold list. The encoder and the decoder are provided with the same preset threshold list. The encoder encodes the index of the preset threshold selected by the user in the preset threshold list, and adds the index of the encoded preset threshold to the geometry header information (the target point cloud is a point cloud sequence) or to the geometry header information (the target point cloud is a point cloud slice), and the decoder decodes the index to obtain the corresponding preset threshold, and determines whether the target point cloud is a sparse point cloud based on the preset threshold.

[0124] Optionally, before determining whether the target point cloud is a sparse point cloud, the method further includes:

[0125] The target volume and the number of target points are obtained according to the geometry header information or geometry header information corresponding to the target point cloud.

[0126] When the target point cloud is a point cloud sequence, the target volume and the target number of points are obtained from the corresponding geometry header information; when the target point cloud is a point cloud slice, the target volume and the target number of points are obtained from the corresponding geometry slice header information.

[0127] As a second optional implementation method, determining the density information of the target point cloud includes:

[0128] If the first identifier exists in the geometry header information or the geometry slice header information corresponding to the target point cloud, the density information of the target point cloud is determined according to the first identifier.

[0129] Specifically, when the above-mentioned first identifier is a first value (such as 1), it indicates that the density information of the target point cloud is a sparse point cloud, and when the above-mentioned first identifier is a second value (such as 0), it indicates that the density information of the target offset is a dense point cloud.

[0130] In this implementation, the decoding end can directly decode the first identifier to determine whether the density information of the target point cloud is a sparse point cloud or a dense point cloud, without having to judge the density information based on the target volume and target point number of the target point cloud.

[0131] The point cloud decoding processing method of the embodiment of the present application first determines the density information of the target point cloud, and then starts the isolated point decoding mode when the density information indicates that the target point cloud is a sparse point cloud and the node to be decoded corresponding to the target point cloud meets the isolated point decoding condition. In this way, suitable nodes for isolated point decoding can be quickly screened out through the above density information and the above isolated point decoding condition, thereby effectively ensuring the decoding performance.

[0132] It should be noted that the point cloud coding processing method provided in the embodiment of the present application can be executed by a point cloud coding processing device, or a control module in the point cloud coding processing device for executing the point cloud coding processing method. In the embodiment of the present application, the point cloud coding processing device provided in the embodiment of the present application is described by taking the point cloud coding processing device executing the point cloud coding processing method as an example.

[0133] like Figure 5 As shown, the embodiment of the present application further provides a point cloud coding processing device 500, comprising:

[0134] The first determination module 501 is used to determine the density information of the target point cloud;

[0135] The first processing module 502 is configured to enable an isolated point coding mode when the density information indicates that the target point cloud is a sparse point cloud and the to-be-coded nodes corresponding to the target point cloud meet an isolated point coding condition.

[0136] Optionally, the first determination module is used to determine density information of the target point cloud according to a target volume corresponding to the target point cloud and the number of target points included in the target point cloud.

[0137] Optionally, the first determining module includes:

[0138] A first determination submodule, configured to divide the target volume by the target number of points to obtain a first value;

[0139] The second determination submodule is used to determine the density information of the target point cloud according to the relationship between the first value and a preset threshold.

[0140] Optionally, the second determining submodule is used to perform at least one of the following:

[0141] When the first value is greater than the preset threshold, determining that the density information of the target point cloud is a sparse point cloud;

[0142] When the first value is less than or equal to the preset threshold, the density information of the target point cloud is determined to be a dense point cloud.

[0143] Optionally, the device of the embodiment of the present application further includes:

[0144] A first encoding module, used for adding the preset threshold or the index of the preset threshold to the geometry header information;

[0145] Alternatively, the preset threshold or the index of the preset threshold is added to the geometry header information;

[0146] The index of the preset threshold corresponds to a threshold in a preset threshold list, and the preset threshold list includes at least one threshold.

[0147] Optionally, the device of the embodiment of the present application further includes:

[0148] The first acquisition module is used to acquire the target volume and the target point number according to the geometric header information or geometric header information corresponding to the target point cloud before the first determination module determines whether the target point cloud is a sparse point cloud.

[0149] Optionally, the device of the embodiment of the present application further includes:

[0150] The second encoding module is used to add a first identifier to the geometric header information or geometric header information corresponding to the target point cloud after the first determination module determines the density information of the target point cloud, wherein the first identifier is used to indicate the density information of the target point cloud.

[0151] Optionally, the target point cloud is a point cloud sequence or a point cloud slice in the point cloud sequence.

[0152] In the embodiment of the present application, the density information of the target point cloud is first determined, and then when the density information indicates that the target point cloud is a sparse point cloud and the node to be decoded corresponding to the target point cloud meets the isolated point decoding condition, the isolated point decoding mode is turned on. In this way, suitable nodes for isolated point decoding can be quickly screened out through the above density information and the above isolated point decoding condition, thereby effectively ensuring the decoding performance.

[0153] The point cloud coding processing device in the embodiment of the present application can be a device, a device or an electronic device with an operating system, or a component, an integrated circuit, or a chip in a terminal. The device or electronic device can be a mobile terminal or a non-mobile terminal.

[0154] The point cloud coding processing device provided in the embodiment of the present application can achieve Figures 2 to 3 The various processes implemented in the method embodiment achieve the same technical effect and will not be described again here to avoid repetition.

[0155] Optional, such as Figure 6As shown, the embodiment of the present application also provides a point cloud coding processing device, including a processor 601, a memory 602, and a program or instruction stored in the memory 602 and executable on the processor 601. When the program or instruction is executed by the processor 601, each process of the above-mentioned point cloud coding processing method embodiment is implemented, and the same technical effect can be achieved. To avoid repetition, it will not be repeated here.

[0156] An embodiment of the present application also provides a point cloud coding processing device, including a processor and a communication interface, the processor being used to: determine the density information of the target point cloud; when the density information indicates that the target point cloud is a sparse point cloud and the nodes to be encoded corresponding to the target point cloud meet the isolated point coding conditions, start the isolated point coding mode.

[0157] Figure 7 A schematic diagram of the hardware structure of a point cloud coding processing device for realizing an embodiment of the present application is provided, wherein the point cloud coding processing device includes but is not limited to: a radio frequency unit 701, a network module 702, an audio output unit 703, an input unit 704, a sensor 705, a display unit 706, a user input unit 707, an interface unit 708, a memory 709, and at least some of the components of a processor 710.

[0158] Those skilled in the art will appreciate that the point cloud coding processing device 700 may also include a power source (such as a battery) for supplying power to each component, and the power source may be logically connected to the processor 710 through a power management system, thereby implementing functions such as managing charging, discharging, and power consumption management through the power management system. Figure 7 The structure shown in does not constitute a limitation on the point cloud coding processing device. The point cloud coding processing device may include more or fewer components than shown in the figure, or a combination of certain components, or a different arrangement of components, which will not be described in detail here.

[0159] It should be understood that in the embodiment of the present application, the input unit 704 may include a graphics processor (Graphics Processing Unit, GPU) 7041 and a microphone 7042, and the graphics processor 7041 processes the image data of the static picture or video obtained by the image capture device (such as a camera) in the video capture mode or the image capture mode. The display unit 706 may include a display panel 7061, and the display panel 7061 may be configured in the form of a liquid crystal display, an organic light emitting diode, etc. The user input unit 707 includes a touch panel 7071 and other input devices 7072. The touch panel 7071 is also called a touch screen. The touch panel 7071 may include two parts: a touch detection device and a touch controller. Other input devices 7072 may include, but are not limited to, a physical keyboard, function keys (such as volume control keys, switch keys, etc.), a trackball, a mouse, and a joystick, which will not be repeated here.

[0160] In the embodiment of the present application, generally, the radio frequency unit 701 includes but is not limited to an antenna, at least one amplifier, a transceiver, a coupler, a low noise amplifier, a duplexer, and the like.

[0161] The memory 709 can be used to store software programs or instructions and various data. The memory 709 can mainly include a program or instruction storage area and a data storage area, wherein the program or instruction storage area can store an operating system, an application program or instruction required for at least one function (such as a sound playback function, an image playback function, etc.), etc. In addition, the memory 709 can include a high-speed random access memory, and can also include a non-volatile memory, wherein the non-volatile memory can be a read-only memory (Read-Only Memory, ROM), a programmable read-only memory (Programmable ROM, PROM), an erasable programmable read-only memory (Erasable PROM, EPROM), an electrically erasable programmable read-only memory (Electrically EPROM, EEPROM) or a flash memory. For example, at least one disk storage device, a flash memory device, or other non-volatile solid-state storage devices.

[0162] The processor 710 may include one or more processing units; optionally, the processor 710 may integrate an application processor and a modem processor, wherein the application processor mainly processes an operating system, a user interface, and application programs or instructions, etc., and the modem processor mainly processes wireless communications, such as a baseband processor. It is understandable that the modem processor may not be integrated into the processor 710.

[0163] Optionally, the processor 710 is used to determine density information of the target point cloud; when the density information indicates that the target point cloud is a sparse point cloud and the nodes to be encoded corresponding to the target point cloud meet the isolated point coding conditions, the isolated point coding mode is turned on.

[0164] Optionally, the processor 710 is used to determine density information of the target point cloud according to a target volume corresponding to the target point cloud and the number of target points included in the target point cloud.

[0165] Optionally, the processor 710 is configured to divide the target volume by the target number of points to obtain a first value;

[0166] The density information of the target point cloud is determined according to the relationship between the first value and a preset threshold.

[0167] Optionally, the processor 710 is configured to perform at least one of the following:

[0168] When the first value is greater than the preset threshold, determining that the density information of the target point cloud is a sparse point cloud;

[0169] When the first value is less than or equal to the preset threshold, the density information of the target point cloud is determined to be a dense point cloud.

[0170] Optionally, the processor 710 is configured to add the preset threshold or an index of the preset threshold to geometry header information;

[0171] Alternatively, the preset threshold or the index of the preset threshold is added to the geometry header information;

[0172] The index of the preset threshold corresponds to a threshold in a preset threshold list, and the preset threshold list includes at least one threshold.

[0173] Optionally, the processor 710 is used to obtain the target volume and the number of target points according to geometry header information or geometry header information corresponding to the target point cloud before determining whether the target point cloud is a sparse point cloud.

[0174] Optionally, the processor 710 is used to add a first identifier to the geometry header information or geometry header information corresponding to the target point cloud after determining the density information of the target point cloud, wherein the first identifier is used to indicate the density information of the target point cloud.

[0175] Optionally, the target point cloud is a point cloud sequence or a point cloud slice in the point cloud sequence.

[0176] The point cloud coding processing device of the embodiment of the present application first determines the density information of the target point cloud, and then turns on the isolated point coding mode when the density information indicates that the target point cloud is a sparse point cloud and the target point cloud satisfies the isolated point coding condition. In this way, suitable nodes for isolated point coding can be quickly screened out through the above density information and the above isolated point coding condition, thereby effectively ensuring the coding performance.

[0177] It should be noted that the point cloud decoding processing method provided in the embodiment of the present application can be executed by a point cloud decoding processing device, or a control module in the point cloud decoding processing device for executing the point cloud decoding processing method. In the embodiment of the present application, the point cloud decoding processing device provided in the embodiment of the present application is described by taking the point cloud decoding processing device executing the point cloud decoding processing method as an example.

[0178] like Figure 8 As shown, the embodiment of the present application further provides a point cloud decoding processing device 800, comprising:

[0179] The second determination module 801 is used to determine the density information of the target point cloud;

[0180] The second processing module 802 is used to start an isolated point decoding mode when the density information indicates that the target point cloud is a sparse point cloud and the to-be-decoded node corresponding to the target point cloud meets an isolated point decoding condition.

[0181] Optionally, the second determination module is used to determine density information of the target point cloud according to a target volume corresponding to the target point cloud and the number of target points included in the target point cloud.

[0182] Optionally, the second determining module includes:

[0183] A third determination submodule, configured to divide the target volume by the target number of points to obtain a first value;

[0184] The fourth determination submodule is used to determine the density information of the target point cloud according to the relationship between the first value and a preset threshold.

[0185] Optionally, the fourth determining submodule is used to perform at least one of the following:

[0186] When the first value is greater than a preset threshold, determining that the density information of the target point cloud is a sparse point cloud;

[0187] When the first value is less than or equal to a preset threshold, the density information of the target point cloud is determined to be a dense point cloud.

[0188] Optionally, the device of the embodiment of the present application further includes:

[0189] A second acquisition module is used for obtaining the preset threshold or the index of the preset threshold in the geometric header information corresponding to the target point cloud before the fourth determination submodule determines the density information of the target point cloud according to the relationship between the first value and the preset threshold; or obtaining the preset threshold or the index of the preset threshold in the geometric header information corresponding to the target point cloud;

[0190] The index of the preset threshold corresponds to a threshold in a preset threshold list, and the preset threshold list includes at least one threshold.

[0191] Optionally, the device of the embodiment of the present application further includes:

[0192] The third acquisition module is used to acquire the target volume and the number of target points according to the geometric header information or geometric header information corresponding to the target point cloud before the second determination module determines whether the target point cloud is a sparse point cloud.

[0193] Optionally, the second determination module is used to determine density information of the target point cloud according to the first identifier if there is a first identifier in the geometry header information or geometry header information corresponding to the target point cloud.

[0194] Optionally, the target point cloud is a point cloud sequence or a point cloud slice in the point cloud sequence.

[0195] The point cloud decoding processing device of the embodiment of the present application first determines the density information of the target point cloud, and then starts the isolated point decoding mode when the density information indicates that the target point cloud is a sparse point cloud and the target point cloud meets the isolated point decoding condition. In this way, suitable nodes for isolated point decoding can be quickly screened out through the above density information and the above isolated point decoding condition, thereby effectively ensuring the decoding performance.

[0196] The point cloud decoding processing device in the embodiment of the present application can be a device, a device or electronic device with an operating system, or a component, integrated circuit, or chip in a terminal. The device or electronic device can be a mobile terminal or a non-mobile terminal.

[0197] The point cloud decoding processing device provided in the embodiment of the present application can achieve Figure 4 The various processes implemented in the method embodiment achieve the same technical effect and will not be described again here to avoid repetition.

[0198] Optionally, the present application embodiment further provides a point cloud decoding processing device (the structure of which can be seen in Figure 6), including a processor, a memory, a program or instruction stored in the memory and executable on the processor, which, when executed by the processor, implements each process of the above-mentioned point cloud decoding processing method embodiment and can achieve the same technical effect. To avoid repetition, it will not be described here.

[0199] An embodiment of the present application also provides a point cloud decoding processing device, including a processor and a communication interface, the processor being used to: determine the density information of the target point cloud; when the density information indicates that the target point cloud is a sparse point cloud and the node to be decoded corresponding to the target point cloud meets the isolated point decoding condition, start the isolated point decoding mode.

[0200] The present application also provides a point cloud decoding processing device, the hardware structure diagram of which can be found in Figure 7 The point cloud decoding and processing device includes but is not limited to: a radio frequency unit, a network module, an audio output unit, an input unit, a sensor, a display unit, a user input unit, an interface unit, a memory, and at least some of the components of a processor.

[0201] Those skilled in the art will appreciate that the point cloud decoding processing device may also include a power source (such as a battery) for supplying power to each component, and the power source may be logically connected to the processor through a power management system, thereby implementing functions such as managing charging, discharging, and power consumption management through the power management system. Figure 7 The structure shown in does not constitute a limitation on the point cloud decoding processing device. The point cloud decoding processing device may include more or fewer components than shown in the figure, or a combination of certain components, or a different arrangement of components, which will not be described in detail here.

[0202] It should be understood that in the embodiment of the present application, the input unit may include a graphics processing unit (GPU) and a microphone, and the graphics processor processes the image data of a static picture or video obtained by an image capture device (such as a camera) in a video capture mode or an image capture mode. The display unit may include a display panel, and the display panel may be configured in the form of a liquid crystal display, an organic light emitting diode, etc. The user input unit includes a touch panel and other input devices. A touch panel is also called a touch screen. The touch panel may include two parts: a touch detection device and a touch controller. Other input devices may include, but are not limited to, a physical keyboard, function keys (such as volume control keys, switch keys, etc.), a trackball, a mouse, and a joystick, which will not be repeated here.

[0203] In the embodiments of the present application, generally, the radio frequency unit includes but is not limited to an antenna, at least one amplifier, a transceiver, a coupler, a low noise amplifier, a duplexer, and the like.

[0204] The memory can be used to store software programs or instructions and various data. The memory can mainly include a program or instruction storage area and a data storage area, wherein the program or instruction storage area can store an operating system, an application program or instruction required for at least one function (such as a sound playback function, an image playback function, etc.), etc. In addition, the memory can include a high-speed random access memory, and can also include a non-volatile memory, wherein the non-volatile memory can be a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), or a flash memory. For example, at least one disk storage device, a flash memory device, or other non-volatile solid-state storage device.

[0205] The processor may include one or more processing units; optionally, the processor may integrate an application processor and a modem processor, wherein the application processor mainly processes an operating system, a user interface, and application programs or instructions, etc., and the modem processor mainly processes wireless communications, such as a baseband processor. It is understandable that the modem processor may not be integrated into the processor.

[0206] Optionally, the processor is used to determine density information of the target point cloud; when the density information indicates that the target point cloud is a sparse point cloud and the node to be decoded corresponding to the target point cloud meets the isolated point decoding condition, the isolated point decoding mode is turned on.

[0207] Optionally, the processor is used to determine density information of the target point cloud according to a target volume corresponding to the target point cloud and the number of target points included in the target point cloud.

[0208] Optionally, the processor is used to divide the target volume by the target point number to obtain a first value; and determine density information of the target point cloud based on a relationship between the first value and a preset threshold.

[0209] Optionally, the processor is configured to perform at least one of the following:

[0210] When the first value is greater than a preset threshold, determining that the density information of the target point cloud is a sparse point cloud;

[0211] When the first value is less than or equal to a preset threshold, the density information of the target point cloud is determined to be a dense point cloud.

[0212] Optionally, the processor is configured to obtain the preset threshold or the index of the preset threshold in the geometry header information corresponding to the target point cloud before determining the density information of the target point cloud according to the relationship between the first value and the preset threshold; or obtain the preset threshold or the index of the preset threshold in the geometry header information corresponding to the target point cloud;

[0213] The index of the preset threshold corresponds to a threshold in a preset threshold list, and the preset threshold list includes at least one threshold.

[0214] Optionally, the processor is used to obtain the target volume and the number of target points according to geometry header information or geometry header information corresponding to the target point cloud before determining whether the target point cloud is a sparse point cloud.

[0215] Optionally, the processor is configured to determine density information of the target point cloud according to a first identifier if there is a first identifier in the geometry header information or geometry header information corresponding to the target point cloud.

[0216] Optionally, the target point cloud is a point cloud sequence or a point cloud slice in the point cloud sequence.

[0217] The point cloud decoding processing device of the embodiment of the present application first determines the density information of the target point cloud, and then starts the isolated point decoding mode when the density information indicates that the target point cloud is a sparse point cloud and the target point cloud meets the isolated point decoding condition. In this way, suitable nodes for isolated point decoding can be quickly screened out through the above density information and the above isolated point decoding condition, thereby effectively ensuring the decoding performance.

[0218] An embodiment of the present application also provides a readable storage medium, on which a program or instruction is stored. When the program or instruction is executed by a processor, each process of the above-mentioned point cloud encoding processing method or point cloud decoding processing method embodiment is implemented, and the same technical effect can be achieved. To avoid repetition, it will not be repeated here.

[0219] The processor is a processor in the point cloud encoding processing device or the point cloud decoding processing device described in the above 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, etc.

[0220] An embodiment of the present application further provides a chip, which includes a processor and a communication interface, wherein the communication interface is coupled to the processor, and the processor is used to run programs or instructions to implement the various processes of the above-mentioned point cloud decoding processing method or point cloud encoding processing method embodiment, and can achieve the same technical effect. To avoid repetition, it will not be repeated here.

[0221] It should be understood that the chip mentioned in the embodiments of the present application can also be called a system-level chip, a system chip, a chip system or a system-on-chip chip, etc.

[0222] It should be noted that, in this article, the terms "comprise", "include" or any other variant thereof are intended to cover non-exclusive inclusion, so that the process, method, article or device including a series of elements includes not only those elements, but also includes other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise one..." do not exclude the presence of other identical elements in the process, method, article or device including the element. In addition, it should be pointed out that 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, for example, the described method may be performed in an order different from that described, and various steps may also be added, omitted, or combined. In addition, the features described with reference to certain examples may be combined in other examples.

[0223] Through the description of the above implementation methods, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of software plus a necessary general hardware platform, and of course by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present application, or the part that contributes to the prior art, can be embodied in the form of a computer software product, which is stored in a storage medium (such as ROM / RAM, a disk, or an optical disk), and includes a number of instructions for a terminal (which can be a mobile phone, a computer, a server, or a network device, etc.) to execute the methods described in each embodiment of the present application.

[0224] The embodiments of the present application are described above in conjunction with the accompanying drawings, but the present application is not limited to the above-mentioned specific implementation methods. The above-mentioned specific implementation methods are merely illustrative and not restrictive. Under the guidance of the present application, ordinary technicians in this field can also make many forms without departing from the purpose of the present application and the scope of protection of the claims, all of which are within the protection of the present application.

Claims

1. A point cloud coding processing method, characterized in that: include: Determine the density information of the target point cloud; When the density information indicates that the target point cloud is a sparse point cloud, if the to-be-encoded node corresponding to the target point cloud satisfies an isolated point encoding condition, an isolated point encoding mode is enabled; Among them, after determining the density information of the target point cloud, it also includes: Adding a first identifier to the geometry header information or geometry slice header information corresponding to the target point cloud, wherein the first identifier is used to indicate density information of the target point cloud, wherein the target point cloud is a point cloud sequence or a point cloud slice in a point cloud sequence; Wherein, the determination of density information of the target point cloud includes: Dividing the target volume corresponding to the target point cloud by the number of target points included in the target point cloud to obtain a first value; The density information of the target point cloud is determined according to the relationship between the first value and a preset threshold.

2. The method according to claim 1, characterized in that Determining the density information of the target point cloud according to the relationship between the first value and the preset threshold value includes at least one of the following: When the first value is greater than the preset threshold, determining that the density information of the target point cloud is a sparse point cloud; When the first value is less than or equal to the preset threshold, the density information of the target point cloud is determined to be a dense point cloud.

3. The method according to claim 1, characterized in that Also includes: Adding the preset threshold or the index of the preset threshold to the geometry header information; Alternatively, the preset threshold or the index of the preset threshold is added to the geometry header information; The index of the preset threshold corresponds to a threshold in a preset threshold list, and the preset threshold list includes at least one threshold.

4. The method according to claim 1, characterized in that Before determining whether the target point cloud is a sparse point cloud, the method further includes: The target volume and the number of target points are obtained according to the geometry header information or geometry header information corresponding to the target point cloud.

5. A point cloud decoding processing method, characterized in that: include: Determine the density information of the target point cloud; When the density information indicates that the target point cloud is a sparse point cloud, if the to-be-decoded node corresponding to the target point cloud satisfies an isolated point decoding condition, an isolated point decoding mode is started; Among them, determining the density information of the target point cloud includes: If there is a first identifier in the geometric header information or geometric slice header information corresponding to the target point cloud, the density information of the target point cloud is determined according to the first identifier, wherein the target point cloud is a point cloud sequence or a point cloud slice in a point cloud sequence; wherein the first identifier is determined based on the relationship between a first numerical value and a preset threshold, and the first numerical value is obtained by dividing the target volume corresponding to the target point cloud by the number of target points contained in the target point cloud.

6. The method according to claim 5, characterized in that Also includes: If the first identifier does not exist in the geometry header information or geometry header information corresponding to the target point cloud, the density information of the target point cloud is determined according to the target volume corresponding to the target point cloud and the number of target points included in the target point cloud.

7. The method according to claim 6, characterized in that The step of determining density information of the target point cloud according to the target volume corresponding to the target point cloud and the number of target points included in the target point cloud includes: Dividing the target volume by the target number of points to obtain a first value; The density information of the target point cloud is determined according to the relationship between the first value and a preset threshold.

8. The method according to claim 7, characterized in that Determining the density information of the target point cloud according to the relationship between the first value and the preset threshold value includes at least one of the following: When the first value is greater than a preset threshold, determining that the density information of the target point cloud is a sparse point cloud; When the first value is less than or equal to a preset threshold, the density information of the target point cloud is determined to be a dense point cloud.

9. The method according to claim 7, characterized in that: Before determining the density information of the target point cloud according to the relationship between the first value and the preset threshold, the method further includes: Obtaining the preset threshold or the index of the preset threshold in the geometry header information corresponding to the target point cloud; Alternatively, the preset threshold or the index of the preset threshold is obtained in the geometric header information corresponding to the target point cloud; The index of the preset threshold corresponds to a threshold in a preset threshold list, and the preset threshold list includes at least one threshold.

10. The method according to claim 6, characterized in that Before determining whether the target point cloud is a sparse point cloud, the method further includes: The target volume and the number of target points are obtained according to the geometry header information or geometry header information corresponding to the target point cloud.

11. A point cloud coding processing device, characterized in that: include: The first determination module is used to determine the density information of the target point cloud; A first processing module is configured to enable an isolated point coding mode if the to-be-coded node corresponding to the target point cloud satisfies an isolated point coding condition when the density information indicates that the target point cloud is a sparse point cloud; Wherein, the device further comprises: A second encoding module is used to add a first identifier to the geometry header information or geometry slice header information corresponding to the target point cloud after the first determination module determines the density information of the target point cloud, wherein the first identifier is used to indicate the density information of the target point cloud, wherein the target point cloud is a point cloud sequence or a point cloud slice in the point cloud sequence; Among them, the first determination module is used to divide the target volume corresponding to the target point cloud by the number of target points contained in the target point cloud to obtain a first value; and determine the density information of the target point cloud based on the relationship between the first value and a preset threshold.

12. A point cloud coding processing device, characterized in that: It includes a processor, a memory, and a program or instruction stored in the memory and executable on the processor, wherein when the program or instruction is executed by the processor, the steps of the point cloud coding processing method according to any one of claims 1 to 4 are implemented.

13. A point cloud decoding processing device, characterized in that: include: The second determination module is used to determine the density information of the target point cloud; A second processing module is configured to start an isolated point decoding mode if the to-be-decoded node corresponding to the target point cloud satisfies an isolated point decoding condition when the density information indicates that the target point cloud is a sparse point cloud; Wherein, the second determination module is used to determine the density information of the target point cloud according to the first identifier if there is a first identifier in the geometric header information or the geometric slice header information corresponding to the target point cloud, wherein the target point cloud is a point cloud sequence or a point cloud slice in a point cloud sequence; wherein the first identifier is determined based on the relationship between a first numerical value and a preset threshold, and the first numerical value is obtained by dividing the target volume corresponding to the target point cloud by the number of target points contained in the target point cloud.

14. A point cloud decoding processing device, characterized in that: It includes a processor, a memory, and a program or instruction stored in the memory and executable on the processor, wherein when the program or instruction is executed by the processor, the steps of the point cloud decoding processing method as described in any one of claims 5 to 10 are implemented.

15. A readable storage medium, characterized in that: The readable storage medium stores a program or instruction, and when the program or instruction is executed by the processor, the steps of the point cloud encoding processing method as described in any one of claims 1 to 4 are implemented, or the steps of the point cloud decoding processing method as described in any one of claims 5 to 10 are implemented.

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