Point cloud compression method, apparatus and device based on variable bitrate, and storage medium

Abstract

The present application relates to the technical field of point cloud processing, and discloses a point cloud compression method, apparatus and device based on a variable bitrate, and a storage medium. The method comprises: acquiring first point cloud data to be compressed and the number of channels between convolution operators; and compressing said first point cloud data by means of a preset convolution model to obtain second point cloud data, wherein the convolution model comprises convolution channels corresponding to the number of channels, and the bitrate of the second point cloud data is associated with the number of channels. According to the present application, when a user has different requirements on the bitrate of a compressed file, a compressed file having a corresponding bitrate can be obtained on the basis of the number of channels between different convolution operators, so that the availability of the compressed file is improved.

Description

Point cloud compression method, device, equipment and storage medium based on variable bit rate Technical Field

[0001] The present application relates to the field of point cloud processing technology, and in particular to a point cloud compression method, apparatus, device and storage medium based on a variable bit rate. Background Art

[0002] With the further development of multimedia technology, point cloud has become another important multimedia data after images and videos. Since point cloud data usually contains a large number of points, an effective compression algorithm is necessary to effectively store and transmit point cloud data.

[0003] Until now, end-to-end point cloud compression has typically employed a fixed bitrate, meaning the encoder outputs a fixed amount of code data per second (or the decoder input bitrate). However, this fixed bitrate approach cannot meet varying user requirements for compressed file bitrates, resulting in limited usability.

[0004] Summary of the Invention

[0005] The main purpose of this application is to provide a point cloud compression method, device, equipment and storage medium based on variable bit rate, aiming to solve the problem that the fixed bit rate method cannot meet the different requirements of users for the bit rate of compressed files, and therefore has low usability.

[0006] To achieve the above objectives, the present application provides a point cloud compression method based on a variable bit rate, the point cloud compression method based on a variable bit rate comprising the following steps:

[0007] Obtaining the number of channels between the first point cloud data to be compressed and the convolution operator;

[0008] The first point cloud data is compressed by a preset convolution model to obtain second point cloud data, wherein the convolution model includes convolution channels corresponding to the number of channels, and the bit rate of the second point cloud data is associated with the number of channels.

[0009] Optionally, the step of compressing the first point cloud data using a preset convolution model to obtain second point cloud data includes:

[0010] Performing a convolution operation on the first point cloud data using a preset convolution model to obtain convolution data;

[0011] Determining anchor points and non-anchor points in the convolution data using a preset entropy model;

[0012] The anchor points and the non-anchor points are encoded to obtain second point cloud data.

[0013] Optionally, the step of encoding the anchor point and the non-anchor point to obtain the second point cloud data includes:

[0014] Encoding the anchor point to obtain first encoded data;

[0015] encoding the non-anchor point according to the first encoded data to obtain second encoded data;

[0016] The first encoded data and the second encoded data are combined to obtain second point cloud data.

[0017] Optionally, the step of determining the anchor points and non-anchor points in the convolution data using a preset entropy model includes:

[0018] Determining the coordinates of each data point in the convolution data using a preset entropy model;

[0019] According to the coordinates, summing the coordinate values ​​of the data points and performing a modulo 2 operation to obtain a calculation result;

[0020] Anchor points and non-anchor points in the convolution data are determined according to the calculation results.

[0021] Optionally, after the step of compressing the first point cloud data using a preset convolution model to obtain second point cloud data, the method further includes:

[0022] The second point cloud data is fed back to the user, so that the user can input the number of channels between convolution operators in the convolution model in the next compression process according to the second point cloud data and its bit rate.

[0023] Optionally, the step of compressing the first point cloud data using a preset convolution model to obtain second point cloud data further includes:

[0024] Encoding and Gaussian modeling the anchor points and non-anchor points using a preset entropy model to obtain second point cloud data, wherein the second point cloud data includes the encoded data and the mean and variance of the Gaussian modeling;

[0025] The encoded data and the mean and variance of the Gaussian model are sent to an arithmetic encoder so that the arithmetic encoder eliminates redundant parts in the encoded data through secondary encoding.

[0026] Optionally, the step of performing Gaussian modeling on the anchor points and non-anchor points using a preset entropy model to obtain the mean and variance of the Gaussian modeling includes:

[0027] Calculate the entropy estimation information of anchor points and non-anchor points through a preset entropy model;

[0028] Gaussian modeling is performed on the entropy estimation information of the anchor point and the non-anchor point to obtain a mean and variance of the Gaussian modeling.

[0029] In addition, to achieve the above-mentioned purpose, the present application also provides a point cloud compression device based on a variable bit rate, and the point cloud compression device based on a variable bit rate includes:

[0030] An acquisition module, configured to acquire the number of channels between the first point cloud data to be compressed and the convolution operator;

[0031] A compression module is used to compress the first point cloud data through a preset convolution model to obtain second point cloud data, wherein the convolution model includes convolution channels corresponding to the number of channels, and the bit rate of the second point cloud data is associated with the number of channels.

[0032] In addition, to achieve the above-mentioned purpose, the present application also provides a point cloud compression device based on a variable bit rate, the device comprising: a memory, a processor, and a point cloud compression program based on a variable bit rate stored on the memory and runnable on the processor, the point cloud compression program based on a variable bit rate being configured to implement the steps of the point cloud compression method based on a variable bit rate.

[0033] In addition, to achieve the above-mentioned purpose, the present application also provides a storage medium, on which a point cloud compression program based on a variable bit rate is stored. When the point cloud compression program based on a variable bit rate is executed by a processor, the steps of the point cloud compression method based on a variable bit rate are implemented.

[0034] The present application provides a point cloud compression method, apparatus, device, and storage medium based on a variable bit rate. In contrast to the related art, end-to-end point cloud compression typically adopts a fixed bit rate, i.e., the amount of code data output per second by the encoder (or the input bit rate of the decoder) is a fixed value. However, the fixed bit rate approach cannot meet the different user requirements for the bit rate of the compressed file, and therefore has low usability. In the present application, the number of channels between the first point cloud data to be compressed and the convolution operator is obtained; the first point cloud data is compressed using a preset convolution model to obtain second point cloud data, wherein the convolution model includes convolution channels corresponding to the number of channels, and the bit rate of the second point cloud data is associated with the number of channels. It can be understood that in the present application, the number of channels between the first point cloud data to be compressed and the convolution operator is obtained, and the convolution model including the convolution channels corresponding to the obtained number of channels is used to obtain the second point cloud data with a bit rate associated with the number of channels. When users have different requirements for the bit rate of the compressed file, compressed files with corresponding bit rates can be obtained based on the number of channels between different convolution operators, thereby improving its usability. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] FIG1 is a schematic diagram of a first flow chart of a first embodiment of a point cloud compression method based on a variable bit rate according to the present application;

[0036] FIG2 is a schematic diagram of a first scenario of the first embodiment of the point cloud compression method based on a variable bit rate of the present application;

[0037] FIG3 is a schematic diagram of a second scenario of the first embodiment of the point cloud compression method based on a variable bit rate of the present application;

[0038] FIG4 is a schematic diagram of a second flow chart of a second embodiment of the point cloud compression method based on a variable bit rate of the present application;

[0039] FIG5 is a schematic diagram of a third scenario of the second embodiment of the point cloud compression method based on a variable bit rate of the present application;

[0040] FIG6 is a schematic diagram of a third flow chart of a third embodiment of the point cloud compression method based on a variable bit rate of the present application;

[0041] FIG7 is a schematic diagram of a fourth scenario of the third embodiment of the point cloud compression method based on a variable bit rate of the present application;

[0042] FIG8 is a structural block diagram of a point cloud compression device based on a variable bit rate according to the present application;

[0043] FIG9 is a schematic diagram of the structure of the hardware operating environment involved in the embodiment of the present application.

[0044] The realization of the objectives, functional features and advantages of this application will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0045] It should be understood that the specific embodiments described herein are only used to explain the present application and are not intended to limit the present application.

[0046] Refer to Figure 1, which is a first flow chart of the first embodiment of the point cloud compression method based on variable bit rate of the present application.

[0047] In a first embodiment, the point cloud compression method based on a variable bit rate includes the following steps:

[0048] Step S10, obtaining the number of channels between the first point cloud data to be compressed and the convolution operator;

[0049] Step S20: compress the first point cloud data through a preset convolution model to obtain second point cloud data, wherein the convolution model includes convolution channels corresponding to the number of channels, and the bit rate of the second point cloud data is associated with the number of channels.

[0050] This embodiment aims to: obtain the number of channels between the first point cloud data to be compressed and the convolution operator, and obtain second point cloud data with a bit rate associated with the number of channels through a convolution model including convolution channels corresponding to the obtained number of channels. When users have different requirements for the bit rate of the compressed file, compressed files with corresponding bit rates can be obtained according to the number of channels between different convolution operators, thereby improving its usability.

[0051] The following describes the specific steps:

[0052] Step S10, obtaining the number of channels between the first point cloud data to be compressed and the convolution operator;

[0053] It should be noted that the execution subject of this embodiment is a point cloud compression device based on a variable bit rate, and the point cloud compression device based on a variable bit rate may be subordinate to a point cloud compression device based on a variable bit rate.

[0054] It is understandable that the point cloud refers to a point set of target surface characteristics, that is, a point set obtained after obtaining the spatial coordinates of each sampling point on the object surface.

[0055] In a specific implementation, the variable bit rate-based point cloud compression device obtains first point cloud data from a target device, and the target device has the ability to collect point data on the surface of an object.

[0056] For example, when a laser beam hits the surface of an object, the reflected laser will carry information such as direction and distance; if the laser beam is scanned along a certain trajectory, the reflected laser point information will be recorded while scanning. Since the scanning is extremely precise, a large number of laser points can be obtained, thus forming a laser point cloud.

[0057] It is understandable that the number of channels between the convolution operators is input by the user according to actual needs.

[0058] In a specific implementation, the variable bit rate-based point cloud compression device obtains the number of channels between convolution operators input by the user through a visual interface.

[0059] For example, when the bit rate is 0.5, the number of channels between convolution operators in the adopted convolution model is 32. The user can change the number of channels to 16 according to actual needs (i.e., the need for a lower bit rate), and then obtain the second point cloud data with a bit rate of 0.3.

[0060] Step S20: compress the first point cloud data through a preset convolution model to obtain second point cloud data, wherein the convolution model includes convolution channels corresponding to the number of channels, and the bit rate of the second point cloud data is associated with the number of channels.

[0061] It should be noted that the bit rate of the second point cloud data is associated with the algorithm complexity, and the algorithm complexity is associated with the number of channels.

[0062] It can be understood that the algorithm complexity increases with the number of channels, and the bit rate of the second point cloud data increases with the algorithm complexity, that is, the bit rate of the second point cloud data is associated with the algorithm complexity and the number of channels.

[0063] It should be noted that, referring to Figure 2, point cloud compression includes geometric compression and attribute compression. Geometric compression encodes the geometric position (coordinates), and attribute compression encodes the accompanying attributes of each point, such as RGB. Point cloud geometric coding or attribute coding includes the following processes: transformation, quantization, entropy coding, entropy model decoding, inverse quantization, and inverse transformation.

[0064] It can be understood that in the transformation process of point cloud coding, an adaptive convolution operator is introduced based on the Minkowski engine, called the Adaptive Sparse Convolution Operator (AdaSConv); this operator can adaptively adjust the number of input and output channels according to the number of channels input by the user.

[0065] In the specific implementation, for the common sparse convolution operator, it is assumed that the coordinates of input and output are and The kernel weight of the kth position is W k , whose input Perform a sparse convolution operation to output the feature map for:

[0066] Among them, k is the occupied points in the receptive field, K d is the size of the convolution kernel. According to the sparse convolution rule, only the occupied points under the receptive field are calculated, which satisfies:

[0067] In this embodiment, as shown in Figure 3, the proposed AdaSConv is able to process inputs with different channel configurations ( or ), and dynamically calculate the output feature map (C out ).

[0068] It can be understood that the variable bit rate based point cloud compression device compresses the first point cloud data through a convolution model including convolution channels corresponding to the number of channels input by the user, and obtains the second point cloud data whose bit rate is associated with the number of channels input by the user.

[0069] In specific implementations, users can adjust the computational complexity and further adjust the bit rate by changing the number of convolution channels under a single model.

[0070] In the specific implementation, for the AdaSConv operator, assuming its weight is For the mth output have:

[0071] Among them, W k (m,n) refers to the convolution kernel with m as the output position and n as the input position; the output of the total feature map of the convolution is expressed as:

[0072] Among them, m can be taken from 1 to C out Any number; for each m, F out (m) are assembled together to form the final feature output.

[0073] Specifically, after step S20, the method further includes step S21:

[0074] Step S21: Feedback the second point cloud data to the user, so that the user can input the number of channels between the convolution operators in the convolution model in the next compression process according to the second point cloud data and its bit rate.

[0075] In a specific implementation, after obtaining the second point cloud data through compression, the point cloud compression device based on a variable bit rate feeds back the second point cloud data and its bit rate to the user. The user inputs the number of channels between the convolution operators in the convolution model in the next compression process according to actual needs.

[0076] In this embodiment, end-to-end point cloud compression in related technologies generally adopts a fixed bit rate, that is, the amount of code data output per second by the encoder (or the input bit rate of the decoder) is a fixed value. However, the fixed bit rate approach cannot meet the different requirements of users for the bit rate of the compressed file, and therefore its usability is low. In this embodiment, the number of channels between the first point cloud data to be compressed and the convolution operator is obtained; the first point cloud data is compressed using a preset convolution model to obtain second point cloud data, wherein the convolution model includes convolution channels corresponding to the number of channels, and the bit rate of the second point cloud data is associated with the number of channels. That is, in this embodiment, the number of channels between the first point cloud data to be compressed and the convolution operator is obtained, and the convolution model including the convolution channels corresponding to the obtained number of channels is used to obtain second point cloud data with a bit rate associated with the number of channels. When users have different requirements for the bit rate of the compressed file, compressed files with corresponding bit rates can be obtained according to the number of channels between different convolution operators, thereby improving its usability.

[0077] Further, referring to FIG. 4 , based on the above embodiment, a second embodiment of the present application is provided. In this embodiment, the step S20 further includes the following steps:

[0078] Step A10: performing a convolution operation on the first point cloud data using a preset convolution model to obtain convolution data;

[0079] It should be noted that the variable bit rate-based point cloud compression device performs a convolution operation on the first point cloud data through a preset convolution model to obtain convolution data.

[0080] It can be understood that, referring to Figure 5, the variable bit rate based point cloud compression device transforms the first point cloud data through DownC1, DownC2, DownC3, UpC1, UpC2 and UpC3 (a process in compression), and then performs entropy encoding on the convolution data after transformation through Entropy Models.

[0081] Step A20, determining anchor points and non-anchor points in the convolution data using a preset entropy model;

[0082] It should be noted that, during the entropy coding process, the point cloud compression device based on a variable bit rate eliminates statistical redundancy according to statistical characteristics through an entropy model.

[0083] It can be understood that the variable bit rate based point cloud compression device divides the data points to be encoded into anchor points and non-anchor points through an entropy model and in a context manner to achieve lossless compression.

[0084] In a specific implementation, the variable bit rate-based point cloud compression device determines the coordinates of each data point in the convolution data, and determines the anchor point and non-anchor point according to the coordinates.

[0085] Specifically, the step A20 further includes steps A21-A23:

[0086] Step A21, determining the coordinates of each data point in the convolution data using a preset entropy model;

[0087] In a specific implementation, after passing through the transformation network, the point cloud compression device based on variable bit rate records the coordinates after the transformation network and the corresponding features as C Y and F Y .

[0088] Step A22, summing the coordinate values ​​of the data points according to the coordinates, and performing a modulo 2 operation to obtain a calculation result;

[0089] In a specific implementation, in order to ensure that all anchor points are adjacent to non-anchor points, the variable bit rate-based point cloud compression device sums the x, y, and z values ​​of each point and performs a modulo 2 operation to obtain a calculation result (0 or 1).

[0090] Step A23: Determine anchor points and non-anchor points in the convolution data according to the calculation results.

[0091] In a specific implementation, if the value after modulo 2 is 0, the point cloud compression device based on a variable bit rate defines the data point as an anchor point; if the value after modulo 2 is 1, the point cloud compression device based on a variable bit rate defines the data point as a non-anchor point.

[0092] Step A30: Encode the anchor points and the non-anchor points to obtain second point cloud data.

[0093] It should be noted that the variable bit rate based point cloud compression device encodes anchor points and non-anchor points through an entropy model, and the encoding process does not lose any information according to the entropy principle.

[0094] In a specific implementation, the point cloud compression device based on a variable bit rate first encodes the anchor point, and then encodes the non-anchor point according to the information of the encoded anchor point.

[0095] Specifically, the step A30 further includes steps A31-A33:

[0096] Step A31, encoding the anchor point to obtain first encoded data;

[0097] In a specific implementation, the variable bit rate-based point cloud compression device encodes the anchor point to obtain first encoded data.

[0098] Step A32: encoding the non-anchor point according to the first coded data to obtain second coded data;

[0099] In a specific implementation, the variable bit rate based point cloud compression device encodes the non-anchor points in combination with the first encoded data to obtain the second encoded data.

[0100] Step A33: merge the first encoded data and the second encoded data to obtain second point cloud data.

[0101] It can be understood that the first encoded data and the second encoded data refer to the second point cloud data obtained by compressing the first point cloud data.

[0102] In this embodiment, compared to related art techniques in which the number of points in a single-frame point cloud can be extremely large, resulting in a very time-consuming autoregressive decoding process, in this embodiment, a convolution operation is performed on the first point cloud data using a preset convolution model to obtain convolution data; anchor points and non-anchor points in the convolution data are determined using a preset entropy model; and the anchor points and non-anchor points are encoded to obtain second point cloud data. Specifically, in this embodiment, convolution data is obtained using a preset convolution model, anchor points and non-anchor points in the convolution data are determined using a preset entropy model, and the anchor points and non-anchor points are encoded to obtain second point cloud data. This entropy model, combined with a contextual approach, reduces the bitstream size of non-anchor point features, thereby reducing the time required for the decoding process.

[0103] Further, referring to FIG. 6 , based on the above embodiment, a third embodiment of the present application is provided. In this embodiment, the step S20 further includes the following steps:

[0104] Step B10: encoding and Gaussian modeling the anchor points and non-anchor points using a preset entropy model to obtain second point cloud data, wherein the second point cloud data includes the encoded data and the mean and variance of the Gaussian modeling;

[0105] It can be understood that the variable bit rate-based point cloud compression device encodes and Gaussian models the anchor points and non-anchor points through a preset entropy model.

[0106] In a specific implementation, as shown in FIG7 , Γ α (Y) and Γ β (Y) is recorded as the anchor point part and the non-anchor point part of Γ(Y). The super prior coding network transforms Γ(Y) into Γ(Z) and gives The variable bit rate based point cloud compression device marking The anchor point part is Anchor point Γ α Entropy estimation information Φ of (Y) α =(μα ,σ α )Depend on gives:

[0107] It can be understood that anchor points provide contextual information for non-anchor points.

[0108] In a specific implementation, as shown in FIG7 , the point cloud compression device based on a variable bit rate records the context information of the non-anchor point reference anchor point as Ω β ,Ω β By the context module g cm obtain; g cm It consists of a 3D masked convolution operator:

[0109] The point cloud compression device based on variable bit rate adopts context information Ω β Together with the decoded information of the super prior network, the entropy estimate Φ of the non-anchor point is predicted β =(μ β ,σ β ):

[0110] The Gaussian distribution of the latent representation univariate is modeled by the variable bit rate point cloud compression device as follows:

[0111] Where μ and σ are the mean and variance of the Gaussian modeling of anchor points and non-anchor points, respectively, expressed as μ = [μ α ,μ β ] and σ=[σ α ,σ β ].

[0112] Specifically, the step B10 further includes:

[0113] Step B11, calculating entropy estimation information of anchor points and non-anchor points using a preset entropy model;

[0114] In a specific implementation, the variable bit rate-based point cloud compression device calculates entropy estimation information of the anchor point through a preset entropy model, and then calculates entropy estimation information of the non-anchor point with reference to context information of the anchor point.

[0115] Step B12: Perform Gaussian modeling on the entropy estimation information of the anchor points and non-anchor points to obtain the mean and variance of the Gaussian modeling.

[0116] In a specific implementation, the variable bit rate-based point cloud compression device performs Gaussian modeling on the entropy estimation information of anchor points and non-anchor points to obtain the mean and variance of the Gaussian modeling.

[0117] Step B20: Send the encoded data and the mean and variance of the Gaussian model to an arithmetic encoder, so that the arithmetic encoder eliminates the redundant parts in the encoded data through secondary encoding.

[0118] It should be noted that the encoded data refers to data obtained by encoding anchor points and non-anchor points through a preset entropy model.

[0119] It can be understood that the encoder re-encodes the encoded data in combination with the mean and variance of the Gaussian model to eliminate redundant parts in the encoded data.

[0120] In this embodiment, compared to the related art where the number of points in a single frame of point cloud can be very large, which results in a very time-consuming autoregressive-based decoding process, in this embodiment, anchor points and non-anchor points are encoded and Gaussian-modeled using a preset entropy model to obtain second point cloud data, wherein the second point cloud data includes the encoded data and the mean and variance of the Gaussian model; the encoded data and the mean and variance of the Gaussian model are sent to an arithmetic encoder, so that the arithmetic encoder can eliminate redundancy in the encoded data through secondary encoding. That is, in this embodiment, anchor points and non-anchor points are encoded and Gaussian-modeled using a preset entropy model, and the mean and variance of the encoded data and Gaussian model are sent to the arithmetic encoder. The arithmetic encoder eliminates redundancy in the encoded data through secondary encoding, removing redundant data using the mean and variance of the encoded data and Gaussian model to streamline the point cloud data, thereby reducing the time consumption of the subsequent decoding process.

[0121] In addition, the embodiment of the present application further proposes a point cloud compression device based on a variable bit rate. Referring to FIG8 , the point cloud compression device based on a variable bit rate includes:

[0122] An acquisition module 10 is used to obtain the number of channels between the first point cloud data to be compressed and the convolution operator;

[0123] The compression module 20 is used to compress the first point cloud data through a preset convolution model to obtain second point cloud data, wherein the convolution model includes convolution channels corresponding to the number of channels, and the bit rate of the second point cloud data is associated with the number of channels.

[0124] Optionally, the compression module further includes:

[0125] a convolution unit, configured to perform a convolution operation on the first point cloud data using a preset convolution model to obtain convolution data;

[0126] a determining unit, configured to determine anchor points and non-anchor points in the convolution data using a preset entropy model;

[0127] The encoding unit is configured to encode the anchor point and the non-anchor point to obtain second point cloud data.

[0128] Optionally, the encoding unit further includes:

[0129] a first sub-encoding unit, configured to encode the anchor point to obtain first encoded data;

[0130] a second sub-encoding unit, configured to encode the non-anchor point according to the first encoded data to obtain second encoded data;

[0131] The merging sub-encoding unit is configured to merge the first encoded data and the second encoded data to obtain second point cloud data.

[0132] Optionally, the determining unit further includes:

[0133] a coordinate sub-determining unit, configured to determine the coordinates of each data point in the convolution data using a preset entropy model;

[0134] a calculation sub-determination unit, configured to sum the coordinate values ​​of the data points according to the coordinates and perform a modulo 2 operation to obtain a calculation result;

[0135] A data point sub-determination unit is used to determine anchor points and non-anchor points in the convolution data according to the calculation result.

[0136] Optionally, the point cloud compression device based on variable bit rate further includes:

[0137] A feedback module is used to feed back the second point cloud data to the user, so that the user can input the number of channels between the convolution operators in the convolution model in the next compression process according to the second point cloud data and its bit rate.

[0138] Optionally, the compression module further includes:

[0139] a modeling unit, configured to encode and Gaussian model the anchor points and non-anchor points using a preset entropy model to obtain second point cloud data, wherein the second point cloud data includes the encoded data and a mean and a variance of the Gaussian model;

[0140] A sending unit is used to send the encoded data and the mean and variance of the Gaussian model to an arithmetic encoder, so that the arithmetic encoder eliminates redundant parts in the encoded data through secondary encoding.

[0141] Optionally, the modeling unit further includes:

[0142] A calculation sub-modeling unit, configured to calculate entropy estimation information of anchor points and non-anchor points using a preset entropy model;

[0143] The Gaussian sub-modeling unit is used to perform Gaussian modeling on the entropy estimation information of the anchor point and the non-anchor point to obtain the mean and variance of the Gaussian modeling.

[0144] In this embodiment, the number of channels between the first point cloud data to be compressed and the convolution operator is obtained; the first point cloud data is compressed using a preset convolution model to obtain second point cloud data, wherein the convolution model includes convolution channels corresponding to the number of channels, and the bit rate of the second point cloud data is associated with the number of channels. That is, in this embodiment, the number of channels between the first point cloud data to be compressed and the convolution operator is obtained, and the convolution model including the convolution channels corresponding to the obtained number of channels is used to obtain second point cloud data with a bit rate associated with the number of channels. If users have different requirements for the bit rate of the compressed file, compressed files with corresponding bit rates can be obtained based on the number of channels between different convolution operators, thereby improving its usability.

[0145] The specific implementation of the variable bit rate point cloud compression device of the present application is basically the same as the above-mentioned embodiments of the variable bit rate point cloud compression method, and will not be repeated here.

[0146] Refer to Figure 9, which is a schematic diagram of the structure of a point cloud compression device based on a variable bit rate in the hardware operating environment involved in the embodiment of the present application.

[0147] As shown in FIG9 , the variable bit rate point cloud compression device may include: a processor 1001, such as a central processing unit (CPU), a communication bus 1002, a user interface 1003, a network interface 1004, and a memory 1005. The communication bus 1002 is used to implement communication between these components. The user interface 1003 may include a display screen and an input unit such as a keyboard. Optionally, the user interface 1003 may also include a standard wired interface or a wireless interface. The network interface 1004 may optionally include a standard wired interface or a wireless interface (such as a wireless fidelity (WI-FI) interface). The memory 1005 may be a high-speed random access memory (RAM) or a stable non-volatile memory (NVM), such as a disk storage device. The memory 1005 may also be a storage device independent of the aforementioned processor 1001.

[0148] Those skilled in the art will understand that the structure shown in FIG9 does not constitute a limitation on the point cloud compression device based on a variable bit rate, and may include more or fewer components than shown in the figure, or a combination of certain components, or a different arrangement of components.

[0149] As shown in FIG9 , the memory 1005 as a computer storage medium may include an operating system, a network communication module, a user interface module, and a point cloud compression program based on a variable bit rate.

[0150] Among them, the operating system is a program that manages and controls the point cloud compression equipment and software resources based on variable bit rate, supports the operation of the network communication module, user interface module, point cloud compression program based on variable bit rate and other programs or software. The network communication module is used to manage and control the network interface 1004; the user interface module is used to manage and control the user interface 1003.

[0151] In the variable bit rate based point cloud compression device shown in Figure 9, the variable bit rate based point cloud compression device calls the variable bit rate based point cloud compression program stored in the memory 1005 through the processor 1001 to implement the steps of the variable bit rate based point cloud compression method described in any one of the above items.

[0152] The specific implementation of the variable bit rate point cloud compression device of the present application is basically the same as the above-mentioned embodiments of the variable bit rate point cloud compression method, and will not be repeated here.

[0153] In addition, an embodiment of the present invention also proposes a storage medium. An embodiment of the present application provides a storage medium, and the storage medium stores one or more programs. The one or more programs can also be executed by one or more processors to implement the steps of the variable bit rate-based point cloud compression method described in any one of the above items.

[0154] The specific implementation of the storage medium of the present application is basically the same as the above-mentioned embodiments of the point cloud compression method based on variable bit rate, and will not be repeated here.

[0155] It should be noted that, in this document, the terms "comprises," "includes," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, method, article, or system comprising a series of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or system. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of other identical elements in the process, method, article, or system comprising the element.

[0156] The serial numbers of the above embodiments of the present application are for description only and do not represent the advantages or disadvantages of the embodiments.

[0157] 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 the necessary general hardware platform, and of course can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present application is essentially or the part that contributes to the prior art can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) as described above, and includes a number of instructions for enabling a terminal device (which can be a mobile phone, computer, server, or network device, etc.) to execute the methods described in each embodiment of the present application.

[0158] The above are only preferred embodiments of the present application and do not limit the patent scope of the present application. Any equivalent structure or equivalent process transformation made using the contents of the present application specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present application.

Claims

1. A point cloud compression method based on variable bit rate, characterized in that, The variable bitrate-based point cloud compression method includes the following steps: Obtain the number of channels between the first point cloud data to be compressed and the convolutional operator; Compress the first point cloud data through a preset convolutional model to obtain second point cloud data, where the convolutional model includes convolutional channels corresponding to the number of channels, and the bitrate of the second point cloud data is associated with the number of channels.

2. The variable bitrate-based point cloud compression method according to claim 1, wherein, The step of compressing the first point cloud data through a preset convolutional model to obtain second point cloud data includes: Perform a convolutional operation on the first point cloud data through a preset convolutional model to obtain convolutional data; Determine the anchor points and non-anchor points in the convolutional data through a preset entropy model; Encode the anchor points and the non-anchor points to obtain second point cloud data.

3. The variable bitrate-based point cloud compression method according to claim 2, wherein, The step of encoding the anchor points and the non-anchor points to obtain second point cloud data includes: Encode the anchor points to obtain first encoded data; According to the first encoded data, encode the non-anchor points to obtain second encoded data; Merge the first encoded data and the second encoded data to obtain second point cloud data.

4. The variable bitrate-based point cloud compression method according to claim 2, wherein The step of determining the anchor points and non-anchor points in the convolutional data through a preset entropy model includes: Determine the coordinates of each data point in the convolutional data through a preset entropy model; According to the coordinates, sum the coordinate values of the data points and perform a modulo 2 operation to obtain a calculation result; Determine the anchor points and non-anchor points in the convolutional data according to the calculation result.

5. The variable bitrate-based point cloud compression method according to claim 1, wherein After the step of compressing the first point cloud data through a preset convolutional model to obtain second point cloud data, it includes: Feed back the second point cloud data to the user for the user to input the number of channels between the convolutional operators in the next compression process according to the second point cloud data and its bitrate.

6. The variable bitrate-based point cloud compression method according to claim 1, characterized in that, The step of compressing the first point cloud data through a preset convolutional model to obtain second point cloud data further includes: Encode and perform Gaussian modeling on the anchor points and non-anchor points through a preset entropy model to obtain second point cloud data, where the second point cloud data includes encoded data and the mean and variance of Gaussian modeling; Send the encoded data and the mean and variance of Gaussian modeling to an arithmetic encoder for the arithmetic encoder to eliminate the redundant part in the encoded data through secondary encoding.

7. The variable bitrate-based point cloud compression method according to claim 6, wherein The step of performing Gaussian modeling on the anchor points and non-anchor points through a preset entropy model to obtain the mean and variance of Gaussian modeling includes: Calculate the entropy estimation information of the anchor points and non-anchor points through a preset entropy model; Perform Gaussian modeling on the entropy estimation information of the anchor points and non-anchor points to obtain the mean and variance of Gaussian modeling.

8. A point cloud compression device based on variable bit rate, characterized in that, The variable bitrate-based point cloud compression device includes: An acquisition module for acquiring the number of channels between the first point cloud data to be compressed and the convolutional operator; A compression module for compressing the first point cloud data through a preset convolutional model to obtain second point cloud data, where the convolutional model includes convolutional channels corresponding to the number of channels, and the bitrate of the second point cloud data is associated with the number of channels.

9. The point cloud compression device based on variable bit rate according to claim 8, characterized in that, The compression module further includes: A convolution unit for performing a convolution operation on the first point cloud data through a preset convolution model, to obtain convolution data; A determination unit for determining anchor points and non-anchor points in the convolution data through a preset entropy model; An encoding unit for encoding the anchor points and the non-anchor points to obtain second point cloud data.

10. The point cloud compression device based on variable bit rate according to claim 9, wherein, The encoding unit further includes: A first sub-encoding unit for encoding the anchor points to obtain first encoded data; A second sub-encoding unit for encoding the non-anchor points according to the first encoded data to obtain second encoded data; A merging sub-encoding unit for merging the first encoded data and the second encoded data to obtain second point cloud data.

11. The variable bitrate-based point cloud compression device according to claim 9, characterized in that, The determination unit further includes: A coordinate sub-determination unit for determining the coordinates of each data point in the convolution data through a preset entropy model; A calculation sub-determination unit for summing the coordinate values of the data point according to the coordinates and performing a modulo 2 operation to obtain a calculation result; A data point sub-determination unit for determining anchor points and non-anchor points in the convolution data according to the calculation result.

12. The point cloud compression device based on variable bit rate according to claim 8, characterized in that, The variable bitrate-based point cloud compression device further includes: A feedback module for feeding back the second point cloud data to the user for the user to input the number of channels between convolution operators in the convolution model in the next compression process according to the second point cloud data and its bitrate.

13. The point cloud compression device based on variable bit rate according to claim 8, wherein The compression module further includes: A modeling unit for encoding and Gaussian modeling anchor points and non-anchor points through a preset entropy model, to obtain second point cloud data, where the second point cloud data includes encoded data and the mean and variance of Gaussian modeling; A sending unit for sending the encoded data and the mean and variance of Gaussian modeling to an arithmetic encoder for the arithmetic encoder to eliminate redundant parts in the encoded data through secondary encoding.

14. The variable bitrate-based point cloud compression device according to claim 13, wherein The modeling unit further includes: A calculation sub-modeling unit for calculating the entropy estimation information of anchor points and non-anchor points through a preset entropy model; A Gaussian sub-modeling unit for performing Gaussian modeling on the entropy estimation information of anchor points and non-anchor points to obtain the mean and variance of Gaussian modeling.

15. A point cloud compression device based on variable bit rate, characterized in that, The device includes: a memory, a processor, and a variable bitrate-based point cloud compression program stored on the memory and executable on the processor, the variable bitrate-based point cloud compression program is configured to implement the steps of the variable bitrate-based point cloud compression method as claimed in claim 1.

16. A point cloud compression device based on variable bit rate, characterized in that, The device includes: a memory, a processor, and a variable bitrate-based point cloud compression program stored on the memory and executable on the processor, the variable bitrate-based point cloud compression program is configured to implement the steps of the variable bitrate-based point cloud compression method as claimed in claim 2.

17. A point cloud compression device based on variable bit rate, characterized in that, The device includes: a memory, a processor, and a variable bitrate-based point cloud compression program stored on the memory and executable on the processor, the variable bitrate-based point cloud compression program is configured to implement the steps of the variable bitrate-based point cloud compression method as claimed in claim 3.

18. A point cloud compression device based on variable bit rate, characterized in that, The device includes: a memory, a processor, and a variable bitrate-based point cloud compression program stored on the memory and executable on the processor, the variable bitrate-based point cloud compression program being configured to implement the steps of the variable bitrate-based point cloud compression method as claimed in claim 4.

19. A point cloud compression device based on variable bit rate, characterized in that, The device includes: a memory, a processor, and a variable bitrate-based point cloud compression program stored on the memory and executable on the processor, the variable bitrate-based point cloud compression program being configured to implement the steps of the variable bitrate-based point cloud compression method as claimed in claim 5.

20. A storage medium, characterized in that, A variable bitrate-based point cloud compression program is stored on the storage medium, and when the variable bitrate-based point cloud compression program is executed by a processor, the steps of the variable bitrate-based point cloud compression method as claimed in claim 1 are implemented.

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