A 3D point cloud data compression method simulating human eyes

Abstract

The present invention discloses a method for compressing three-dimensional point cloud data that simulates the human eye, comprising: obtaining raw point cloud data, performing Gaussian filtering and denoising on the raw point cloud data; extracting regions of interest from the denoised point cloud using a human eye-simulated target of interest extraction algorithm; retaining the extracted regions of interest at high resolution and performing non-uniform compression on non-interested regions using a human eye-simulated target of interest compression algorithm; and finally displaying the compressed results using a display program. The present invention obtains regions of interest based on the human eye's retinal attention perception mechanism, then performs high resolution on the regions of interest and non-uniform redundant compression on non-interested regions. This addresses the inefficiency of current point cloud data processing algorithms that have fixed resolution when processing regions of interest and non-interested regions, reduces the amount of data involved in non-interested regions, and thus effectively improves the quality of three-dimensional point cloud data and enhances the recognition accuracy and speed in the field of three-dimensional object recognition.

Description

Technical Field

[0001] The present invention belongs to the technical field of autonomous driving data processing, and in particular relates to a method for compressing three-dimensional point cloud data that simulates the human eye. Background Art

[0002] With the continuous advancement of optics, computer science, electronic information, and automatic control technologies, intelligent upgrades are becoming increasingly prevalent in various fields. The rise of autonomous driving, in particular, has placed ever-more stringent demands on optoelectronic sensing technologies for complex environments. This trend is not only driving the development of sensor technology, but also fostering innovation in data processing and intelligent algorithms to meet the urgent need for highly dynamic, multi-dimensional scene perception.

[0003] However, existing 3D point cloud processing technologies still face numerous challenges in practical applications. In terms of efficiency, 3D point cloud data is typically massive, particularly in complex scenes, containing hundreds of thousands or even millions of point clouds. This places a significant computational burden on data processing and analysis. In terms of accuracy, the high computational complexity of point cloud data makes it challenging to quickly and accurately extract and identify features of interest. Furthermore, point cloud data acquisition is often accompanied by noise and incompleteness, placing even higher demands on target recognition accuracy.

[0004] Among existing methods, although some technologies can process 3D point cloud data to a certain extent, such as point cloud recognition for autonomous driving, the shortcomings of existing technologies are mainly reflected in their inefficiency and interference when processing regions of interest and non-regions of interest due to fixed resolution. Especially in complex scenes, the 3D point cloud recognition process has difficulty effectively distinguishing and processing different objects in the point cloud, resulting in inaccurate recognition results. For example, PointNet++ mentions the adaptability problem of point sets sampled with different densities. This shows that when processing point clouds with different densities, fixed-resolution methods have difficulty effectively capturing all important feature details. This inadaptability will lead to inefficiency and reduced accuracy in the recognition process. In addition, in complex scenes, the number of point clouds usually reaches millions. These methods are inefficient when processing large-scale point cloud data and have difficulty meeting real-time requirements. For example, in the 3D target recognition process, VoxelNet first needs to convert each point cloud into voxels and then perform feature extraction on the large amount of voxel data. This makes it difficult for VoxelNet to meet real-time requirements in complex scenes. Summary of the Invention

[0005] In order to solve the above technical problems, the present invention proposes a three-dimensional point cloud data compression method that simulates the human eye. The human eye retina has a non-uniform structure, with many central cells and high resolution characteristics, and the surrounding cells gradually decrease. The data of non-interested targets can be compressed, so that clear imaging can be achieved in the central gaze area, and compression and early warning can be performed in the surrounding non-interested areas, achieving rapid imaging, which provides ideas for solving the above problems.

[0006] To achieve the above object, the present invention provides a method for compressing three-dimensional point cloud data simulating human eyes, comprising:

[0007] Obtain the original 3D point cloud data of autonomous driving and perform Gaussian filtering and denoising on the original 3D point cloud data;

[0008] The human eye-simulated object of interest extraction algorithm is used to extract the region of interest from the denoised 3D point cloud data;

[0009] The human eye-simulated object of interest compression algorithm is used to retain the high resolution of the extracted region of interest and perform non-uniform compression on the non-interest region.

[0010] Preferably, the process of performing Gaussian filtering denoising on the original three-dimensional point cloud data includes:

[0011] Estimate the noise level of each point in the original point cloud data;

[0012] Perform Gaussian fitting on the local neighborhood of each point to determine its mean and variance;

[0013] Use Gaussian model to denoise each point;

[0014] Update the position of each point to reflect the denoising result.

[0015] Preferably, the human eye-simulating object of interest extraction algorithm utilizes an encoder-decoder structure to simulate the context information of the point cloud through a three-dimensional point cloud context attention change module encoder and a scene context attention change change decoder.

[0016] Preferably, the process of retaining the high resolution of the extracted region of interest by using a human eye-simulated target of interest compression algorithm and performing non-uniform compression on the non-interest region includes:

[0017] Using the three-dimensional retinal variable resolution positive change model, the point cloud data is converted from the Cartesian coordinate system to the polar coordinate system, and the polar coordinates are transformed logarithmically;

[0018] Using the 3D retinal variable resolution compression model, the voxel compression point cloud method is used to achieve uniform density distribution of point cloud data.

[0019] The point cloud in log polar coordinates is converted back to the point cloud in Cartesian coordinates using the 3D retinal variable resolution inverse model.

[0020] The point cloud is fused using the 3D retinal variable resolution point cloud fusion model.

[0021] Preferably, the point cloud data is converted from a Cartesian coordinate system to a polar coordinate system, and the formula for the logarithmic polar coordinate change includes:

[0022]

[0023] θ=arctan2(y-y0,x-x0)

[0024]

[0025] Where r is the distance from the point to the origin, θ is the angle between the projection of the point on the XY plane and the X-axis, and φ is the angle between the point and the Z-axis;

[0026] The calculation formula for the point cloud in logarithmic polar coordinates is:

[0027] log r =log(r+ε)

[0028] Where ∈ is a constant.

[0029] Preferably, the process of achieving uniform density distribution of point cloud data by voxel compression point cloud method using a three-dimensional retinal variable resolution compression model includes:

[0030] Divide the point cloud space into voxel grids of equal size, where each voxel is a cubic area and the voxel size is set according to application requirements;

[0031] Replace all points in each voxel with a representative point, which is the centroid or center point of the points in the voxel;

[0032] The original point cloud is downsampled to fewer points by point replacement, maintaining the overall structure and shape of the point cloud. Preferably, the formula for converting the point cloud in logarithmic polar coordinates back to the point cloud in Cartesian coordinates is:

[0033] x=(e logr -ε)sin(φ)cos(θ)+x0

[0034] y=(e logr -ε)sin(φ)sin(φ)+y0

[0035] z=(e logr -ε)cos(φ)+z0

[0036] Among them, (x, y, z) are the coordinates in the Cartesian coordinate system, and (x0, y0, z0) is the center point of the region of interest.

[0037] Preferably, the process of fusing point clouds includes:

[0038] The point cloud data of all targets of interest are superimposed in sequence to form a fused point cloud;

[0039] Establish a KD tree index to organize the spatial data of the fused point cloud to facilitate efficient spatial query;

[0040] Use KD tree to perform radius search to find the neighboring points of each point in the fused point cloud;

[0041] Remove duplicate points in the fused point cloud by comparing the indices of neighboring points.

[0042] The present invention also provides a computer storage medium having computer program instructions stored thereon. When the computer program instructions are executed by a processor, the method for compressing three-dimensional point cloud data simulating the human eye is implemented.

[0043] Compared with the prior art, the present invention has the following advantages and technical effects:

[0044] The present invention provides a method for compressing three-dimensional point cloud data simulating the human eye. First, the original point cloud data is obtained and Gaussian filtering denoising is performed on the original point cloud data. Second, the denoised point cloud is extracted from the region of interest using a human eye-simulating target of interest extraction algorithm. Finally, the extracted region of interest is retained at high resolution using a human eye-simulating target of interest compression algorithm, while non-uniform compression is performed on the non-interested region.

[0045] The present invention obtains the region of interest in the three-dimensional data based on the retinal attention perception mechanism of the human eye, and then performs high resolution on the region of interest of the three-dimensional point cloud target, and performs non-uniform data redundancy compression on the three-dimensional point cloud region of no interest, thereby reducing the amount of data involved in the non-interest region, solving the inefficiency of the current point cloud data processing algorithm with fixed resolution when processing the region of interest and the non-interest region, reducing the amount of data involved in the non-interest region, thereby effectively improving the quality of the three-dimensional point cloud data, and improving the recognition accuracy and speed in the field of three-dimensional target recognition. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] The accompanying drawings, which constitute part of this application, are intended to provide a further understanding of this application. The exemplary embodiments and descriptions of this application are intended to explain this application and do not constitute an improper limitation on this application. In the accompanying drawings:

[0047] Figure 1 This is an overall flow chart of the human eye-simulating three-dimensional point cloud data compression method according to an embodiment of the present invention;

[0048] Figure 2 This is a flow chart of a human eye-simulating object of interest compression algorithm according to an embodiment of the present invention;

[0049] Figure 3 Schematic diagram of forward transformation and inverse transformation of human eye-simulated resolution change according to an embodiment of the present invention;

[0050] Figure 4 This is a schematic diagram of the complete process results of using the human eye-simulating three-dimensional point compression method in the field of autonomous driving according to an embodiment of the present invention. DETAILED DESCRIPTION

[0051] It should be noted that, in the absence of conflict, the embodiments and features of the embodiments in this application can be combined with each other. The present application will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0052] It should be noted that the steps shown in the flowcharts of the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and that, although a logical order is shown in the flowcharts, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0053] Example 1

[0054] like Figure 1 As shown, this embodiment provides a method for compressing three-dimensional point cloud data simulating the human eye, including:

[0055] S1. Obtain original 3D point cloud data and perform Gaussian filtering and denoising on the original point cloud data;

[0056] Specifically, the original three-dimensional point cloud data can be obtained from a point cloud file (in formats such as pcd, ply, xyz, etc.) or directly from a lidar device.

[0057] Furthermore, the process of performing Gaussian filtering denoising on the original 3D point cloud data includes:

[0058] Estimate the noise level of each point in the original point cloud data;

[0059] Perform Gaussian fitting on the local neighborhood of each point to determine its mean and variance;

[0060] Use Gaussian model to denoise each point;

[0061] Update the position of each point to reflect the denoising result.

[0062] Specifically, the point cloud Gaussian filtering algorithm is used to remove noise from the point cloud data while retaining the edge and feature information of the point cloud data.

[0063] The basic idea of ​​3D Gaussian denoising is to use the characteristics of Gaussian distribution to estimate the noise level at each point and perform denoising accordingly. In 3D space, the noise at each point can be considered as a random variable with a Gaussian distribution. The probability density function of the Gaussian distribution is given by the following formula:

[0064]

[0065] Where μ is the mean of the Gaussian distribution, representing the true position of the point, and σ is the standard deviation, representing the intensity of the noise. In a 3D point cloud, the noise of each point can be described by its Gaussian distribution on the three coordinate axes. For a point p = (x, y, z), its Gaussian model can be expressed as:

[0066]

[0067] The denoising process usually includes the following steps:

[0068] Noise estimation: First, the noise level is estimated for each point in the point cloud, which is usually achieved by calculating the variance of the points in a local neighborhood.

[0069] Gaussian Fitting: Perform a Gaussian fit on the local neighborhood of each point to determine its mean and variance.

[0070] Noise removal: De-noise each point using a Gaussian model, usually achieved through weighted averaging or filtering. The weight is usually inversely proportional to the distance from the point to the center of the neighborhood, determined by the Gaussian kernel.

[0071] Result update: Update the position of each point to reflect the denoising result.

[0072] S2, using the human eye-simulated target extraction algorithm to extract the region of interest from the denoised 3D point cloud data;

[0073] Furthermore, the human eye-simulating object of interest extraction algorithm utilizes an encoder-decoder structure to simulate the context information of the point cloud through a three-dimensional point cloud context attention change module encoder and a scene context attention change change decoder.

[0074] Specifically, the human eye-simulating object of interest extraction algorithm is a point cloud region of interest ROI detection algorithm based on a self-attention mechanism. The algorithm uses an encoder-decoder structure and makes full use of the self-attention mechanism to simulate multi-scale and scene context feature information to realize ROI detection of the point cloud of interest.

[0075] The point cloud region of interest (ROI) detection algorithm uses a self-attention mechanism: The encoder first extracts multi-level features from the point cloud, and the decoder gradually restores the resolution of the point cloud. Specifically, given a fixed view V = {v1, v2, …, vN} of N points, where each V is a raw feature (such as XYZ position information and RGB color information), it is input into a multi-stage encoder to understand the semantic information of the entire scene. The encoder uses two different transformers to extract local context and simulate global context.

[0076] The decoder decodes the features produced by the encoder through upsampling and transformers, and then integrates the encoder's multi-scale features through the context aggregation module to learn global scene context information. Specifically, the decoder upsamples the encoder's output value and combines this value with the encoder's output to form the decoder through the transformer. The context aggregation module takes the output of the multi-stage encoder as input and connects them together as global context to form aggregated information.

[0077] Finally, the results are fed into the prediction head, which converts the output features into predictions of interest, ultimately generating the point cloud ROI. Specifically, the encoder output is fused and concatenated with the context aggregation module. A multi-layer perceptron (MLP) then performs feature fusion and feature conversion on features from different sources, ultimately outputting the prediction results to form the point cloud ROI.

[0078] S3, using the human eye-simulated interest target compression algorithm to retain the high resolution of the extracted interest region and perform non-uniform compression on the non-interest region. The flowchart of the human eye-simulated interest target compression algorithm is as follows: Figure 2 shown.

[0079] Specifically, by studying the principles of retinal imaging in the human eye, it was found that the human retina has a non-uniform structure, with more central cells and high-resolution characteristics, and the surrounding cells gradually become lower, which can compress the data of non-targets of interest. In this way, clear imaging can be achieved in the central gaze area while early warning can be given in the surrounding non-interest areas, achieving rapid imaging. This lays the foundation for resolving the contradiction between the amount of data and real-time performance of three-dimensional target recognition technology.

[0080] Furthermore, the process of retaining the high resolution of the extracted region of interest by using the human eye-simulated target of interest compression algorithm and performing non-uniform compression on the non-interest region includes:

[0081] S31. using a three-dimensional retinal variable resolution positive change model, converting the point cloud data from a Cartesian coordinate system to a polar coordinate system, and performing logarithmic polar coordinate transformation;

[0082] The core of the human eye-simulated retinal variable resolution sampling model is logarithmic polar coordinate transformation, which is the mainstream concept for achieving spatial variable resolution imaging. The human eye-simulated sampling model is a geometric image transformation that simulates the mapping relationship between the human eye's retina and the brain's visual cortex.

[0083] First, convert the point cloud data from the Cartesian coordinate system to the polar coordinate system. For a point P (x, y, z) in three-dimensional space, the polar coordinates (r, θ, φ) of the point of interest P0 (x0, y0, x0) can be converted from the Cartesian coordinate system to the polar coordinate system using the following formula:

[0084]

[0085] θ=arctan2(y-y0,x-x0) (4)

[0086]

[0087] Where r is the distance from the point to the origin, θ is the angle between the projection of the point on the XY plane and the X-axis, and φ is the angle between the point and the Z-axis.

[0088] In order to further process the point cloud data in the polar coordinate system, the logarithmic transformation is introduced. The logarithmic transformation can effectively compress the data range and reduce the dynamic range of the data. The logarithmic polar coordinates (logr,θ,φ) can be calculated by the following formula to obtain the point cloud in the logarithmic polar coordinates:

[0089] log r =log(r+ε) (6)

[0090] Here, ε is a small constant used to prevent taking the logarithm of 0.

[0091] S32. Using a three-dimensional retinal variable resolution compression model, a voxel compression point cloud method is used to achieve uniform density distribution of point cloud data;

[0092] Specifically, in the field of visual perception, the structural characteristics of the human retina are characterized by a higher visual resolution in the fovea, while the resolution gradually decreases as it transitions to the peripheral areas. This non-uniform distribution of visual sensitivity inspires the sampling strategy in point cloud data processing. In the logarithmic polar coordinate system, point cloud data can be parameterized by polar radius and angle to effectively simulate this non-uniform resolution characteristic of the human eye. Based on this, this embodiment adopts a voxel compression point cloud method to achieve a uniform density distribution of point cloud data. The specific process is as follows:

[0093] S3201. Spatial Partitioning: First, the point cloud space is divided into a grid of voxels of equal size, with each voxel representing a cubic region. The key to this step is determining the voxel size, which directly impacts the effectiveness and efficiency of downsampling. Voxel size is typically set by the user based on application requirements. Smaller voxels preserve more detail but increase computational effort, while larger voxels reduce computational effort but may lose some detail.

[0094] S3202, point replacement: For each voxel, replace all points in it with a representative point. This representative point is usually the center of mass or center point of these points. The formula for calculating the center of mass is as follows

[0095]

[0096] Among them, (x i ,y i ,z i ) is the coordinate of the i-th point in the voxel, and m is the total number of points in the voxel.

[0097] S3203, downsampling: Through the above process, the original point cloud is downsampled to fewer points while maintaining the overall structure and shape of the point cloud.

[0098] S33, using a three-dimensional retinal variable resolution inverse change model, converting the point cloud in the logarithmic polar coordinate system back to a point cloud in the Cartesian coordinate system;

[0099] In order to facilitate the intuitive understanding of deep learning networks and human visual perception, it is also necessary to convert the point cloud in logarithmic polar coordinates back to the point cloud in Cartesian coordinates, as shown in the formula:

[0100] x=(e logr -ε)sin(φ)cos(θ)+x0

[0101] y=(e logr -ε)sin(φ)sin(φ)+y0

[0102] z=(e logr -ε)cos(φ)+z0 (8)

[0103] Where (x, y, z) is the coordinate in the Cartesian coordinate system, and (x0, y0, z0) is the center point of the region of interest.

[0104] S34. Use the three-dimensional retinal variable resolution point cloud fusion model to fuse the point cloud.

[0105] When there are multiple targets of interest, the point clouds need to be fused. The fusion steps are:

[0106] First, the point cloud is overlaid: all the objects of interest are overlaid in sequence to form a point cloud fusion point cloud, and then the point cloud is deduplicated: first, a KD tree index is established for the point cloud. The KD tree (k-dimensional tree) is a data structure used to organize k-dimensional spatial data, which can efficiently perform spatial queries. In the point cloud deduplication operation, the KD tree can be used to quickly find the neighboring points of a given point. Then, for each point in the point cloud, the KD tree is used to perform a radius search to find the neighboring points of each point. Next, the point cloud is deduplicated: duplicate points are removed by comparing the indexes of the neighboring points. Finally, a compressed point cloud is formed.

[0107] The effect of the compression algorithm simulating the target of interest of the human eye is shown in the figure. Figure 3 shown.

[0108] S4. Use a display program to display the compression result.

[0109] like Figure 4 The figure shows the complete flowchart of human-eye-like 3D point cloud data compression in the field of autonomous driving using the public KITTI dataset, as well as its verification in target recognition applications.

[0110] (a) RGB image captured by the visible light camera of the KITTI dataset;

[0111] (b) The point cloud image formed after denoising after the original point cloud data is input into the point cloud preprocessing algorithm;

[0112] (c) Two regions of interest are formed by using the human eye-simulated object of interest extraction algorithm;

[0113] (d) is the point cloud compressed data obtained after the human eye-simulated target compression algorithm;

[0114] (e) The result obtained by feeding the compressed input to the open source PointRCNN 3D object recognition algorithm.

[0115] After compression using the above-mentioned human eye-simulating 3D point cloud data compression method, the original point cloud number was compressed from 19,388 to 798, with a compression ratio of 24.3 times, effectively identifying cars, pedestrians, and cyclists. Compared with the point cloud method formed by traditional imaging, the above scheme reduces the recognition speed from the original 102ms to 14.7ms, increasing the recognition rate by 6.94 times. In addition, in the KITTI dataset, pedestrian target recognition was performed. The average recognition target accuracy in the three modes of simple, medium, and difficult was increased from the original 52.28% average recognition accuracy to 53.62%, an increase of 1.34% accuracy. The reason for this is that the resolution adaptive processing method has the effect of compressing redundancy, making it possible to more fully extract the effective feature information of the point cloud, thereby improving the accuracy of target detection and recognition.

[0116] The present invention obtains the target of interest in a low-resolution image and maps it to a high-resolution image, then performs a human-eye-like resolution change on the high-resolution image, thereby effectively retaining the high resolution of the attention image and compressing the data in the non-attention area, greatly improving the information utilization efficiency of the system. Finally, the human-eye-like resolution-changed image is passed into the target recognition network to identify the target in the field of view, effectively improving the accuracy of target recognition and meeting the large-field-of-view, highly dynamic and complex environment recognition tasks in the field of autonomous driving.

[0117] The above are merely preferred embodiments of the present application, but the scope of protection of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this application should be included in the scope of protection of the present application. Therefore, the scope of protection of the present application should be based on the scope of protection of the claims.

Claims

1. A method for compressing three-dimensional point cloud data simulating the human eye, characterized in that: The following steps are involved: Obtain the original 3D point cloud data of autonomous driving and perform Gaussian filtering and denoising on the original 3D point cloud data; The human eye-simulated object of interest extraction algorithm is used to extract the region of interest from the denoised 3D point cloud data; The human eye-simulated object of interest extraction algorithm uses an encoder-decoder structure to simulate the context information of the point cloud through a 3D point cloud context attention change module encoder and a scene context attention change change decoder; The human eye-like object of interest compression algorithm is used to retain the high resolution of the extracted region of interest and perform non-uniform compression on the non-interest region. Specifically include: Using the three-dimensional retinal variable resolution positive change model, the point cloud data is converted from the Cartesian coordinate system to the polar coordinate system, and the polar coordinates are transformed logarithmically; Using the 3D retinal variable resolution compression model, the voxel compression point cloud method is used to achieve uniform density distribution of point cloud data. The point cloud in log polar coordinates is converted back to the point cloud in Cartesian coordinates using the 3D retinal variable resolution inverse model. The point cloud is fused using the 3D retinal variable resolution point cloud fusion model; The formulas for converting point cloud data from Cartesian coordinates to polar coordinates and logarithmic polar coordinates are: θ=arctan2(y-y0,x-x0) Where r is the distance from the point to the origin, θ is the angle between the projection of the point on the XY plane and the X axis, φ is the angle between the point and the Z axis, (x, y, z) are the coordinates in the Cartesian coordinate system, and (x0, y0, z0) is the center point of the region of interest; The calculation formula for the point cloud in logarithmic polar coordinates is: log r =log(r+ε) Where ε is a constant.

2. The human eye-simulating three-dimensional point cloud data compression method according to claim 1, characterized in that: The process of Gaussian filtering and denoising the original 3D point cloud data includes: Estimate the noise level of each point in the original point cloud data; Perform Gaussian fitting on the local neighborhood of each point to determine its mean and variance; Use Gaussian model to denoise each point; Update the position of each point to reflect the denoising result.

3. The human eye-simulating three-dimensional point cloud data compression method according to claim 1, characterized in that: The process of achieving uniform density distribution of point cloud data by voxel compression point cloud method using the 3D retinal variable resolution compression model includes: Divide the point cloud space into voxel grids of equal size, where each voxel is a cubic area and the voxel size is set according to application requirements; Replace all points in each voxel with a representative point, which is the centroid or center point of the points in the voxel; The original point cloud is downsampled to fewer points through point replacement, maintaining the overall structure and shape of the point cloud.

4. The human eye-simulating three-dimensional point cloud data compression method according to claim 1, characterized in that: The formula for converting the point cloud in logarithmic polar coordinates back to the point cloud in Cartesian coordinates is: x=(e logr -ε)sin(φ)cos(θ)+x0 y=(e logr -ε)sin(φ)sin(φ)+y0 z=(e logr -ε)cos(φ)+z0 Among them, (x, y, z) are the coordinates in the Cartesian coordinate system, and (x0, y0, z0) is the center point of the region of interest.

5. The human eye-simulating three-dimensional point cloud data compression method according to claim 1, characterized in that: The process of fusing point clouds includes: The point cloud data of all targets of interest are superimposed in sequence to form a fused point cloud; Establish a KD tree index to organize the spatial data of the fused point cloud to facilitate efficient spatial query; Use KD tree to perform radius search to find the neighboring points of each point in the fused point cloud; Remove duplicate points in the fused point cloud by comparing the indices of neighboring points.

6. A computer storage medium, characterized in that The computer storage medium stores computer program instructions, which, when executed by a processor, implement the human eye-simulating three-dimensional point cloud data compression method according to any one of claims 1 to 5.

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