Non-perceptual lightweight three-dimensional data processing method, device and storage medium

Abstract

The present application discloses a non-perceptual lightweight three-dimensional data processing method, device, and storage medium, relating to the technical field of three-dimensional data processing. The non-perceptual lightweight three-dimensional data processing method includes: obtaining key feature information of pre-processed three-dimensional model data, and performing importance evaluation processing on the key feature information based on a graph theory algorithm to obtain an importance score of the key feature information; after removing the key feature information whose importance score is less than a preset score, performing data simplification processing on the key feature information based on a preset simplification algorithm; determining a compression strategy based on the data type of the three-dimensional model data, and after encoding the three-dimensional model data, compressing the encoded data based on the compression strategy to obtain compressed data. Performing dimensionality reduction and simplification processing on the three-dimensional data based on the importance score of the key feature information improves data processing efficiency.

Description

Technical Field

[0001] The present application relates to the field of three-dimensional data processing technology, and in particular to a method, device and storage medium for imperceptible lightweight three-dimensional data processing. Background Art

[0002] When processing 3D model data, the algorithms typically used include geometric simplification and texture compression. Geometric simplification involves using algorithms to analyze the geometric features of a 3D model and remove details that have little impact on the overall visual effect. This allows the simplified model to remain visually similar to the original model, while significantly reducing geometric information such as the number of vertices and faces, thereby reducing storage requirements and rendering complexity. Texture compression utilizes a texture compression algorithm to compress the texture image of a 3D model. This allows the compressed texture image to maintain a certain level of visual quality while significantly reducing storage requirements and speeding up texture loading and rendering.

[0003] However, existing geometric simplification and texture compression methods often require manual parameter setting, such as error thresholds and compression ratios. Manual parameter setting relies on subjective judgment. Excessive simplification can cause distortion of the 3D model's shape and loss of important details, while inappropriate compression ratios can lead to loss of texture detail. Consequently, current 3D model data processing methods suffer from a low degree of automation and inefficiency.

[0004] The above content is only used to assist in understanding the technical solution of this application and does not constitute an admission that the above content is prior art. Summary of the Invention

[0005] The main purpose of this application is to provide a non-perceptual lightweight three-dimensional data processing method, device and storage medium, aiming to solve the current technical problem of low efficiency in three-dimensional model data processing.

[0006] To achieve the above objectives, this application proposes a non-perceptual lightweight three-dimensional data processing method, which includes:

[0007] Acquire key feature information of the preprocessed three-dimensional model data, and perform importance evaluation processing on the key feature information based on a graph theory algorithm to obtain an importance score of the key feature information;

[0008] After removing the key feature information whose importance score is less than a preset score from the key feature information, performing data simplification processing on the key feature information based on a preset simplification algorithm;

[0009] A compression strategy is determined according to the data type of the three-dimensional model data, and after the three-dimensional model data is encoded, the encoded data is compressed based on the compression strategy to obtain compressed data.

[0010] In one embodiment, after the step of determining a compression strategy based on the data type of the 3D model data, and compressing the encoded data based on the compression strategy after encoding the 3D model data to obtain compressed data, the method further includes:

[0011] Respond to the decompression information sent by the client;

[0012] Read and decompress the data to be decompressed based on a preset decompression algorithm to obtain decompressed target three-dimensional model data;

[0013] The target three-dimensional model data is output to the client.

[0014] In one embodiment, the step of reading and decompressing the data to be decompressed based on a preset decompression algorithm to obtain the decompressed target three-dimensional model data includes:

[0015] Reading the characteristic information, metadata and the data to be decompressed during the compression processing of the three-dimensional model data based on the preset decompression algorithm;

[0016] The data to be decompressed is decompressed, and the decompressed data is restored based on the feature information and the metadata to obtain the target three-dimensional model data.

[0017] In one embodiment, after removing the key feature information having an importance score less than a preset score from the key feature information, the step of performing data simplification processing on the key feature information based on a preset simplification algorithm includes:

[0018] Determining, in the key feature information, feature information to be reduced in dimension whose importance score feature value is less than a preset score;

[0019] Performing dimensionality reduction processing on the feature information to be reduced based on a preset dimensionality reduction algorithm to remove the key feature information whose importance score is less than a preset score;

[0020] The key feature information is subjected to data simplification processing based on the preset simplification algorithm.

[0021] In one embodiment, the step of determining a compression strategy based on the data type of the three-dimensional model data, and compressing the encoded data after encoding the three-dimensional model data based on the compression strategy to obtain compressed data includes:

[0022] Acquire a data type of the three-dimensional model data, and determine the compression strategy associated with the data type;

[0023] encoding the three-dimensional model data based on a preset encoding algorithm to obtain the encoded data;

[0024] The coded data is compressed according to a compression algorithm corresponding to the compression strategy to obtain the compressed data.

[0025] In one embodiment, the steps of obtaining key feature information of the preprocessed three-dimensional model data and performing importance evaluation processing on the key feature information based on a graph theory algorithm to obtain an importance score of the key feature information include:

[0026] Acquire the key feature information of the preprocessed three-dimensional model data based on a geometric feature extraction method; or acquire the key feature information of the preprocessed three-dimensional model data based on a deep learning model;

[0027] The key feature information is subjected to importance evaluation processing based on a graph theory algorithm to obtain the importance evaluation information.

[0028] In one embodiment, before the step of obtaining key feature information of the preprocessed three-dimensional model data and performing importance evaluation processing on the key feature information based on a graph theory algorithm to obtain an importance score of the key feature information, the step further includes:

[0029] Receiving original three-dimensional model data, and performing noise removal processing on the original three-dimensional data based on Gaussian filtering, median filtering, bilateral filtering or wavelet transform to obtain denoised data;

[0030] Data reconstruction and simplification processing are performed based on the denoised data to obtain the pre-processed three-dimensional model data.

[0031] In one embodiment, after the step of receiving the original three-dimensional model data and performing noise removal processing on the original three-dimensional data based on Gaussian filtering, median filtering, bilateral filtering, or wavelet transform to obtain denoised data, the method further includes:

[0032] The denoised data is simplified based on a voxel grid simplification method to obtain the pre-processed three-dimensional model data.

[0033] In addition, to achieve the above-mentioned purpose, the present application also proposes a three-dimensional data processing device, which includes: a memory, a processor, and a computer program stored on the memory and runnable on the processor, and the computer program is configured to implement the steps of the imperceptible lightweight three-dimensional data processing method as described above.

[0034] In addition, to achieve the above-mentioned purpose, the present application also proposes a storage medium, which is a computer-readable storage medium, and a computer program is stored on the storage medium. When the computer program is executed by the processor, the steps of the imperceptible lightweight three-dimensional data processing method as described above are implemented.

[0035] One or more technical solutions proposed in this application have at least the following technical effects:

[0036] When processing three-dimensional model data, the key feature information of the three-dimensional model data is first obtained, including key feature points, edges, and surfaces used for computational processing of the three-dimensional model data. At the same time, the importance of these key feature information is evaluated and processed using a graph theory algorithm to obtain corresponding importance scores. This allows the removal of key feature information with an importance score less than a preset score. The remaining key feature information is then simplified based on a preset simplification algorithm, without the need for manual operation. Finally, a compression strategy corresponding to the three-dimensional model data is determined, and the encoded three-dimensional model data is compressed based on the compression strategy to obtain compressed data of the three-dimensional model data. This approach automatically analyzes the redundancy and importance of the three-dimensional data, dynamically adjusts compression parameters and strategies, and ensures an optimal balance between data compression rate and fidelity. This approach eliminates the need for manual user intervention or parameter setting, improves the efficiency of three-dimensional data processing and compression, and enables a seamless processing process. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0038] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0039] Figure 1 A flowchart illustrating the first embodiment of the non-perceptual lightweight three-dimensional data processing method of this application;

[0040] Figure 2 A schematic diagram of the process of data dimensionality reduction and simplification processing for the non-perceptual lightweight three-dimensional data processing method of this application;

[0041] Figure 3 A schematic diagram of the encoding and compression process of the non-perceptual lightweight three-dimensional data processing method of this application;

[0042] Figure 4A flowchart illustrating the second embodiment of the non-perceptual lightweight three-dimensional data processing method of this application;

[0043] Figure 5 A schematic diagram of an exemplary implementation flow provided for the second embodiment of the present application;

[0044] Figure 6 A flowchart illustrating the third embodiment of the non-perceptual lightweight three-dimensional data processing method of this application;

[0045] Figure 7 This is a schematic diagram of a process for extracting key features based on a deep learning model in the third embodiment of the present application;

[0046] Figure 8 This is a schematic diagram of a process for pre-processing original three-dimensional data in the fourth embodiment of the present application;

[0047] Figure 9 This is a schematic diagram of the device structure of the hardware operating environment involved in the imperceptible lightweight three-dimensional data processing method in the embodiment of the present application.

[0048] The purpose, features and advantages of this application will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0049] It should be understood that the specific embodiments described herein are merely used to explain the technical solutions of the present application and are not intended to limit the present application.

[0050] The main solution of the embodiment of the present application is to obtain key feature information of the pre-processed 3D model data, and perform importance evaluation processing on the key feature information based on a graph theory algorithm to obtain an importance score of the key feature information;

[0051] After removing the key feature information whose importance score is less than a preset score from the key feature information, performing data simplification processing on the key feature information based on a preset simplification algorithm;

[0052] A compression strategy is determined according to the data type of the three-dimensional model data, and after the three-dimensional model data is encoded, the encoded data is compressed based on the compression strategy to obtain compressed data.

[0053] Existing methods for geometric simplification and texture compression often require manual parameter setting, such as error thresholds and compression ratios. Manually setting these parameters relies on subjective judgment. Excessive simplification can cause the 3D model's shape to distort, losing important details. Inappropriate compression can also lead to loss of texture detail. Consequently, current 3D model data processing methods have a low degree of automation and suffer from poor processing performance.

[0054] This application provides a solution that automatically obtains key feature information from pre-processed 3D model data and uses graph theory algorithms to score the importance of these key feature information. Based on the importance assessment information, dimensionality reduction is performed on key features with lower importance, that is, key feature information with lower importance scores is removed from the key feature information to reduce the amount of data required for subsequent calculations. The remaining data after dimensionality reduction is then simplified and compressed. This process does not require user participation, making 3D model data processing unconscious. The 3D model data is processed based on unconscious intelligent optimization processing, effectively improving the processing effect of the 3D model data.

[0055] It should be noted that the execution subject of this embodiment can be a computing service device with data processing, network communication, and program execution functions, such as a tablet computer, personal computer, mobile phone, etc., or an electronic device capable of performing the above functions, a three-dimensional data processing device, etc. The following uses a three-dimensional data processing device as an example to illustrate this embodiment and the following embodiments.

[0056] In order to better understand the technical solution of the present application, a detailed description will be given below in conjunction with the accompanying drawings and specific implementation methods.

[0057] The present application embodiment provides a non-perceptual lightweight three-dimensional data processing method, referring to Figure 1 , Figure 1 This is a flow chart of the first embodiment of the non-perceptual lightweight three-dimensional data processing method of this application.

[0058] In this embodiment, the non-perceptual lightweight three-dimensional data processing method includes steps S10 to S30:

[0059] Step S10: Acquire key feature information of the preprocessed three-dimensional model data, and perform importance evaluation processing on the key feature information based on a graph theory algorithm to obtain an importance score of the key feature information.

[0060] It should be noted that key feature data refers to the model's feature points, edges (lines), and faces, which are details and attribute information that are crucial for model recognition, analysis, rendering, or subsequent applications. For example, in a three-dimensional cube model, its key feature information contains at least eight feature points, lines between faces, and other content that can keep the shape of the cube unchanged. The importance of feature data is assessed based on its contribution to the overall shape and details of the three-dimensional model. The importance score is usually expressed in numerical form, such as 0-100 points, or in a graded form, such as AD. Before extracting the key feature information of the three-dimensional model data, it is usually necessary to preprocess the three-dimensional model data to remove the noise of the three-dimensional model data while reducing the repeated or highly similar parts of the data, that is, to reduce redundant data.

[0061] In this embodiment, key feature information of the pre-processed three-dimensional model data can be extracted through the built-in algorithm of the three-dimensional data processing device, or key features such as edges, faces, etc. can be extracted through the built-in deep learning model of the device, such as the convolutional neural network (CNN).

[0062] Furthermore, after extracting the key feature information of the three-dimensional model data, there is data whose key feature information has little impact on the subsequent calculation of the three-dimensional model. For example, when the three-dimensional model data is a building, the key feature information obtained includes hundreds of vertex data of the building. In the actual calculation process, at least four key feature points (representing the shape of the top of the model) can be used to perform spatial calculations of the three-dimensional model. The importance of the remaining data is relatively low, which usually affects the storage and query efficiency of subsequent data. Therefore, it is necessary to reduce the number of vertices and faces in the model through data dimensionality reduction and model simplification. As for the key feature information such as the top surface, edges and number of faces that need to be reduced, they are selected based on the importance evaluation information of these key feature information.

[0063] Specifically, after obtaining key feature information, the importance of each feature needs to be evaluated based on its contribution to the overall shape and details. During this evaluation process, graph-theory-based algorithms, such as PageRank or HITS (Hyperlink-Induced Topic Search), are used to assess the importance of features. For example, for node importance assessment, graph-theory algorithms such as PageRank, HITS, and Betweenness Centrality are used to assess the importance of each node. Similarly, for edge importance assessment, algorithms such as Edge Betweenness Centrality can be used to assess the importance of each edge. For face features, the importance of the entire region can be assessed based on the importance of the nodes and edges it contains. For example, the average or weighted sum of the importance of all nodes in the region can be calculated, and the result is used as the region's importance score.

[0064] It should be noted that after acquiring the key feature information, the three-dimensional data processing device automatically calculates the importance evaluation information of these features based on an algorithm, so as to perform subsequent data dimensionality reduction processing based on the importance evaluation information.

[0065] It is understandable that each key feature information corresponds to importance evaluation information. For example, if the extracted key feature information contains 100 vertices, then each of the 100 vertices corresponds to 100 importance scores.

[0066] Step S20 : After removing the key feature information whose importance score is less than a preset score from the key feature information, data simplification processing is performed on the key feature information based on a preset simplification algorithm.

[0067] In this embodiment, after obtaining the key feature information and the importance score, it is necessary to perform lightweight processing on the key feature information of the three-dimensional model data to reduce the redundancy of the three-dimensional model data. Lightweight processing refers to the dimensionality reduction of the data and the simplification of the model data. The dimensionality reduction process is to remove the key feature information with a low importance score, and the simplification process is to further reduce the amount of data that needs to be stored after the dimensionality reduction process. Therefore, the dimensionality reduction of the key feature information is completed by removing the key feature information whose importance score is less than the preset score. Among them, if the importance score is a numerical system, the feature information to be reduced in dimension with an importance score less than the preset score is selected from the key feature data, wherein in the numerical system, generally the larger the value, the higher the degree of importance.

[0068] Optionally, if the importance score is a letter grade, data to be reduced in dimension with a feature grade of a preset grade is selected and deleted from the key feature data, wherein the preset grade is usually the grade that requires dimensionality reduction processing.

[0069] Therefore, as an optional implementation, step S20 includes steps S21 to S23:

[0070] Step S21 : determining, in the key feature information, feature information to be reduced in dimension whose importance score feature value is less than a preset score.

[0071] Step S22: performing dimensionality reduction processing on the feature information to be reduced based on a preset dimensionality reduction algorithm, so as to remove the key feature information whose importance score is less than a preset score in the key feature information.

[0072] In this embodiment, according to the importance scores of the features, a preset dimensionality reduction algorithm such as principal component analysis (PCA) and multidimensional scaling (MDS) may be used to perform dimensionality reduction processing on the three-dimensional data.

[0073] For example, please refer to Figure 2 When selecting feature data to be reduced in dimensionality and performing dimensionality reduction processing on it, the data can be reduced in dimensionality through preset dimensionality reduction algorithms such as principal component analysis (PCA) / multi-dimensional scaling (MDS) and other algorithm technologies.

[0074] Optionally, nonlinear dimensionality reduction techniques such as ISOMAP (isometric mapping) or LLE (local linear embedding) can be used to reduce the dimensionality of key feature information, thereby better preserving the local and global structural information of the data.

[0075] Step S23: performing data simplification processing on the key feature information based on the preset simplification algorithm.

[0076] In this embodiment, after the data is subjected to dimensionality reduction processing, the reduced data needs to be further simplified, such as using a preset simplification algorithm such as the quadratic error metric (QEM) algorithm to remove unnecessary vertices and faces while maintaining the basic shape and key features of the model, thereby obtaining the target key feature information after the data simplification processing.

[0077] For example, please refer to Figure 2 After the key feature information is reduced in dimension, the other key feature information after dimension reduction is simplified based on the preset simplification algorithm QEM algorithm, thereby obtaining a simplified three-dimensional model.

[0078] Optionally, in addition to the QEM algorithm, a progressive mesh simplification algorithm, such as the Edge Collapse algorithm, can be used to simplify the model by gradually removing edges and vertices that have the least impact on the model shape.

[0079] In this embodiment, the dimensionality reduction and simplification of the three-dimensional model data are performed by scoring the importance of key feature information. In this process, the importance of each feature information is automatically assessed and the simplification algorithm that meets the requirements is automatically selected for processing. There is no need for the user to manually set the simplification parameters, thereby improving the efficiency of automated processing.

[0080] Step S30 : determining a compression strategy according to the data type of the three-dimensional model data, and after encoding the three-dimensional model data, compressing the encoded data based on the compression strategy to obtain compressed data.

[0081] The compression strategy refers to the compression algorithm selected when compressing the 3D model data after dimensionality reduction and simplification. Among them, the 3D data processing equipment has a variety of different compression algorithms pre-installed.

[0082] Therefore, in this embodiment, when compressing 3D model data, a compression algorithm that meets the requirements can be selected based on the data type of the 3D model data, eliminating the need for the user to manually set compression parameters and select a corresponding compression algorithm, thereby improving compression efficiency. However, when compressing 3D model data, encoding is typically required before data compression to improve subsequent compression efficiency.

[0083] Specifically, step S30 includes steps S31 to S33:

[0084] Step S31: Acquire the data type of the three-dimensional model data, and determine the compression strategy associated with the data type.

[0085] In this embodiment, 3D model data includes types such as point clouds, meshes, and voxels. Each data type corresponds to different data characteristics, so an associated compression strategy can be determined based on the data type of the 3D model data. For example, if the current compression strategies include A, B, and C, strategy A corresponds to the point cloud data type, strategy B corresponds to the mesh data type, and so on. The specific compression strategy information is not limited in this application, and the corresponding relationship between strategies and types is pre-set by the user in the device.

[0086] Step S32: encoding the three-dimensional model data based on a preset encoding algorithm to obtain the encoded data.

[0087] For example, please refer to Figure 3After specifying the corresponding compression strategy in multiple efficient compression algorithms, the dimensionality-reduced and lightweight three-dimensional model data is encoded through preset coding algorithms such as predictive coding or transform coding, thereby obtaining encoded data for subsequent compression processing.

[0088] Step S33: compress the encoded data according to the compression algorithm corresponding to the compression strategy to obtain the compressed data.

[0089] Please continue to refer to Figure 3 The compression algorithm corresponding to the compression strategy may include a Huffman coding algorithm or a run-length coding algorithm. Based on this, when the compression algorithm is applied based on the compression strategy, the encoded data is compressed by the Huffman coding algorithm or the run-length coding algorithm to obtain the compressed data, thus completing the compressed data storage.

[0090] Optionally, in addition to Huffman coding and run-length coding, more efficient compression algorithms can be introduced, such as dictionary compression algorithms such as LZ77 and LZ78, or prediction-based coding technologies such as the EBCOT (Embedded Block Coding Optimized Truncation) algorithm in JPEG2000 can be used to improve the compression ratio and compression efficiency.

[0091] This embodiment selects a corresponding compression strategy based on the type of three-dimensional model data. After encoding the three-dimensional model data based on a preset encoding algorithm, the encoded data is automatically compressed using the compression algorithm of the automatically selected compression strategy, without the need for manual operation, thereby improving data processing efficiency.

[0092] It should be noted that during the data processing and compression process, the processing equipment will also use technical means such as encrypted transmission, access control, and data desensitization to ensure the security of three-dimensional data and the protection of user privacy during transmission, storage, and processing.

[0093] This embodiment provides a non-perceptual lightweight three-dimensional data processing method that automatically obtains key feature information of pre-processed three-dimensional model data, then performs dimensionality reduction processing on the key feature information based on the importance score of the key feature information to remove irrelevant data with low importance, that is, data that affects the calculation of the three-dimensional data. The remaining key feature information after the dimensionality reduction processing is then simplified to reduce the number of points and facets in the three-dimensional model data, further reducing the complexity of the processed data. Finally, a corresponding compression strategy is selected based on the type of three-dimensional model data, and after the lightweight three-dimensional model data is encoded, the three-dimensional data is compressed using the compression algorithm corresponding to the compression strategy to obtain compressed data for subsequent spatial calculations. Based on this, the user is unaware of the simplification and compression process of the three-dimensional model data, and there is no need for the user to manually set the simplification parameters and compression parameters of the three-dimensional model data, thereby improving the processing efficiency of the three-dimensional model data.

[0094] Based on the first embodiment of the present application, in the second embodiment of the present application, the same or similar contents as those in the first embodiment can be referred to the above introduction and will not be described in detail later. Figure 4 After step S30, the non-perceptual lightweight three-dimensional data processing method further includes steps S40 to S60:

[0095] Step S40, responding to the decompression information sent by the client;

[0096] In this embodiment, the decompression information sent by the user on the client side can be responded to, and based on an efficient decompression algorithm, the decompression process can be completed in a short time, thus reducing the user's waiting time and improving the perception between the user and the processing device after the data compression process.

[0097] Understandably, after compressing 3D model data, it often fails to fully consider the user's perception needs, resulting in the processed model deviating from user expectations in some aspects. Therefore, in this embodiment, by quickly displaying the data in response to the user's actual request, the user's perception of the compressed 3D model data is improved.

[0098] Step S50 : reading and decompressing the data to be decompressed based on a preset decompression algorithm to obtain decompressed target three-dimensional model data.

[0099] In this embodiment, the decompression algorithm can be used to directly read and decompress the data to be decompressed, thereby obtaining the target 3D model data. During the decompression process, the decompressed 3D data can also be restored to its original quality using pre-decompression feature information and metadata retained during compression, such as detail enhancement, texture restoration, and decoding, to improve the decompression effect.

[0100] Therefore, as an optional implementation, the feature information, metadata, and the data to be decompressed during the compression processing of the three-dimensional model data can be read based on a preset decompression algorithm, and then the data to be decompressed can be decompressed, and the decompressed data can be restored based on the feature information and metadata to obtain the target three-dimensional model data. It can be understood that the feature information and metadata are stored in a database table, and the metadata can be used to enhance the data details, and the feature information can be used to decode the data. In the decompression process, in order to improve the data decompression efficiency, the decompression algorithm can be a decompression algorithm based on GPU (Graphics Processing Unit) acceleration, which uses the parallel processing capabilities of the GPU to accelerate the decompression process and reduce user waiting time.

[0101] Please refer to Figure 5After the user initiates a decompression request, the device's server receives the request and starts the decompression algorithm to improve decompression efficiency through GPU acceleration and parallel processing. It then loads the compressed data from the storage medium, that is, reads the compressed data, and decompresses the compressed data, including data decoding, data recovery, and detail enhancement processing. After the decompression is completed, the decompressed data is generated.

[0102] Step S60: output the target three-dimensional model data to the client.

[0103] Please continue to refer to Figure 5 After obtaining the decompressed data, the decompressed data is sent to the user device, thereby displaying the decompressed three-dimensional model in the client.

[0104] This embodiment provides a non-perceptual lightweight three-dimensional data processing method. After responding to the data decompression request of the user terminal, non-perceptual decompression processing of the three-dimensional model data is performed. By setting an efficient decompression algorithm, the decompression process is ensured to be completed in a short time, reducing the user waiting time. During the decompression process, known information is used to decompress, decode and enhance the data, thereby improving the processing efficiency of subsequent spatial data.

[0105] Based on the first embodiment of the present application, in the third embodiment of the present application, the same or similar contents as those in the first embodiment can be referred to the above introduction and will not be described in detail later. Figure 6 , step S10 also includes steps S11~S12:

[0106] Step S11 : determining a feature extraction method for the pre-processed three-dimensional model data, and acquiring the key feature information based on the feature extraction method.

[0107] In this embodiment, feature extraction methods include deep learning model feature extraction and geometric feature extraction. Therefore, if the feature extraction method is deep learning model feature extraction, key feature information of the pre-processed 3D model data is obtained based on geometric feature extraction methods. Geometric feature extraction methods include SIFT (Scale Invariant Feature Transform) and SURF (Speeded Up Robust Features), combined with morphological analysis to identify key feature information in the 3D model data.

[0108] Optionally, if the feature extraction method is a deep learning model extraction method, key feature information of the pre-processed 3D model data is obtained based on the deep learning model. It is understandable that after the deep learning model is trained, it can automatically identify the key feature information of the 3D model data.

[0109] For example, please refer to Figure 7In the process of feature extraction based on deep learning network, the three-dimensional model data is extracted and analyzed through deep learning models such as convolutional neural network (CNN), so as to extract key features such as points, edges and faces, and obtain the key feature set, namely key feature information.

[0110] Step S12: performing importance evaluation processing on the key feature information based on a graph theory algorithm to obtain the importance evaluation information.

[0111] In this embodiment, when evaluating feature importance, the centrality or influence of the feature in the network is calculated based on a graph theory algorithm to determine its importance, thereby effectively knowing which feature information needs to be reduced in dimension without the user having to manually select parameters.

[0112] Based on the first embodiment of the present application, in the fourth embodiment of the present application, the same or similar contents as those in the first embodiment can be referred to the above introduction and will not be repeated hereafter. On this basis, before step S10, the non-perceptual lightweight three-dimensional data processing method further includes steps S01 to S02:

[0113] Step S01 : receiving original three-dimensional model data, and performing noise removal processing on the original three-dimensional data based on Gaussian filtering, median filtering, bilateral filtering or wavelet transform to obtain denoised data.

[0114] Step S02: reconstruct and simplify the data based on the denoised data to obtain the pre-processed three-dimensional model data.

[0115] In this embodiment, 3D model data can be received from a scanning device, modeling software, or a database. Subsequently, during noise removal, algorithms such as Gaussian filtering or median filtering can be used to remove noise points from the data, thereby improving data quality. Alternatively, bilateral filtering or wavelet transform techniques can be used to remove noise, achieving better results when processing noise of varying types and complexities.

[0116] After removing the noise, the repeated or highly similar parts in the three-dimensional data are analyzed and redundancy is reduced through data reconstruction, simplification and other methods.

[0117] Optionally, after step S01, the denoised data may be simplified based on a voxel grid simplification method, such as octree partitioning, by recursively dividing the space to reduce redundant data while maintaining the geometric and topological structure of the data, thereby obtaining preprocessed three-dimensional model data.

[0118] For example, please refer to Figure 8After obtaining the original three-dimensional data, the noise is removed by Gaussian filtering and median filtering, and then the redundant data is reduced by data reconstruction and simplification, and finally the preprocessed three-dimensional model data is obtained.

[0119] This embodiment removes noise and redundant data generated during the scanning process of the three-dimensional model file through the preprocessing stage, thereby improving the quality of the three-dimensional model data.

[0120] To help understand the results of combining the above-mentioned embodiments, taking a 3D car model as an example, this application first removes noise and redundant data generated during the scanning process of the 3D model file through a preprocessing stage. Subsequently, in the feature extraction and analysis stage, key features such as the car's body lines, lights, and wheels are automatically identified. Then, in the lightweight processing stage, data dimensionality reduction and model simplification techniques are used to remove less important key features and reduce the number of vertices and faces in the model, while maintaining the basic shape and key features of the car for subsequent analysis and processing. Finally, in the efficient compression algorithm stage, the lightweight model is encoded and compressed to obtain a compact data file. When the user needs to view or edit the car model, a high-quality 3D model can be quickly restored through a non-perceptual decompression process.

[0121] It should be noted that before and after the three-dimensional model data of the car is processed, the model does not change at the macro level for the user, that is, the content of the model seen by the user before and after processing is basically the same.

[0122] The present application provides a three-dimensional data processing device, which includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to execute the imperceptible lightweight three-dimensional data processing method in the above-mentioned first embodiment.

[0123] Reference below Figure 9 , which shows a schematic diagram of the structure of a three-dimensional data processing device suitable for implementing embodiments of the present application. The three-dimensional data processing device in the embodiments of the present application may include, but is not limited to, mobile terminals such as mobile phones, laptop computers, digital broadcast receivers, personal digital assistants (PDAs), tablet computers (PADs), portable multimedia players (PMPs), and in-vehicle terminals (e.g., in-vehicle navigation terminals), as well as fixed terminals such as digital TVs and desktop computers. Figure 9The three-dimensional data processing device shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present application.

[0124] like Figure 9 As shown, the three-dimensional data processing device may include a processing device 1001 (e.g., a central processing unit, a graphics processing unit, etc.), which can perform various appropriate actions and processes based on programs stored in a read-only memory (ROM) 1002 or programs loaded from a storage device 1003 into a random access memory (RAM) 1004. RAM 1004 also stores various programs and data required for the operation of the three-dimensional data processing device. Processing device 1001, ROM 1002, and RAM 1004 are interconnected via a bus 1005. An input / output (I / O) interface 1006 is also connected to the bus. Typically, the following systems can be connected to the input / output interface 1006: input devices 1007 including, for example, a touch screen, touchpad, keyboard, mouse, image sensor, microphone, accelerometer, gyroscope, etc.; output devices 1008 including, for example, a liquid crystal display (LCD), speaker, vibrator, etc.; storage devices 1003 including, for example, magnetic tape, hard disk, etc.; and communication devices 1009. Communication devices 1009 can allow the three-dimensional data processing device to communicate with other devices wirelessly or wired to exchange data. Although the figure shows a three-dimensional data processing device with various systems, it should be understood that implementation or presence of all the illustrated systems is not required. More or fewer systems may alternatively be implemented or present.

[0125] In particular, according to the embodiments disclosed in the present application, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, the embodiments disclosed in the present application include a computer program product comprising a computer program carried on a computer-readable medium, the computer program comprising program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network via a communication device, or installed from a storage device 1003, or installed from a read-only memory 1002. When the computer program is executed by the processing device 1001, the above-mentioned functions defined in the method of the embodiment disclosed in the present application are performed.

[0126] The three-dimensional data processing device provided in this application utilizes the imperceptible, lightweight three-dimensional data processing method described in the aforementioned embodiment, addressing the current technical issue of low efficiency in three-dimensional model data processing. Compared to the prior art, the beneficial effects of the three-dimensional data processing device provided in this application are identical to those of the imperceptible, lightweight three-dimensional data processing method described in the aforementioned embodiment. Other technical features of this three-dimensional data processing device are identical to those disclosed in the aforementioned embodiment and are not further elaborated here.

[0127] It should be understood that the various parts disclosed in this application can be implemented using hardware, software, firmware, or a combination thereof. In the description of the above embodiments, specific features, structures, materials, or characteristics can be combined in any one or more embodiments or examples in a suitable manner.

[0128] The above description is merely a specific embodiment 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 this application. Therefore, the scope of protection of this application should be based on the scope of protection of the claims.

[0129] The present application provides a computer-readable storage medium having computer-readable program instructions (i.e., a computer program) stored thereon, and the computer-readable program instructions are used to execute the imperceptible lightweight three-dimensional data processing method in the above-mentioned embodiment.

[0130] The computer-readable storage medium provided herein may be, for example, a USB flash drive, but is not limited to electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, systems, or devices, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to, an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In this embodiment, the computer-readable storage medium may be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, system, or device. The program code contained on the computer-readable storage medium may be transmitted using any suitable medium, including, but not limited to, wires, optical cables, radio frequency (RF), etc., or any suitable combination thereof.

[0131] The computer-readable storage medium may be included in the three-dimensional data processing device; or may exist independently without being assembled into the three-dimensional data processing device.

[0132] The computer-readable storage medium carries one or more programs. When the one or more programs are executed by a three-dimensional data processing device, the three-dimensional data processing device:

[0133] Acquire key feature information of the preprocessed three-dimensional model data, and perform importance evaluation processing on the key feature information based on a graph theory algorithm to obtain an importance score of the key feature information;

[0134] After removing the key feature information whose importance score is less than a preset score from the key feature information, performing data simplification processing on the key feature information based on a preset simplification algorithm;

[0135] A compression strategy is determined according to the data type of the three-dimensional model data, and after the three-dimensional model data is encoded, the encoded data is compressed based on the compression strategy to obtain compressed data.

[0136] Computer program code for performing the operations of the present application may be written in one or more programming languages, or a combination thereof, including object-oriented programming languages such as Java, Smalltalk, C++, and conventional procedural programming languages such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a stand-alone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer via any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0137] The flowcharts and block diagrams in the accompanying drawings illustrate the possible implementation architecture, functions and operations of the systems, methods and computer program products according to various embodiments of the present application. In this regard, each box in the flowchart or block diagram can represent a module, program segment or part of code, and the module, program segment or part of code contains one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in an order different from that marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flowchart, as well as the combination of boxes in the block diagram and / or flowchart, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or can be implemented using a combination of dedicated hardware and computer instructions.

[0138] The modules described in the embodiments of the present application may be implemented in software or hardware, wherein the name of a module does not necessarily limit the unit itself.

[0139] The computer-readable storage medium provided in this application stores computer-readable program instructions (i.e., a computer program) for executing the aforementioned imperceptible, lightweight 3D data processing method. This computer-readable storage medium can address the current technical issue of low 3D model data processing efficiency. Compared to the prior art, the beneficial effects of the computer-readable storage medium provided in this application are similar to those of the imperceptible, lightweight 3D data processing method provided in the aforementioned embodiments, and are not further elaborated here.

[0140] The above description is only part of the embodiments of the present application and does not limit the patent scope of the present application. All equivalent structural transformations made by using the contents of the present application specification and drawings under the technical concept of the present application, or direct / indirect application in other related technical fields are included in the patent protection scope of the present application.

Claims

1. A non-perceptual lightweight three-dimensional data processing method, characterized in that: The non-perceptual lightweight three-dimensional data processing method includes: Obtaining key feature information of the preprocessed three-dimensional model data, and performing importance evaluation processing on the key feature information based on a graph theory algorithm to obtain an importance score of the key feature information, including evaluating the importance of each node of the three-dimensional model data based on a betweenness centrality graph theory algorithm, evaluating the importance of each edge based on an edge betweenness centrality algorithm, and evaluating the importance score of the surface feature based on the importance scores of the nodes and edges contained in the surface feature; Determining, in the key feature information, feature information to be reduced in dimension whose importance score feature value is less than a preset score; Performing dimensionality reduction processing on the feature information to be reduced based on a preset dimensionality reduction algorithm to remove the key feature information whose importance score is less than a preset score in the key feature information, wherein the preset dimensionality reduction algorithm is a principal component analysis algorithm or a multidimensional scaling analysis algorithm; Simplifying the key feature information after dimensionality reduction based on a preset simplification algorithm; A compression strategy is determined according to the data type of the three-dimensional model data, and after the three-dimensional model data is encoded, the encoded data is compressed based on the compression strategy to obtain compressed data.

2. The imperceptible lightweight three-dimensional data processing method according to claim 1, characterized in that: After the step of determining a compression strategy according to the data type of the three-dimensional model data, and compressing the encoded data after encoding the three-dimensional model data based on the compression strategy to obtain compressed data, the method further includes: Respond to the decompression information sent by the client; Read and decompress the data to be decompressed based on a preset decompression algorithm to obtain decompressed target three-dimensional model data; The target three-dimensional model data is output to the client.

3. The imperceptible lightweight three-dimensional data processing method according to claim 2, characterized in that: The step of reading and decompressing the data to be decompressed based on a preset decompression algorithm to obtain the decompressed target three-dimensional model data includes: Reading the characteristic information, metadata and the data to be decompressed during the compression processing of the three-dimensional model data based on the preset decompression algorithm; The to-be-decompressed data is decompressed, and the decompressed data is restored based on the feature information and the metadata to obtain the target three-dimensional model data.

4. The imperceptible lightweight three-dimensional data processing method according to claim 1, characterized in that: The step of determining a compression strategy according to the data type of the three-dimensional model data, and compressing the encoded data after encoding the three-dimensional model data based on the compression strategy to obtain compressed data includes: Acquire a data type of the three-dimensional model data, and determine the compression strategy associated with the data type; encoding the three-dimensional model data based on a preset encoding algorithm to obtain the encoded data; The coded data is compressed according to a compression algorithm corresponding to the compression strategy to obtain the compressed data.

5. The imperceptible lightweight three-dimensional data processing method according to claim 1, characterized in that: The steps of obtaining key feature information of the preprocessed three-dimensional model data and performing importance evaluation processing on the key feature information based on a graph theory algorithm to obtain an importance score of the key feature information include: Acquire the key feature information of the preprocessed three-dimensional model data based on a geometric feature extraction method; or acquire the key feature information of the preprocessed three-dimensional model data based on a deep learning model; The importance of the key feature information is evaluated based on a graph theory algorithm to obtain the importance score.

6. The imperceptible lightweight three-dimensional data processing method according to claim 1, characterized in that: Before the step of obtaining key feature information of the preprocessed three-dimensional model data and performing importance evaluation processing on the key feature information based on a graph theory algorithm to obtain an importance score of the key feature information, the method further includes: Receiving original three-dimensional model data, and performing noise removal processing on the original three-dimensional model data based on Gaussian filtering, median filtering, bilateral filtering or wavelet transform to obtain denoised data; Data reconstruction and simplification processing are performed based on the denoised data to obtain the pre-processed three-dimensional model data.

7. The imperceptible lightweight three-dimensional data processing method according to claim 6, characterized in that: After the step of receiving the original three-dimensional model data and performing noise removal processing on the original three-dimensional model data based on Gaussian filtering, median filtering, bilateral filtering or wavelet transform to obtain denoised data, the method further includes: The denoised data is simplified based on a voxel grid simplification method to obtain the pre-processed three-dimensional model data.

8. A three-dimensional data processing device, characterized in that: The three-dimensional data processing device includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the computer program is configured to implement the steps of the imperceptible lightweight three-dimensional data processing method according to any one of claims 1 to 7.

9. A storage medium, characterized in that: The storage medium is a computer-readable storage medium, and a computer program is stored on the storage medium. When the computer program is executed by a processor, the steps of the imperceptible lightweight three-dimensional data processing method according to any one of claims 1 to 7 are implemented.

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