Vehicle body model optimization method and device, electronic equipment and storage medium

Abstract

The invention relates to a vehicle body model optimization method and device, electronic equipment and a storage medium, and the method comprises the steps: obtaining an original vehicle body model, and recognizing a topological structure in the original vehicle body model; based on the topological structure, extracting a network redundancy structure meeting a preset defect condition from the original vehicle body model; and repairing the network redundant structure to obtain a complete vehicle body model meeting a preset qualified condition. Therefore, the technical problems that the complexity of a vehicle body model is relatively high, the labor cost is relatively high when grid redundancy processing is carried out, and the vehicle body model depends on artificial experience and is not beneficial to popularization and application in related technologies are solved.

Description

Technical Field

[0001] This application relates to the field of electronic digital data processing technology, and in particular to a method, apparatus, electronic device and storage medium for optimizing a vehicle body model. Background Technology

[0002] In the field of automotive engineering, before conducting aerodynamic simulation analysis, the vehicle body model needs to undergo geometric cleanup and its topology repair to meet the quality requirements of the simulation analysis. During the scanning process of the vehicle body model, redundant mesh structures (such as duplicate faces, overly dense vertices, and useless edges) are often generated. These not only affect the model's storage and rendering efficiency but also hinder subsequent core functions such as manifold construction, parameterization, and orientation field optimization.

[0003] In related technologies, methods such as manual deletion are usually used to deal with mesh redundancy, with a small number being data-driven. Although manual repair can remove all redundant meshes to ensure model quality and meet the requirements of subsequent simulation experiments, the complexity of the vehicle body model means that this process requires a lot of manpower and resources, resulting in a waste of resources, and urgently needs improvement. Summary of the Invention

[0004] This application provides a method, apparatus, electronic device, and storage medium for optimizing a vehicle body model, in order to solve the technical problems in related technologies, such as the high complexity of vehicle body models, high labor costs and reliance on human experience when performing mesh redundancy processing, which are not conducive to widespread application.

[0005] The first aspect of this application provides a method for optimizing a vehicle body model, comprising the following steps: obtaining an original vehicle body model and identifying the topology in the original vehicle body model; extracting a network redundancy structure that meets preset defect conditions from the original vehicle body model based on the topology; and repairing the network redundancy structure to obtain a complete vehicle body model that meets preset qualification conditions.

[0006] Optionally, in one embodiment of this application, after extracting the network redundancy structure that meets the preset defect conditions from the original vehicle body model, the method further includes: marking the network redundancy structure using a preset marking method to obtain the marked vehicle body model, and displaying the marked vehicle body model.

[0007] Optionally, in one embodiment of this application, after displaying the marked vehicle body model, the method further includes: determining whether any defect marking instruction is received; if any defect marking instruction is received, then in response to the any defect marking instruction, extracting or deleting the corresponding redundant marking network structure from the original vehicle body model, and optimizing the preset defect condition using the any defect marking instruction; otherwise, repairing the redundant network structure.

[0008] Optionally, in one embodiment of this application, after displaying the marked vehicle body model, the method further includes: determining whether any parameter adjustment instruction is received; if any parameter adjustment instruction is received, then in response to the parameter adjustment instruction, adjusting at least one of the mesh simplification degree, vertex merging rule, and feature edge preservation rule of the marked vehicle body model to determine the corresponding vehicle body model processing strategy, and using the vehicle body model processing strategy to repair the network redundancy structure.

[0009] Optionally, in one embodiment of this application, identifying the topological structure in the original vehicle body model includes: representing the data structure of the original vehicle body model based on the boundary of a polygonal mesh to obtain the geometric relationships and topological information between vertices, edges, and faces in the original vehicle body model; and obtaining the topological structure based on the geometric relationships and the topological information.

[0010] A second aspect of this application provides an optimization device for a vehicle body model, comprising: an acquisition module for acquiring an original vehicle body model and identifying the topology in the original vehicle body model; an extraction module for extracting network redundancy structures that meet preset defect conditions from the original vehicle body model based on the topology; and a repair module for repairing the network redundancy structures to obtain a complete vehicle body model that meets preset qualification conditions.

[0011] Optionally, in one embodiment of this application, it further includes: a marking module, used to mark the network redundancy structure using a preset marking method to obtain a marked vehicle body model and display the marked vehicle body model.

[0012] Optionally, in one embodiment of this application, the marking module further includes: a first judgment unit, configured to determine whether any defect marking instruction is received; and a first control unit, configured to, in response to the received defect marking instruction, extract or delete the corresponding redundant marking network structure from the original vehicle body model, and optimize the preset defect conditions using the defect marking instruction; otherwise, repair the redundant network structure.

[0013] Optionally, in one embodiment of this application, the marking module further includes: a second judgment unit, configured to determine whether any parameter adjustment instruction is received; and a second control unit, configured to, in response to the received parameter adjustment instruction, adjust at least one of the mesh simplification degree, vertex merging rule, and feature edge preservation rule of the marked vehicle body model to determine the corresponding vehicle body model processing strategy, and use the vehicle body model processing strategy to repair the network redundancy structure.

[0014] Optionally, in one embodiment of this application, the acquisition module includes: a first acquisition unit, used to obtain the geometric relationships and topological information between vertices, edges and faces in the original body model based on the boundary representation of the polygonal mesh; and a second acquisition unit, used to obtain the topological structure based on the geometric relationships and the topological information.

[0015] A third aspect of this application provides an electronic device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the vehicle body model optimization method as described in the above embodiments.

[0016] A fourth aspect of this application provides a computer-readable storage medium storing computer instructions for causing the computer to perform the vehicle body model optimization method as described in the above embodiments.

[0017] A fifth aspect of this application provides a computer program product, including a computer program that, when executed, implements the above-described method for optimizing a vehicle body model.

[0018] This application addresses the quality issue of mesh redundancy in vehicle bodies after scanning by automatically extracting redundant meshes through vehicle body topology identification and repairing the extracted redundant meshes to obtain a complete vehicle body surface. This achieves highly efficient improvement in model structural integrity through fully automated detection and repair. Therefore, it solves the technical problems in related technologies where the complexity of vehicle body models is high, resulting in high manual costs and reliance on human experience during mesh redundancy processing, hindering widespread application.

[0019] Additional aspects and advantages of this application will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of this application. Attached Figure Description

[0020] The above and / or additional aspects and advantages of this application will become apparent and readily understood from the following description of the embodiments taken in conjunction with the accompanying drawings, wherein: Figure 1 This is a flowchart of a method for optimizing a vehicle body model according to an embodiment of this application; Figure 2 This is a schematic diagram illustrating the principle of an optimization method for a vehicle body model according to an embodiment of this application; Figure 3 This is a schematic diagram illustrating an example of redundant meshing and a comparison of the results after remeshing, according to an embodiment of this application. Figure 4This is a schematic diagram of the structure of an optimization device for a car body model according to an embodiment of this application; Figure 5 This is a schematic diagram of the structure of an electronic device provided according to an embodiment of this application. Detailed Implementation

[0021] The embodiments of this application are described in detail below. Examples of these embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain this application, and should not be construed as limiting this application.

[0022] The following description, with reference to the accompanying drawings, outlines an optimization method, apparatus, electronic device, and storage medium for a vehicle body model according to embodiments of this application. Addressing the technical problems mentioned in the background art, such as the high complexity of vehicle body models, high manual costs and reliance on human experience in mesh redundancy processing, which hinders widespread application, this application provides a vehicle body model optimization method. This method addresses the quality issues of mesh redundancy in the vehicle body after scanning by automatically extracting redundant meshes through identification of the vehicle body topology, and repairing the extracted redundant meshes to obtain a complete vehicle body surface. This achieves fully automated detection and repair, and efficiently improves the integrity of the model structure. Therefore, it solves the technical problems in related technologies, such as the high complexity of vehicle body models, high manual costs in mesh redundancy processing, and reliance on human experience, which hinders widespread application.

[0023] Specifically, Figure 1 This is a flowchart illustrating a method for optimizing a vehicle body model provided in an embodiment of this application.

[0024] like Figure 1 As shown, the optimization method for this car body model includes the following steps: In step S101, the original vehicle body model is obtained, and the topology in the original vehicle body model is identified.

[0025] In actual implementation, the embodiments of this application can obtain a three-dimensional model file containing vehicle body geometry information, that is, obtain the original vehicle body model, and preliminarily identify the vehicle body structural features.

[0026] The original vehicle body model can come from 3D scanning (such as .stl, .obj, point cloud data) or CAD design (such as .stp, .igs). These formats store the model's vertex, edge, and face data, but may contain a large amount of redundancy, errors, and non-standard topology.

[0027] In order to remove redundant, erroneous, and non-standard topologies, embodiments of this application can analyze the relationships between the data of vertices, edges, and faces in the original vehicle body model to determine the topology and distinguish between redundant, erroneous, and non-standard topologies.

[0028] Optionally, in one embodiment of this application, identifying the topological structure in the original vehicle body model includes: representing the data structure of the original vehicle body model based on the boundary of a polygonal mesh to obtain the geometric relationships and topological information between vertices, edges, and faces in the original vehicle body model; and obtaining the topological structure based on the geometric relationships and topological information.

[0029] In this embodiment, the original vehicle body model file format can be converted into a data structure that can be understood and manipulated (usually a mesh representation based on half-side or wing-side structures) to construct topological relationships.

[0030] In this context, vertices are points in 3D space; edges are line segments connecting two vertices; and faces are regions enclosed by multiple edges (usually triangles or quadrilaterals). Boundary representation is a method of defining a 3D solid by describing the boundaries (i.e., surfaces) of an object. Polygonal meshes are a method of approximating the surface of an object using a set of polygons (most commonly triangles). Topological relations refer to the connections and adjacencies between vertices, edges, and faces, regardless of their geometric positions.

[0031] This application embodiment can establish and record complex connection relationships in the original vehicle body model, such as which edges each vertex connects to, which face each edge belongs to, and which edges each face contains, through the boundary representation of polygonal meshes. This accurately describes the geometric and topological information of the model. Through efficient data parsing algorithms, large-scale model files are quickly read, and the topological structure in the model is automatically identified, enabling the correlation analysis of faces, edges, and vertices.

[0032] In step S102, based on the topology, a network redundancy structure that meets the preset defect conditions is extracted from the original vehicle body model.

[0033] As one possible implementation method, embodiments of this application can automatically scan the model surface, extract all existing mesh redundancy topological defects, analyze the model boundary, face loop and vertex connection relationships, and determine which regions belong to mesh redundancy structures (such as repeated faces, overly dense vertices, useless edges, etc.).

[0034] Optionally, in one embodiment of this application, after extracting the network redundancy structure that meets the preset defect conditions from the original vehicle body model, the method further includes: marking the network redundancy structure using a preset marking method to obtain the marked vehicle body model, and displaying the marked vehicle body model.

[0035] Furthermore, embodiments of this application can display a vehicle body model. During the display, redundant network structures are highlighted to facilitate inspection by technicians to determine whether there are any misidentifications or omissions.

[0036] Optionally, in one embodiment of this application, after displaying the marked vehicle body model, the method further includes: determining whether any defect marking instruction has been received; if any defect marking instruction has been received, then in response to any defect marking instruction, extracting or deleting the corresponding redundant marking network structure from the original vehicle body model, and optimizing the preset defect conditions using any defect marking instruction; otherwise, repairing the redundant network structure.

[0037] Based on the displayed content, technicians can intervene manually if they determine that there are errors or omissions in the identification. If a misjudgment is found (such as mislabeling important details as redundant or omitting certain defects), they can manually select specific areas and perform "forced retention" or "forced deletion" operations to ensure the flexibility and reliability of automatic detection, combining automation efficiency with the final decision-making power of expert experience.

[0038] Based on this, the embodiments of this application can also perform continuous self-learning. After each manual intervention, it learns the characteristics of the network redundancy structure marked or deleted under manual intervention to optimize defect conditions, thereby continuously improving the accuracy of automatic identification.

[0039] Optionally, in one embodiment of this application, after displaying the marked vehicle body model, the method further includes: determining whether any parameter adjustment instruction is received; if any parameter adjustment instruction is received, then in response to any parameter adjustment instruction, adjusting at least one of the mesh simplification degree, vertex merging rule, and feature edge preservation rule of the marked vehicle body model to determine the corresponding vehicle body model processing strategy, and using the vehicle body model processing strategy to repair the network redundant structure.

[0040] It is understandable that the original scanned model with ultra-high precision may have tens of millions or even hundreds of millions of faces, making direct simulation computation extremely computational. The embodiments of this application allow for parameter adjustments based on user needs, such as adjusting the number of faces after remeshing and the selection of repair areas.

[0041] For example, to pursue computational speed, users can reduce the number of target patches, sacrificing some non-critical details for faster simulation speed; to pursue simulation accuracy, users can increase the number of target patches, retaining more geometric features to ensure accurate and reliable simulation results.

[0042] Users can set global processing parameters, such as the number of target patches to control the degree of mesh simplification; merging tolerance to define the distance at which vertices will be automatically merged; and feature angle threshold to define the angle at which edges are considered to be retained as feature edges.

[0043] Furthermore, based on user adjustments, this application embodiment can determine the actual redundant mesh structures that need to be deleted, and then determine the deletion method, deletion sequence, etc.

[0044] In step S103, the network redundancy structure is repaired to obtain a complete vehicle body model that meets the preset qualification conditions.

[0045] After completing parameter settings and region selection, this embodiment can automatically perform remeshing to repair the detected and selected redundant mesh regions with high quality. For example, when repairing redundant network structures, this embodiment can decompose the actual processing strategy and perform the following actions: remove all duplicate faces and isolated elements marked as redundant; merge vertices that are too close together into one; fill the holes caused by deletion operations, or stitch together broken mesh pieces; retriangulate the defective region or the entire model to generate a high-quality, uniformly sized mesh, etc.

[0046] After modification, the embodiments of this application can output a complete 3D model file after automatic detection and mesh optimization.

[0047] Combination Figure 2 and Figure 3 As shown, the working principle of the vehicle body model optimization method of this application embodiment is explained in detail with an example.

[0048] like Figure 2 As shown, the embodiments of this application can be implemented based on a data parsing and reading module, an automatic detection module, a parameter setting and manual intervention module, and an automatic repair module.

[0049] Specifically, it may include the following steps: Step S1: Input: Input the 3D model file of the vehicle body to initially identify the structural features of the vehicle body.

[0050] Step S2: Utilizing the data parsing and reading module, large-scale model files are read, and the topological structure within the model is automatically identified. This enables the correlation analysis of faces, edges, and vertices. Using a boundary representation (B-Rep) data structure based on a polygon mesh, vertices, edges, and faces, along with their topological relationships, are effectively organized, accurately describing the geometric and topological information of the model. Through efficient data parsing algorithms, large-scale model files are quickly read, and the topological structure within the model is automatically identified, enabling the correlation analysis of faces, edges, and vertices.

[0051] Step S3: Using the automatic detection module, automatically scan the model surface, extract all existing mesh redundancy topological defects, analyze the model boundary, face loop and vertex connection relationship, determine which areas belong to the mesh redundancy structure (such as repeated faces, overly dense vertices, useless edges, etc.), and mark them in a highlighted manner in the 3D preview window.

[0052] Step S4: Using the parameter setting and manual intervention module, combined with the automatic detection results, users can adjust parameters and intervene manually according to actual needs. Parameters such as the number of remeshable patches and the selection of repair areas can be set. For complex or special defects, users can manually select specific redundant meshes in the preview interface to specify key repair areas.

[0053] Step S5: Using the automatic repair module, after completing parameter settings and region selection, automatic remeshing is performed to perform high-quality repair on the detected and selected redundant mesh regions.

[0054] Step S6 outputs a complete 3D model file after automatic detection and mesh optimization. Examples of redundant meshes and the results after remeshing are shown below. Figure 3 As shown.

[0055] The following is the algorithm flow for handling redundant meshes: / / Input: original mesh with vertices V, edges E, and faces F / / Output: simplified mesh with reduced faces while preserving shapefeatures 1: / / Step 1: Initialization 2: for each vertex v in V: 3:Compute quadric error matrix Q[v]; / / Precompute per-vertex error 4: / / Step 2: Edge queue construction 5: for each edge e = (v1, v2) in E: 6:Compute optimal collapse position v_new; 7:Compute collapse cost using Q[v1] + Q[v2]; 8:Insert edge e into priority queue Q, sorted by cost; 9: / / Step 3: Iterative edge collapse 10: while Q is not empty and termination condition not met: 11:e_min = Q.pop(); / / Edge with minimal cost 12:if CollapseIsValid(e_min): / / Check for manifold preservation 13:CollapseEdge(e_min); / / Merge vertices, remove affected faces andedges 14:Update Q[v_new]; / / Recalculate Q for merged vertex 15:Recompute costs for neighboring edges; 16:Update affected edges in queue; 17: / / Step 4: Termination 18: if number of faces<target or cost>threshold: 19:break; 20: / / Step 5: Output 21: Return simplified mesh with reduced face count and preserved keygeometry。

[0056] In summary, the embodiments of this application differ from the single fully automated process commonly used in related technologies. By combining efficient automatic detection algorithms with controllable parametric manual intervention, it accurately identifies and locates specific problem areas requiring repair. Simultaneously, it achieves high-quality repair through an optimized remeshing algorithm to remove redundant meshes, effectively improving the integrity of the model and the subsequent processing results. Furthermore, the embodiments of this application support multiple mainstream modeling formats such as OBJ and PLY, facilitating wide application in practical engineering and scientific research scenarios.

[0057] Based on actual engineering needs, this application provides a clear mathematical description of problems such as redundant and repeated surfaces in the mesh structure, and designs corresponding automated detection and repair algorithms accordingly. All processes are implemented through software, ensuring the engineering practicality and reproducibility of the method.

[0058] In the algorithm design process, the embodiments of this application can ensure that all types of redundant meshes can be accurately detected and repaired. A multi-level detection strategy is adopted to reduce the false negative rate. The processing order of different algorithm modules is optimized through experiments to avoid the execution of a certain algorithm from affecting the accuracy and stability of subsequent steps, thereby ensuring the integrity and efficiency of the overall repair process.

[0059] The vehicle body model optimization method proposed in this application addresses the quality issue of mesh redundancy in the vehicle body after scanning. By identifying the vehicle body topology, redundant meshes are automatically extracted, and the extracted redundant meshes are used to repair the vehicle body model structure, resulting in a complete vehicle body surface. This achieves highly efficient improvement in the integrity of the model structure through fully automated detection and repair. Therefore, it solves the technical problems in related technologies where the complexity of the vehicle body model is high, resulting in high manual costs and reliance on human experience during mesh redundancy processing, which hinders widespread application.

[0060] Next, the optimization device for the vehicle body model proposed according to the embodiments of this application is described with reference to the accompanying drawings.

[0061] Figure 4 This is a block diagram of an optimization device for a vehicle body model according to an embodiment of this application.

[0062] like Figure 4 As shown, the optimization device 10 for the vehicle body model includes: an acquisition module 100, an extraction module 200, and a repair module 300.

[0063] Specifically, the acquisition module 100 is used to acquire the original vehicle body model and identify the topological structure in the original vehicle body model.

[0064] Extraction module 200 is used to extract network redundancy structures that meet preset defect conditions from the original vehicle body model based on topology.

[0065] Repair module 300 is used to repair redundant network structures and obtain a complete vehicle body model that meets preset qualification conditions.

[0066] Optionally, in one embodiment of this application, the vehicle body model optimization device 10 further includes a marking module.

[0067] The marking module is used to mark redundant network structures using a preset marking method to obtain the marked vehicle body model and display the marked vehicle body model.

[0068] Optionally, in one embodiment of this application, the marking module further includes a first judgment unit and a first control unit.

[0069] The first judgment unit is used to determine whether any defect marking instruction has been received.

[0070] The first control unit is used to extract or delete the corresponding redundant marker network structure from the original vehicle body model in response to any defect marking instruction received, and to optimize the preset defect conditions using any defect marking instruction; otherwise, it repairs the redundant network structure.

[0071] Optionally, in one embodiment of this application, the marking module further includes: a second judgment unit and a second control unit.

[0072] The second judgment unit is used to determine whether any parameter adjustment command has been received.

[0073] The second control unit is configured to, upon receiving any parameter adjustment instruction, adjust at least one of the following in response to the parameter adjustment instruction: the degree of mesh simplification, vertex merging rules, and feature edge preservation rules of the marked body model, in order to determine the corresponding body model processing strategy and use the body model processing strategy to repair the network redundancy structure.

[0074] Optionally, in one embodiment of this application, the acquisition module 100 includes: a first acquisition unit and a second acquisition unit.

[0075] The first acquisition unit is used to obtain the data structure of the original vehicle body model based on the boundary representation of the polygon mesh, so as to obtain the geometric relationships and topological information between vertices, edges and faces in the original vehicle body model.

[0076] The second acquisition unit is used to obtain the topological structure based on geometric relationships and topological information.

[0077] It should be noted that the explanation of the aforementioned method for optimizing the vehicle body model also applies to the optimization device for the vehicle body model in this embodiment, and will not be repeated here.

[0078] The vehicle body model optimization device proposed in this application can address the quality problem of mesh redundancy in the vehicle body after scanning. By identifying the vehicle body topology, it automatically extracts redundant meshes and repairs the extracted redundant meshes to obtain a complete vehicle body surface. This achieves highly efficient improvement in the integrity of the model structure through fully automated detection and repair. Therefore, it solves the technical problems in related technologies where the complexity of the vehicle body model is high, resulting in high manual costs and reliance on human experience during mesh redundancy processing, which hinders widespread application.

[0079] Figure 5 A schematic diagram of the structure of an electronic device provided in an embodiment of this application. The electronic device may include: The memory 501, the processor 502, and the computer program stored on the memory 501 and capable of running on the processor 502.

[0080] When the processor 502 executes the program, it implements the optimization method for the vehicle body model provided in the above embodiments.

[0081] Furthermore, electronic devices also include: Communication interface 503 is used for communication between memory 501 and processor 502.

[0082] The memory 501 is used to store computer programs that can run on the processor 502.

[0083] Memory 501 may include high-speed RAM memory, and may also include non-volatile memory, such as at least one disk storage device.

[0084] If the memory 501, processor 502, and communication interface 503 are implemented independently, then the communication interface 503, memory 501, and processor 502 can be interconnected via a bus to complete communication between them. The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. Buses can be categorized as address buses, data buses, control buses, etc. For ease of representation, Figure 5 The bus is represented by a single thick line, but this does not mean that there is only one bus or one type of bus.

[0085] Optionally, in a specific implementation, if the memory 501, processor 502, and communication interface 503 are integrated on a single chip, then the memory 501, processor 502, and communication interface 503 can communicate with each other through an internal interface.

[0086] Processor 502 may be a central processing unit (CPU), an application specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of this application.

[0087] This embodiment also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described method for optimizing the vehicle body model.

[0088] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the vehicle body model optimization method provided in this embodiment of the invention.

[0089] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0090] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this application, "N" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0091] Any process or method described in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or N executable instructions for implementing custom logic functions or processes, and the scope of the preferred embodiments of this application includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the functions involved, as should be understood by those skilled in the art to which embodiments of this application pertain.

[0092] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of computer-readable media include: an electrical connection having one or more wires (electronic device), a portable computer disk drive (magnetic device), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Alternatively, the computer-readable medium may be paper or other suitable media on which the program can be printed, since the program can be obtained electronically by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in a computer memory.

[0093] It should be understood that the various parts of this application can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, the N steps or methods can be implemented using software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0094] Those skilled in the art will understand that all or part of the steps of the methods in the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, the program includes one or a combination of the steps of the method embodiments.

[0095] Furthermore, the functional units in the various embodiments of this application can be integrated into a processing module, or each unit can exist physically separately, or two or more units can be integrated into a module. The integrated module can be implemented in hardware or as a software functional module. If the integrated module is implemented as a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium.

[0096] The storage medium mentioned above can be a read-only memory, a disk, or an optical disk, etc. Although embodiments of this application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting this application. Those skilled in the art can make changes, modifications, substitutions, and variations to the above embodiments within the scope of this application.

Claims

1. A method for optimizing a vehicle body model, characterized in that, Includes the following steps: Obtain the original vehicle body model and identify the topological structure in the original vehicle body model; Based on the topology, a network redundancy structure that meets the preset defect conditions is extracted from the original vehicle body model; The network redundancy structure is repaired to obtain a complete vehicle body model that meets the preset qualification conditions.

2. The method according to claim 1, characterized in that, After extracting the network redundancy structure that meets the preset defect conditions from the original vehicle body model, the method further includes: The network redundancy structure is marked using a preset marking method to obtain the marked vehicle body model, and the marked vehicle body model is then displayed.

3. The method according to claim 2, characterized in that, After displaying the marked car body model, the following is also included: Determine whether any defect marking instruction has been received; If any defect marking instruction is received, in response to the defect marking instruction, the corresponding redundant marking network structure is extracted or deleted from the original vehicle body model, and the preset defect condition is optimized using the defect marking instruction; otherwise, the redundant network structure is repaired.

4. The method according to claim 2, characterized in that, After displaying the marked car body model, the following is also included: Determine whether any parameter adjustment command has been received; If any parameter adjustment instruction is received, in response to the parameter adjustment instruction, at least one of the following is adjusted: the mesh simplification degree, vertex merging rule, and feature edge preservation rule of the marked body model, in order to determine the corresponding body model processing strategy, and the network redundancy structure is repaired using the body model processing strategy.

5. The method according to claim 1, characterized in that, The identification of the topology in the original vehicle body model includes: The boundary representation of the original vehicle body model is based on a polygonal mesh to obtain the geometric relationships and topological information between vertices, edges and faces in the original vehicle body model; The topological structure is obtained based on the geometric relationships and the topological information.

6. An optimization device for a car body model, characterized in that, include: The acquisition module is used to acquire the original vehicle body model and identify the topological structure in the original vehicle body model; The extraction module is used to extract network redundancy structures that meet preset defect conditions from the original vehicle body model based on the topology. The repair module is used to repair the network redundancy structure to obtain a complete vehicle body model that meets the preset qualification conditions.

7. The apparatus according to claim 6, characterized in that, Also includes: The marking module is used to mark the network redundancy structure using a preset marking method to obtain the marked vehicle body model and display the marked vehicle body model.

8. An electronic device, characterized in that, include: A memory, a processor, and a computer program stored in the memory and executable on the processor, the processor executing the program to implement the method for optimizing a vehicle body model as described in any one of claims 1-5.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, The program is executed by the processor to implement the optimization method for the vehicle body model as described in any one of claims 1-5.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed, it is used to implement the optimization method for the vehicle body model as described in any one of claims 1-5.

Images