A three-dimensional mesh model simplification method, device, equipment and medium

Abstract

The application provides a three-dimensional mesh model simplification method, device, equipment and medium, and relates to the technical field of model simplification. The method comprises the following steps: adopting a region growing algorithm, determining a growing region according to a free selection region and geometric topology information of a three-dimensional mesh model; determining an error matrix of each triangular facet in the growing region according to the geometric topology information, and determining the sum of quadratic errors of all associated triangular facets according to the error matrix; determining a vertex folding cost and a new vertex according to the sum of quadratic errors, determining an edge folding cost and performing priority sorting according to the new vertex; and performing edge simplification folding according to the sorted edge folding cost, so as to obtain simplified vertices and corresponding simplified geometric topology information. The application can improve the simplification effect.

Description

Technical Field

[0001] This invention relates to the field of model simplification technology, and more specifically, to a method, apparatus, device, and medium for simplifying three-dimensional mesh models. Background Technology

[0002] A 3D mesh model is a digital model used to represent a 3D object. It consists of a series of vertices, edges, and faces, which together define the object's shape and appearance. 3D mesh models are widely used in various fields, including but not limited to precision components, virtual reality, game development, animation production, architectural visualization, engineering simulation, medical image processing, and cultural heritage preservation. In some fields, simplification of 3D mesh models is necessary to meet requirements such as computational efficiency, optimized storage space, enhanced interactivity, and addressing limitations of existing technologies.

[0003] In related technologies, simplification is generally achieved using algorithms such as Quadratic Error Metric (QEM), clustering algorithms, and wavelet transform. However, these methods suffer from several drawbacks: an excessive pursuit of overall compression ratio leads to severe loss of local details (such as texture and edge sharpness); the simplification process lacks awareness of the model's topology, which may disrupt model connectivity; and different models require repeated parameter adjustments to achieve the best results, which weakens the algorithm's versatility and practicality, resulting in poor simplification effects. Summary of the Invention

[0004] The problem addressed by this invention is how to improve the simplification effect of three-dimensional mesh models.

[0005] To address the above problems, this invention provides a method, apparatus, device, and medium for simplifying three-dimensional mesh models.

[0006] In a first aspect, the present invention provides a method for simplifying a three-dimensional mesh model, comprising:

[0007] A region growing algorithm is used to determine the growing region based on the freely selectable region and geometric topology information of the 3D mesh model;

[0008] Based on the geometric topology information, the error matrix of each triangular facet in the growth region is determined, and the sum of the quadratic errors of all associated triangular facets is determined based on the error matrix.

[0009] Based on the sum of the quadratic errors, determine the vertex folding cost and the new vertex, and based on the new vertex, determine the edge folding cost and prioritize them.

[0010] Based on the sorted edge folding cost, perform edge simplification folding to obtain simplified vertices and corresponding simplified geometric topology information.

[0011] Optionally, the step of performing simplified edge folding based on the sorted edge folding costs to obtain simplified vertices and corresponding simplified geometric topology information includes:

[0012] Extract the vertex data of the edge corresponding to the edge folding cost with the highest priority, and perform pre-simplified folding based on the vertex data to generate pre-simplified vertices;

[0013] When the pre-simplified vertex coincides with the boundary vertex of the freely selected region, the edge folding cost is re-determined and the priority is sorted.

[0014] When the pre-simplified vertex does not coincide with the boundary vertex of the freely selected region, the pre-simplified vertex is used as the simplified vertex, and the corresponding simplified geometric topology information is obtained.

[0015] Optionally, the region growing algorithm is used to determine the growing region based on the freely selectable region and geometric topology information of the 3D mesh model, including:

[0016] Based on the freely selected region, determine the simplified region boundary of the three-dimensional mesh model;

[0017] Based on the region growth criterion, the initial seed vertex is expanded according to the initial seed vertex and the simplified region boundary, and the final seed vertex and its corresponding neighboring vertex are determined iteratively.

[0018] Based on the final seed vertex, a quadratic error metric algorithm is used to determine the extended vertices of the adjacent vertices;

[0019] Obtain the geometric topology information of all the final seed vertices, the adjacent vertices, and the extended vertices within the simplified region boundary to determine the growth region.

[0020] Optionally, the region growth criterion includes geometric similarity conditions and topological connectivity conditions. The step of expanding the initial seed vertex based on the region growth criterion and the simplified region boundary, and iteratively determining the final seed vertex and its corresponding neighboring vertices, includes:

[0021] The growth threshold is determined based on the geometric similarity condition and the topological connectivity condition.

[0022] Determine the candidate neighboring vertices of the initial seed vertex, and determine the geometric similarity data and topological connectivity data of the candidate neighboring vertices;

[0023] Based on the growth threshold, the final seed vertex and its corresponding neighboring vertex are determined according to the geometric similarity data and the topological connectivity data, and the neighboring vertex is used as the candidate neighboring vertex to iteratively determine all the final seed vertices.

[0024] Optionally, the step of expanding the initial seed vertex based on the region growth criterion, according to the initial seed vertex and the simplified region boundary, and iteratively determining the final seed vertex and its corresponding neighboring vertices, further includes:

[0025] When the difference between the geometric similarity data and the topological connectivity data of different candidate adjacent vertices exceeds a preset range, the growth threshold is adjusted according to a dynamic adjustment strategy.

[0026] Optionally, before determining the growth region based on the freely selected region and geometric topology information of the 3D mesh model using the region growing algorithm, the method further includes:

[0027] Based on the requirement of continuity, the freely selectable area is obtained according to the human-computer interaction instructions.

[0028] Optionally, after performing edge simplification folding based on the sorted edge folding cost to obtain simplified vertices and corresponding simplified geometric topology information, the method further includes:

[0029] Based on the simplified vertices and the simplified geometric topology information, a simplified 3D mesh model is drawn.

[0030] In a second aspect, the present invention provides a three-dimensional mesh model simplification device, comprising:

[0031] The growth module is used to determine the growth region based on the freely selectable region and geometric topology information of the 3D mesh model using a region growth algorithm.

[0032] An error module is used to determine the error matrix of each triangular facet in the growth region based on the geometric topology information, and to determine the sum of the quadratic errors of all associated triangular facets based on the error matrix.

[0033] The cost module is used to determine the vertex folding cost and the new vertex based on the sum of the quadratic errors, and to determine the edge folding cost and prioritize it based on the new vertex.

[0034] The simplification module is used to perform edge simplification folding based on the sorted edge folding cost to obtain simplified vertices and corresponding simplified geometric topology information.

[0035] Thirdly, the present invention provides an electronic device, including a memory and a processor;

[0036] The memory is used to store computer programs;

[0037] The processor is configured to implement the three-dimensional mesh model simplification method as described in the first aspect when executing the computer program.

[0038] Fourthly, the present invention provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the three-dimensional mesh model simplification method as described in the first aspect.

[0039] The beneficial effects of the three-dimensional mesh model simplification method, apparatus, device, and medium of the present invention are:

[0040] A region growing algorithm is employed to determine the growing region based on the freely selectable region and geometric topology information of the 3D mesh model. This process defines the simplification range through human-computer interaction by allowing users to freely select regions, and dynamically expands the growing region using geometric topology information. This accurately identifies non-feature regions to be simplified, effectively preventing feature regions (such as assembly holes and edge sharpness of precision parts) from being included in the simplification range, thus reducing the risk of feature deformation or distortion from the outset. Furthermore, based on the geometric topology information, the error matrix of each triangular facet in the growing region is determined, and the sum of the quadratic errors of related triangular facets is calculated. Through the precise measurement of geometric errors using the error matrix, the impact of the simplification operation on local geometric features can be accurately quantified, ensuring that the sum of the quadratic errors truly reflects the potential impact of simplification. The resulting geometric deviations provide a reliable basis for error judgment in subsequent simplification operations. Based on the sum of quadratic errors, the vertex folding cost and new vertex are determined, and the edge folding cost and priority ranking are determined accordingly. This allows for the priority selection of edges with the least impact on the model's visual and geometric features (minimum folding cost), ensuring that non-critical geometric elements are processed first during the simplification process, reducing disturbance to important geometric structures within the region. Finally, edge simplification and folding are performed according to the ranked edge folding costs. Simplification can be carried out in an orderly manner according to the order of errors from smallest to largest, avoiding the destruction of the model's topological connectivity due to disordered folding. At the same time, operations are only performed on edges within the growth region, ensuring efficient simplification of non-feature regions while maximizing the preservation of geometric fidelity of feature regions. Ultimately, this achieves a synergy between "feature preservation" and "efficient simplification," solving the problems of easy loss of local features and easy destruction of topology in existing global simplification methods, thereby improving the simplification effect. Attached Figure Description

[0041] Figure 1 A flowchart illustrating the method for simplifying a three-dimensional mesh model provided in an embodiment of the present invention;

[0042] Figure 2 This is a schematic diagram of the structure of the three-dimensional mesh model simplification device provided in an embodiment of the present invention;

[0043] Figure 3 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention;

[0044] Figure 4 A schematic diagram of the freely selectable region provided in an embodiment of the present invention;

[0045] Figure 5 A schematic diagram of the growth region provided in an embodiment of the present invention;

[0046] Figure 6 This is a simplified schematic diagram provided for an embodiment of the present invention. Detailed Implementation

[0047] To make the above-mentioned objects, features, and advantages of the present invention more apparent and understandable, specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Although some embodiments of the present invention are shown in the drawings, it should be understood that the present invention can be implemented in various forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided to provide a more thorough and complete understanding of the present invention. It should be understood that the accompanying drawings and embodiments of the present invention are for illustrative purposes only and are not intended to limit the scope of protection of the present invention.

[0048] It should be understood that the various steps described in the method embodiments of the present invention may be performed in different orders and / or in parallel. Furthermore, the method embodiments may include additional steps and / or omit the steps shown. The scope of the present invention is not limited in this respect.

[0049] The term "comprising" and its variations as used herein are open-ended, meaning "including but not limited to"; the term "based on" means "at least partially based on"; the term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one additional embodiment"; the term "some embodiments" means "at least some embodiments"; and the term "optionally" means "optional embodiments". Definitions of other terms will be given in the following description. It should be noted that the concepts of "first," "second," etc., mentioned in this invention are used only to distinguish different devices, modules, or units, and are not intended to limit the order of functions performed by these devices, modules, or units or their interdependencies.

[0050] It should be noted that the terms "a" and "a plurality of" used in this invention are illustrative rather than restrictive. Those skilled in the art should understand that, unless otherwise expressly indicated in the context, they should be understood as "one or more".

[0051] The names of the messages or information exchanged between the multiple devices in the embodiments of the present invention are for illustrative purposes only and are not intended to limit the scope of these messages or information.

[0052] To address the problems existing in the aforementioned related technologies, this embodiment provides a method, apparatus, device, and medium for simplifying three-dimensional mesh models.

[0053] like Figure 1As shown in the figure, an embodiment of the present invention provides a method for simplifying a three-dimensional mesh model, comprising:

[0054] A region growing algorithm is used to determine the growing region based on the freely selectable region and geometric topology information of the 3D mesh model.

[0055] Specifically, firstly, a three-dimensional mesh model of the precision part, a freely selectable region of the three-dimensional mesh model, and geometric topology information are obtained. The freely selectable region can be obtained through human-computer interaction. The geometric topology information includes vertex data, edge data, and triangular facet data. Then, a region growing algorithm is used to determine the growing region based on the freely selectable region and geometric topology information, thereby simplifying the geometric topology information in the growing region. The growing region includes at least one region, and the specific number is determined according to the actual situation.

[0056] Based on the geometric topology information, the error matrix of each triangular facet in the growth region is determined, and the sum of the quadratic errors of all associated triangular facets is determined based on the error matrix.

[0057] Specifically, based on the geometric topology information, a quadratic error metric algorithm is used to determine the error matrix of each triangular facet in the growth region. The error matrix is ​​a way of quantifying the impact of folding and simplifying the vertices of a triangular facet on the overall shape of the 3D network model. Essentially, it is a symmetric matrix that quantifies the shape change that may be caused when folding one vertex to another. It contains information about the vertex and its associated edges. Then, based on the error matrix, the sum of quadratic errors of all associated triangular facets is determined. An associated triangular facet is a triangular facet connected to each vertex of the triangular facet. The error matrices of all associated triangular facets are added together to form a comprehensive error matrix, which is the sum of quadratic errors, thereby determining the new vertex.

[0058] Based on the sum of the quadratic errors, the vertex folding cost and the new vertex are determined, and based on the new vertex, the edge folding cost is determined and prioritized.

[0059] Specifically, a quadratic error metric algorithm is employed. Based on the sum of quadratic errors, the vertex folding cost of all vertices in the growth region is determined. In this algorithm, the sum of quadratic errors is the basis for calculating the folding cost and is an existing technique, so it will not be elaborated upon here. The vertex folding cost is used to evaluate and determine which vertex folding operations can reduce the number of polygons with minimal visual loss. Through the vertex folding cost, new vertices can be determined. These new vertices are those that can reduce the number of polygons with minimal visual loss among all vertices in the growth region. This can be achieved by solving for the sum of quadratic errors, determining the eigenvector corresponding to the smallest eigenvalue in the comprehensive error matrix, i.e., finding the point that minimizes the quadratic form represented by the error matrix. This can be solved using eigenvalue decomposition techniques in linear algebra. The position pointed to by the obtained eigenvector is the optimal position of the new vertex. Then, based on the optimal position of the new vertex, the quadratic error metric algorithm is used to determine the folding cost of each edge in the growth region folding to that new vertex, i.e., the edge folding cost, and these costs are prioritized. The priority ranking rule is that the smaller the edge folding cost, the higher the priority. The edge folding cost can be the sum of the point folding costs of folding the two vertices on the edge to the new vertex.

[0060] Based on the sorted edge folding cost, perform edge simplification folding to obtain simplified vertices and corresponding simplified geometric topology information.

[0061] Specifically, the edge with the lowest folding cost, i.e. the highest priority, is simplified and folded. The two original vertices on this edge are folded to the new vertex positions, and the edges originally connected to the two vertices are also connected to the new vertex positions. The original vertices and edges are then deleted. This process is repeated to determine the next new vertex and perform edge folding simplification again, until the next new vertex is located at the boundary of the growth region. This completes the loop and obtains all simplified vertices and their corresponding simplified geometric topology information within the growth region, thus completing the simplification. When there are multiple growth regions, after simplifying the current growth region, the above steps are used to simplify the next growth region, until all growth regions are simplified.

[0062] In this embodiment, a region growing algorithm is employed to determine the growing region based on the freely selectable region of the 3D mesh model and geometric topology information. This process defines the simplification range through human-computer interaction by allowing free region selection, and dynamically expands the growing region using geometric topology information (such as curvature and normal vectors). This accurately identifies the non-feature regions to be simplified while relying on topological connectivity to prevent the growing region from crossing feature boundaries, effectively preventing feature regions (such as assembly holes and edge sharpness of precision parts) from being included in the simplification range, thus reducing the risk of feature deformation or distortion from the source. Furthermore, based on the geometric topology information, the error matrix of each triangular facet in the growing region is determined, and the sum of the quadratic errors of the associated triangular facets is calculated. Through the precise measurement of geometric errors using the error matrix, the impact of the simplification operation on local geometric features can be accurately quantified. The method ensures that the sum of quadratic errors accurately reflects the geometric deviations that simplification may bring, providing a reliable basis for error judgment in subsequent simplification operations. Based on the sum of quadratic errors, the vertex folding cost and new vertex are determined, and the edge folding cost and priority ranking are determined accordingly. This allows for the priority selection of edges with the least impact on the model's visual and geometric features (minimum folding cost), ensuring that non-critical geometric elements are processed first during the simplification process, reducing disturbance to important geometric structures within the region. Finally, edge simplification and folding are performed according to the ranked edge folding costs. Simplification can be performed in an orderly manner according to the order of errors from smallest to largest, avoiding the destruction of the model's topological connectivity due to disordered folding. At the same time, operations are only performed on edges within the growth region, ensuring efficient simplification of non-feature regions while maximizing the preservation of geometric fidelity of feature regions. Ultimately, this achieves a synergy between "feature preservation" and "efficient simplification," solving the problems of easy loss of local features and easy destruction of topology in existing global simplification methods, thereby improving the simplification effect.

[0063] Optionally, the step of performing simplified edge folding based on the sorted edge folding costs to obtain simplified vertices and corresponding simplified geometric topology information includes:

[0064] Extract the vertex data of the edge corresponding to the edge folding cost with the highest priority, and perform pre-simplified folding based on the vertex data to generate pre-simplified vertices;

[0065] When the pre-simplified vertex coincides with the boundary vertex of the freely selected region, the edge folding cost is re-determined and the priority is sorted.

[0066] When the pre-simplified vertex does not coincide with the boundary vertex of the freely selected region, the pre-simplified vertex is used as the simplified vertex, and the corresponding simplified geometric topology information is obtained.

[0067] Specifically, before performing a simplified fold, a pre-simplified fold is required. This involves determining whether the optimal position of the simplified vertices (the new vertices mentioned above) coincides with the boundary vertices. If they coincide, the edge folding cost step is returned and recalculated. If they do not coincide, a simplified fold is performed. This avoids affecting the model's boundary information, ensuring that the simplified model retains the main shape of the original model while reducing unnecessary details, and avoiding boundary problems caused by improper folding.

[0068] Optionally, the region growing algorithm is used to determine the growing region based on the freely selectable region and geometric topology information of the 3D mesh model, including:

[0069] Based on the freely selected region, determine the simplified region boundary of the three-dimensional mesh model;

[0070] Based on the region growth criterion, the initial seed vertex is expanded according to the initial seed vertex and the simplified region boundary, and the final seed vertex and its corresponding neighboring vertex are determined iteratively.

[0071] Based on the final seed vertex, a quadratic error metric algorithm is used to determine the extended vertices of the adjacent vertices;

[0072] Obtain the geometric topology information of all the final seed vertices, the adjacent vertices, and the extended vertices within the simplified region boundary to determine the growth region.

[0073] Specifically, the area can be freely selected, such as Figure 4 As shown, the 3D coordinates of each vertex in the freely selected region can be read using the WindowButtonUpFcn() function, thus transforming the freely selected region obtained through human-computer interaction into a simplified region boundary of a 3D mesh model. Then, based on the region growth criterion, and according to one or more initial seed vertices obtained through human-computer interaction and the simplified region boundary, the initial seed vertices are expanded. The initial seed vertices serve as the starting point for the region growth algorithm. If the adjacent vertices of an initial seed vertex satisfy the region growth criterion, they are added to the current region and treated as new seed points for further checking until no adjacent vertices satisfy the criteria are found. This process yields at least one final seed vertex and its corresponding adjacent vertices. Each final seed vertex has a corresponding growth region; that is, the number of final seed vertices corresponds to the number of growth regions. Then, based on the final seed vertices, a quadratic error metric algorithm is used to determine the expandable vertices among the adjacent vertices to clarify the range of the growth region. Finally, the geometric topological information of all final seed vertices, adjacent vertices, and expanded vertices within the simplified region boundary is obtained to determine the information of the growth region. Based on the coordinates, edge data, and triangular facet data of the final seed vertices, adjacent vertices, and expanded vertices, the spatial range of the growth region is determined. The range of the growth region is as follows: Figure 5As shown within the dashed line, data such as curvature and normal vector are recorded in the growth region, i.e., statistical growth region information, which facilitates subsequent simplification.

[0074] Optionally, the region growth criterion includes geometric similarity conditions and topological connectivity conditions. The step of expanding the initial seed vertex based on the region growth criterion and the simplified region boundary, and iteratively determining the final seed vertex and its corresponding neighboring vertices, includes:

[0075] The growth threshold is determined based on the geometric similarity condition and the topological connectivity condition.

[0076] Determine the candidate neighboring vertices of the initial seed vertex, and determine the geometric similarity data and topological connectivity data of the candidate neighboring vertices;

[0077] Based on the growth threshold, the final seed vertex and its corresponding neighboring vertex are determined according to the geometric similarity data and the topological connectivity data, and the neighboring vertex is used as the candidate neighboring vertex to iteratively determine all the final seed vertices.

[0078] Specifically, the region growth criteria include geometric similarity conditions and topological connectivity conditions. Geometric similarity conditions refer to setting certain geometric similarity standards, such as curvature deviation <5% or normal vector angle <15°, as the basis for determining whether neighboring vertices can be added to the current region. Topological connectivity conditions refer to considering the need for connectivity between vertices to ensure that the model's coherence is not disrupted during expansion. First, based on the geometric similarity and topological connectivity conditions, a growth threshold is determined through experimentation or experience. The growth threshold mainly controls the standard for region expansion, ensuring that only neighboring vertices meeting specific conditions can be included in the current region. Then, candidate neighboring vertices of the initial seed vertex are determined, and their geometric similarity and topological connectivity data are further determined. Finally, based on the growth threshold and the geometric similarity and topological connectivity data, it is determined whether the candidate neighboring vertex is included in the current region. If included, the candidate neighboring vertex becomes the final seed vertex, thus determining the final seed vertex and its corresponding neighboring vertices. These neighboring vertices are then used as candidate neighboring vertices. This process iteratively determines all final seed vertices until no candidate neighboring vertices meet the conditions. The growth threshold is not a single value, but a set of rules determined by multiple parameters. It allows for flexible adjustment based on different model characteristics and application requirements. In practical applications, it may be necessary to conduct multiple trials to find the most suitable combination of thresholds for a specific task.

[0079] For example, geometric similarity conditions may include gray-level difference, color space Euclidean distance, and texture gradient difference. The growth threshold may include corresponding gray-level difference thresholds, color space Euclidean distance thresholds, and texture gradient difference thresholds. The gray-level difference, color space Euclidean distance, and texture gradient difference of candidate adjacent vertices can be determined using the gray-level difference formula, color space Euclidean distance formula, and texture gradient difference formula, respectively. The gray-level difference formula includes:

[0080] ;

[0081] in, Let be the gray-level difference value at point (p, q), and I be the gray-level function.

[0082] The Euclidean distance formula for color space includes:

[0083] ;

[0084] in, Let R, G, and B be the Euclidean distance in color space at point (p, q), where R, G, and B are all color functions.

[0085] The formula for texture gradient difference includes:

[0086] ;

[0087] in, Let be the Euclidean distance in color space at point (p, q). This is the texture gradient function.

[0088] For example, iteration can be performed using an iterative process formula, which includes:

[0089] ;

[0090] Among them, R t For the region at time t, R t+1 For the region at time t+1, N(R) t ) represents the set of neighboring pixels of the current region. For region R t The average eigenvalues, where T is the growth threshold parameter. This refers to the numerical value corresponding to the geometric similarity condition, such as the grayscale difference value, the Euclidean distance value in the color space, or the texture gradient difference value.

[0091] Optionally, the step of expanding the initial seed vertex based on the region growth criterion, according to the initial seed vertex and the simplified region boundary, and iteratively determining the final seed vertex and its corresponding neighboring vertices, further includes:

[0092] When the difference between the geometric similarity data and the topological connectivity data of different candidate adjacent vertices exceeds a preset range, the growth threshold is adjusted according to a dynamic adjustment strategy.

[0093] Specifically, during region growing, the growth threshold can be dynamically adjusted based on changes in local features (such as encountering edges or sharp features). For example, when significant feature changes are detected (such as curvature transitions exceeding 30%), the growth threshold may be reduced to capture these features more precisely; while in relatively smooth regions, the threshold may be relaxed to speed up processing.

[0094] Optionally, before determining the growth region based on the freely selected region and geometric topology information of the 3D mesh model using the region growing algorithm, the method further includes:

[0095] Based on the requirement of continuity, the freely selectable area is obtained according to the human-computer interaction instructions.

[0096] Specifically, the continuity requirement means that the vertices of the freely selectable region obtained through human-computer interaction commands are continuous vertices in the three-dimensional network model.

[0097] Optionally, after performing edge simplification folding based on the sorted edge folding cost to obtain simplified vertices and corresponding simplified geometric topology information, the method further includes:

[0098] Based on the simplified vertices and the simplified geometric topology information, a simplified 3D mesh model is drawn.

[0099] Specifically, simplified vertex and simplified geometric topology information is stored in an SMF file, and a simplified 3D mesh model is drawn based on the simplified vertex and simplified geometric topology information, such as... Figure 6 As shown, the area within the dashed line represents the simplified portion.

[0100] like Figure 2 As shown in the figure, an embodiment of the present invention provides a three-dimensional mesh model simplification device, comprising:

[0101] The growth module is used to determine the growth region based on the freely selectable region and geometric topology information of the 3D mesh model using a region growth algorithm.

[0102] An error module is used to determine the error matrix of each triangular facet in the growth region based on the geometric topology information, and to determine the sum of the quadratic errors of all associated triangular facets based on the error matrix.

[0103] The cost module is used to determine the vertex folding cost and the new vertex based on the sum of the quadratic errors, and to determine the edge folding cost and prioritize it based on the new vertex.

[0104] The simplification module is used to perform edge simplification folding based on the sorted edge folding cost to obtain simplified vertices and corresponding simplified geometric topology information.

[0105] like Figure 3 As shown, an electronic device 300 provided in this embodiment of the invention includes a memory 310 and a processor 320; the memory 310 is used to store a computer program; the processor 320 is used to implement the three-dimensional mesh model simplification method as described above when the computer program is executed.

[0106] Alternatively, an electronic device 300 includes a memory 310 and a processor 320 coupled to the memory 310; the memory 310 is configured to store a computer program; and the processor 320 is configured to perform the following operations when the computer program is executed:

[0107] A region growing algorithm is used to determine the growing region based on the freely selectable region and geometric topology information of the 3D mesh model;

[0108] Based on the geometric topology information, the error matrix of each triangular facet in the growth region is determined, and the sum of the quadratic errors of all associated triangular facets is determined based on the error matrix.

[0109] Based on the sum of the quadratic errors, determine the vertex folding cost and the new vertex, and based on the new vertex, determine the edge folding cost and prioritize them.

[0110] Based on the sorted edge folding cost, perform edge simplification folding to obtain simplified vertices and corresponding simplified geometric topology information.

[0111] This invention provides a computer-readable storage medium storing a computer program. When the computer program is executed by a processor, it implements the three-dimensional mesh model simplification method described above.

[0112] Alternatively, a non-volatile computer-readable storage medium storing a computer program that, when executed by a processor, causes the processor to perform the following operations:

[0113] A region growing algorithm is used to determine the growing region based on the freely selectable region and geometric topology information of the 3D mesh model;

[0114] Based on the geometric topology information, the error matrix of each triangular facet in the growth region is determined, and the sum of the quadratic errors of all associated triangular facets is determined based on the error matrix.

[0115] Based on the sum of the quadratic errors, determine the vertex folding cost and the new vertex, and based on the new vertex, determine the edge folding cost and prioritize them.

[0116] Based on the sorted edge folding cost, perform edge simplification folding to obtain simplified vertices and corresponding simplified geometric topology information.

[0117] The present invention will now be described an electronic device 300 that can serve as a server or client of the present invention, which is an example of a hardware device that can be applied to various aspects of the present invention. Electronic device 300 is intended to represent various forms of digital electronic computer devices, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. Electronic device 300 can also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the invention described and / or claimed herein.

[0118] Electronic device 300 includes a computing unit that can perform various appropriate actions and processes based on a computer program stored in read-only memory (ROM) or a computer program loaded from a storage unit into random access memory (RAM). The RAM may also store various programs and data required for device operation. The computing unit, ROM, and RAM are interconnected via a bus. Input / output (I / O) interfaces are also connected to the bus.

[0119] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. The storage medium can be a magnetic disk, optical disk, read-only memory (ROM), or random access memory (RAM), etc. In this application, the units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of the embodiments of the present invention according to actual needs. Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated units can be implemented in hardware or as software functional units.

[0120] While the present invention has been disclosed above, its scope of protection is not limited thereto. Those skilled in the art can make various changes and modifications without departing from the spirit and scope of the present invention, and all such changes and modifications will fall within the scope of protection of the present invention.

Claims

1. A method of simplifying a three-dimensional mesh model, characterized by, The method comprises the following steps: obtaining a three-dimensional mesh model of a precision part, a free selection area of the three-dimensional mesh model and geometric topology information; determining a growth area according to the free selection area of the three-dimensional mesh model and the geometric topology information by using a region growing algorithm; determining an error matrix of each triangular facet in the growth area according to the geometric topology information, and determining a sum of quadratic errors of all associated triangular facets according to the error matrix; determining a vertex folding cost and a new vertex according to the sum of quadratic errors, and determining an edge folding cost and priority ranking according to the new vertex; performing edge simplification folding according to the edge folding cost after ranking to obtain a simplified vertex and corresponding simplified geometric topology information; the step of determining the growth area according to the free selection area of the three-dimensional mesh model and the geometric topology information by using the region growing algorithm comprises the following steps: determining a simplified area boundary of the three-dimensional mesh model according to the free selection area; expanding an initial seed vertex and iteratively determining a final seed vertex and corresponding adjacent vertices according to the initial seed vertex and the simplified area boundary based on a region growing criterion; determining an expanded vertex of the adjacent vertices by using a quadratic error measurement algorithm according to the final seed vertex; determining the growth area by obtaining geometric topology information of all the final seed vertices, the adjacent vertices and the expanded vertices within the simplified area boundary; the region growing criterion comprises a geometric similarity condition and a topological connectivity condition, and the step of expanding the initial seed vertex and iteratively determining the final seed vertex and the corresponding adjacent vertices according to the initial seed vertex and the simplified area boundary based on the region growing criterion comprises the following steps: determining a growth threshold based on the geometric similarity condition and the topological connectivity condition; determining a candidate adjacent vertex of the initial seed vertex, and determining geometric similarity data and topological connectivity data of the candidate adjacent vertex; determining the final seed vertex and the corresponding adjacent vertices according to the geometric similarity data and the topological connectivity data based on the growth threshold, and returning to the step of determining the geometric similarity data and the topological connectivity data of the candidate adjacent vertex, and iteratively determining all the final seed vertices by taking the adjacent vertices as the candidate adjacent vertices.

2. The three-dimensional mesh model simplification method of claim 1, wherein, the step of performing edge simplification folding according to the edge folding cost after ranking to obtain a simplified vertex and corresponding simplified geometric topology information comprises the following steps: extracting edge vertex data corresponding to the edge folding cost with the highest priority, and performing pre-simplification folding according to the edge vertex data to generate a pre-simplified vertex; when the pre-simplified vertex coincides with a boundary vertex of the free selection area, re-determining the edge folding cost and performing priority ranking; when the pre-simplified vertex does not coincide with the boundary vertex of the free selection area, taking the pre-simplified vertex as the simplified vertex, and obtaining corresponding simplified geometric topology information.

3. The three-dimensional mesh model simplification method of claim 1, wherein, The region growing criterion comprises a geometric similarity condition and a topological connectivity condition, and the extending the initial seed vertex according to the region growing criterion and the simplified region boundary comprises: When the difference between the geometric similarity data and the topological connectivity data of different candidate adjacent vertices exceeds a preset range, a dynamic adjustment strategy is used to adjust the growth threshold.

4. The three-dimensional mesh model simplification method of claim 1, wherein, Before the region growing algorithm is used to determine a growth region according to the free selection region and the geometric topological information of the three-dimensional mesh model, the method further comprises: According to the continuity requirement, the free selection region is obtained according to the human-computer interaction instruction.

5. The method of claim 1, wherein After the edge simplification folding is performed according to the sorted edge folding cost to obtain simplified vertices and corresponding simplified geometric topological information, the method further comprises: According to the simplified vertices and the simplified geometric topological information, a simplified three-dimensional mesh model is drawn.

6. A three-dimensional mesh model simplification apparatus characterized by comprising: The method comprises: a growing module configured to obtain a three-dimensional mesh model of a precision part, a free selection region of the three-dimensional mesh model, and geometric topological information of the three-dimensional mesh model; and use a region growing algorithm to determine a growth region according to the free selection region and the geometric topological information of the three-dimensional mesh model; an error module configured to determine an error matrix of each triangular facet in the growth region according to the geometric topological information, and determine a sum of quadratic errors of all associated triangular facets according to the error matrix; a cost module configured to determine a vertex folding cost and a new vertex according to the sum of quadratic errors, and determine an edge folding cost and perform priority sorting according to the new vertex; a simplification module configured to perform edge simplification folding according to the sorted edge folding cost to obtain simplified vertices and corresponding simplified geometric topological information; The region growing algorithm is used to determine a growth region according to a free selection region and geometric topological information of a three-dimensional mesh model, and the method comprises: According to the free selection region, a simplified region boundary of the three-dimensional mesh model is determined; According to a region growing criterion, an initial seed vertex is extended according to the initial seed vertex and the simplified region boundary, and a final seed vertex and a corresponding adjacent vertex are iteratively determined; According to the final seed vertex, a topological vertex of the adjacent vertex is determined by using a quadratic error measurement algorithm; Geometric topological information of all the final seed vertices, the adjacent vertices, and the topological vertices in the simplified region boundary is obtained to determine the growth region; The region growing criterion comprises a geometric similarity condition and a topological connectivity condition, and the extending the initial seed vertex according to the region growing criterion and the simplified region boundary comprises: Based on the geometric similarity condition and the topological connectivity condition, a growth threshold is determined; Candidate adjacent vertices of the initial seed vertex are determined, and geometric similarity data and topological connectivity data of the candidate adjacent vertices are determined; Based on the growth threshold, the final seed vertex and its corresponding adjacent vertex are determined according to the geometric similarity data and the topological connectivity data, and the adjacent vertex is taken as the candidate adjacent vertex, and the step of determining the geometric similarity data and the topological connectivity data of the candidate adjacent vertex is returned, and iteration is performed to determine all the final seed vertices.

7. An electronic device, comprising: comprise a memory and a processor; the memory is configured to store a computer program; the processor is configured to implement the three-dimensional mesh model simplification method according to any one of claims 1 to 5 when the computer program is executed.

8. A computer-readable storage medium, characterized in that, The storage medium has a computer program stored thereon, and when the computer program is executed by a processor, the three-dimensional mesh model simplification method according to any one of claims 1 to 5 is implemented.

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