Three-dimensional grid model simplification method and device, equipment and medium

Abstract

The invention provides a three-dimensional grid model simplification method and device, equipment and a medium, and relates to the technical field of model simplification, and the method comprises the steps: employing a region growth algorithm, and determining a growth region according to a free selection region and geometric topology information of a three-dimensional grid model; determining an error matrix of each triangular patch in the growth area according to the geometric topology information, and determining the sum of secondary errors of all associated triangular patches according to the error matrix; according to the sum of the secondary errors, determining a vertex folding cost and a new vertex, and according to the new vertex, determining an edge folding cost and performing priority ranking; and performing edge simplified folding according to the sorted edge folding cost to obtain simplified vertexes and corresponding simplified geometric topology information. The invention can improve the simplification effect.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of model simplification, in particular to a three-dimensional mesh model simplification method, device, equipment and medium. BACKGROUND

[0002] A three-dimensional mesh model is a digital model used to represent a three-dimensional object, which is composed of a series of vertices, edges and faces that collectively define the shape and appearance of the object. Three-dimensional mesh models are widely used in various fields, including but not limited to precision parts, virtual reality, game development, animation production, architectural visualization, engineering simulation, medical image processing, and cultural heritage preservation. In some fields, to meet the needs of computational efficiency, optimize storage space, enhance interactivity, and address the limitations of existing technologies, it is necessary to simplify three-dimensional mesh models.

[0003] In related technologies, methods such as quadratic error metric (QEM) algorithm, clustering algorithm and wavelet transform are generally used for simplification. However, these methods have the problem of over-pursuing overall compression rate, resulting in severe loss of local details (such as texture, edge sharpness), lack of perception of model topology in the simplification process, which may lead to destruction of model connectivity, and the need for repeated parameter adjustment for different models to achieve optimal results, which weakens the universality and practicality of the algorithm, and the simplification effect is poor. SUMMARY

[0004] The problem solved by the present application is how to improve the simplification effect of three-dimensional mesh models.

[0005] To solve the above problems, the present application provides a three-dimensional mesh model simplification method, device, equipment and medium.

[0006] In a first aspect, the present application provides a three-dimensional mesh model simplification method, comprising: using 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 vertex folding cost and new vertices according to the sum of quadratic errors, and determining edge folding cost and performing priority sorting according to the new vertices; performing edge simplification folding according to the sorted edge folding cost to obtain simplified vertices and corresponding simplified geometric topology information.

[0007] Optionally, the edge simplification folding according to the sorted edge folding cost to obtain simplified vertices and corresponding simplified geometric topology information comprises: extracting an edge vertex data corresponding to an edge collapse cost with the highest priority, and performing a pre-simplification collapse according to the edge vertex data to generate a pre-simplification vertex; when the pre-simplification vertex coincides with a boundary vertex of the free selection region, re-determining the edge collapse cost and prioritizing; when the pre-simplification vertex does not coincide with the boundary vertex of the free selection region, taking the pre-simplification vertex as the simplification vertex, and obtaining corresponding simplification geometric topology information.

[0008] Optionally, the region growing algorithm is used to determine a growing region according to the free selection region and the geometric topology information of the three-dimensional mesh model, comprising: determining a simplification region boundary of the three-dimensional mesh model according to the free selection region; based on a region growing criterion, expanding the initial seed vertex according to the initial seed vertex and the simplification region boundary, and iteratively determining a final seed vertex and a corresponding adjacent vertex; using a quadratic error metric algorithm to determine an expanded vertex of the adjacent vertex according to the final seed vertex; obtaining geometric topology information of all the final seed vertices, the adjacent vertices and the expanded vertices within the simplification region boundary to determine the growing region.

[0009] Optionally, the region growing criterion comprises a geometric similarity condition and a topological connectivity condition, and the region growing criterion is used to expand the initial seed vertex according to the initial seed vertex and the simplification region boundary, and iteratively determine a final seed vertex and a corresponding adjacent vertex, comprising: determining a growing 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; based on the growing threshold, determining the final seed vertex and the corresponding adjacent vertex according to the geometric similarity data and the topological connectivity data, and taking the adjacent vertex as the candidate adjacent vertex to iteratively determine all the final seed vertices.

[0010] Optionally, the region growing criterion is used to expand the initial seed vertex according to the initial seed vertex and the simplification region boundary, and iteratively determine a final seed vertex and a corresponding adjacent vertex, further comprising: when differences between the geometric similarity data and the topological connectivity data of different candidate adjacent vertices exceed a preset range, adjusting the growing threshold according to a dynamic adjustment strategy.

[0011] Optionally, before the growing region is determined by using the region growing algorithm according to the free selection region and the geometric topology 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.

[0012] Optionally, after the simplified vertex and the corresponding simplified geometric topology information are obtained by performing the edge simplification folding according to the sorted edge folding cost, the method further comprises: According to the simplified vertex and the simplified geometric topology information, the simplified three-dimensional mesh model is drawn.

[0013] In a second aspect, the present application provides a three-dimensional mesh model simplification device, comprising: a growing module configured to determine a growing region by using a region growing algorithm according to a free selection region and geometric topology information of a three-dimensional mesh model; an error module configured to determine an error matrix of each triangular facet in the growing region according to the geometric topology 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 a simplified vertex and corresponding simplified geometric topology information.

[0014] In a third aspect, the present application provides an electronic device comprising 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 of the first aspect when the computer program is executed.

[0015] In a fourth aspect, the present application provides a computer readable storage medium, wherein the storage medium stores a computer program, and when the computer program is executed by a processor, the three-dimensional mesh model simplification method of the first aspect is implemented.

[0016] The three-dimensional mesh model simplification method, device, equipment and medium of the present application have the following advantages: The region growing algorithm is adopted, the growing region is determined according to the free selection region and geometric topological information of the three-dimensional grid model, the process is simplified through the free selection region defined by human-computer interaction, and the growing region is dynamically expanded in combination with the geometric topological information, so that the non-feature region to be simplified can be accurately locked, and the feature region (such as the assembly hole position and edge sharpness of a precision part) can be effectively prevented from being included in the simplified range, thereby reducing the risk of feature deformation or distortion from the source; on this basis, the error matrix of each triangular facet in the growing region is determined according to the geometric topological information, and the sum of the quadratic errors of the associated triangular facets is calculated, through the fine measurement of the geometric error by the error matrix, the influence of the simplification operation on the local geometric feature can be accurately quantified, and it is ensured that the sum of the quadratic errors can truly reflect the geometric deviation possibly caused by the simplification, thereby providing a reliable error judgment basis for the subsequent simplification operation; based on the sum of the quadratic errors, the vertex folding cost and the new vertex are determined, and the edge folding cost and the priority sorting are determined according to the vertex folding cost and the new vertex, so that the edge (with the minimum folding cost) with the minimum influence on the visual and geometric features of the model can be preferentially selected, and the non-key geometric elements are preferentially processed in the simplification process, thereby reducing the disturbance to the important geometric structure in the region; finally, the edge simplification folding is performed according to the edge folding cost after sorting, the simplification can be sequentially performed in the order from small to large error, the topological connectivity of the model is avoided from being damaged due to unordered folding, and the operation is only performed on the edges in the growing region, so that the geometric fidelity of the feature region is maximally preserved while the non-feature region is efficiently simplified, and finally the cooperation of 'preserving features' and 'efficient simplification' is realized, thereby solving the problems of easy loss of local features and easy damage of topology in the existing global simplification method, and improving the simplification effect. BRIEF DESCRIPTION OF DRAWINGS

[0017] Figure 1 A flowchart of a three-dimensional grid model simplification method provided by an embodiment of the present application is shown in the figure; Figure 2 A structural schematic diagram of a three-dimensional grid model simplification device provided by an embodiment of the present application is shown in the figure; Figure 3 A structural schematic diagram of an electronic device provided by an embodiment of the present application is shown in the figure; Figure 4 A schematic diagram of a free selection region provided by an embodiment of the present application is shown in the figure; Figure 5 A schematic diagram of a growing region provided by an embodiment of the present application is shown in the figure; Figure 6 A schematic diagram of a simplified region provided by an embodiment of the present application is shown in the figure. DETAILED DESCRIPTION

[0018] In order to make the above objectives, characteristics and advantages of the present application more apparent, concrete embodiments of the present application will be described in detail below with reference to the drawings. Although some embodiments of the present application are shown in the drawings, it should be understood that the present application can be implemented in various forms, and should not be interpreted as being limited to the embodiments set forth herein, but rather, these embodiments are provided so as to more thoroughly and completely understand the present application. It should be understood that the drawings and embodiments of the present application are merely for illustrative purposes, and are not intended to limit the scope of protection of the present application.

[0019] It should be understood that each of the steps described in the method embodiments of the present application can be performed in different orders, and / or in parallel. In addition, the method embodiments can include additional steps and / or omit the steps shown. The scope of the present application is not limited in this respect.

[0020] As used herein, the term "comprises" and its variants are to be construed as open- ended, that is, "including, but not limited to," the term "based on" is "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"; the term "optionally" means "optional embodiments." Related definitions will be given in the description below. It should be noted that the concepts of "first", "second", etc. mentioned in the present application are only used to distinguish different devices, modules or units, and are not intended to limit the order or interdependence of the functions performed by these devices, modules or units.

[0021] It should be noted that the modification of "one" or "multiple" mentioned in the present application is illustrative rather than limiting, and those skilled in the art should understand that, unless otherwise explicitly indicated in the context, it should be understood as "one or more".

[0022] The names of the messages or information exchanged between the devices in the embodiments of the present application are only for illustrative purposes, and are not intended to limit the scope of the messages or information.

[0023] To solve the problems of the related art described above, the embodiments of the present application provide a three-dimensional mesh model simplification method, device, equipment and medium.

[0024] As shown in Figure 1 A three-dimensional mesh model simplification method provided by the embodiments of the present application includes: The region growing algorithm is used to determine the growing region according to the free selection region and geometric topology information of the three-dimensional mesh model.

[0025] Specifically, first, a three-dimensional mesh model of a precision part, a free selection area of the three-dimensional mesh model and geometric topology information are acquired, the free selection area can be obtained through human-computer interaction, and the geometric topology information includes vertex data, edge data and triangular facet data; then, a region growing algorithm is adopted to determine a growing region according to the free selection area and the geometric topology information, so as to simplify the geometric topology information in the growing region, wherein the growing region includes at least one, and the specific number is determined according to actual conditions.

[0026] According to the geometric topology information, an error matrix of each triangular facet in the growing region is determined, and a sum of quadratic errors of all associated triangular facets is determined according to the error matrix.

[0027] Specifically, according to the geometric topology information, a quadratic error measurement algorithm is adopted to determine an error matrix of each triangular facet in the growing region, the error matrix is an expression mode for quantifying the influence of folding a vertex on the overall shape of the three-dimensional mesh model, and is essentially a symmetric matrix, which quantifies the shape change that may be caused when folding one vertex to another vertex, and contains information about the vertex and its associated edges; then, a sum of quadratic errors of all associated triangular facets is determined according to the error matrix, the associated triangular facets refer to the triangular facets connected with each vertex of the triangular facet, the error matrices of all associated triangular facets are added to form a comprehensive error matrix, i.e., the sum of quadratic errors, so as to determine a new vertex.

[0028] According to the sum of quadratic errors, a vertex folding cost and a new vertex are determined, and according to the new vertex, an edge folding cost is determined and prioritized.

[0029] Specifically, a quadratic error metric algorithm is adopted to determine vertex folding costs of all vertices in the growing region according to a sum of quadratic errors, the sum of quadratic errors is the basis for calculating the folding cost in the quadratic error metric algorithm, which is prior art and will not be described here. The vertex folding cost is used to evaluate and determine which vertex folding operation can achieve the purpose of reducing the number of polygons with the least visual loss. Through the vertex folding cost, a new vertex can be determined, which refers to a vertex in all vertices in the growing region that can achieve the purpose of reducing the number of polygons with the least visual loss. The sum of quadratic errors can be solved to determine a feature vector corresponding to the minimum eigenvalue in a comprehensive error matrix corresponding to the sum of quadratic errors, that is, to find a point that minimizes the quadratic form represented by the error matrix. The feature vector obtained by eigenvalue decomposition technology in linear algebra points to the best position of the new vertex. Then, according to the best position of the new vertex, the quadratic error metric algorithm is used to determine the folding cost of each edge folding to the new vertex in the growing region, that is, the edge folding cost, and to perform priority sorting. The rule of priority sorting 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 the two vertices of the edge folding to the new vertex.

[0030] Edge simplification folding is performed according to the sorted edge folding cost to obtain simplified vertices and corresponding simplified geometric topology information.

[0031] Specifically, the edge with the smallest edge folding cost, that is, the highest priority, is subjected to edge simplification folding. The two original vertices on the edge are folded to the position of the new vertex, and the edges originally connected to the two vertices are also connected to the position of the new vertex. The original vertices and edges are then deleted. Then, the cycle is restarted to determine the next new vertex and perform edge folding simplification until the next new vertex is located at the boundary of the growing region, the cycle is completed, and all simplified vertices and corresponding simplified geometric topology information in the growing region are obtained to complete the simplification. When there are multiple growing regions, the above steps are used to simplify the next growing region after the simplification of the growing region is completed, until all growing regions are simplified.

[0032] In this embodiment, the region growing algorithm is adopted, the growing region is determined according to the free selection region and geometric topology information of the three-dimensional mesh model, the simplification range is defined through the free selection region of human-computer interaction, and the growing region is dynamically expanded in combination with the geometric topology information (such as curvature, normal vector and the like), so that the non-feature region to be simplified can be accurately locked, the growing region can be avoided from crossing the feature boundary relying on the topological connectivity, the feature region (such as the assembly hole of a precision part, the edge sharpness) can be effectively prevented from being included in the simplification range, the risk of feature distortion or distortion is reduced from the source, the error matrix of each triangular facet in the growing region is determined according to the geometric topology information, the sum of the quadratic errors of the associated triangular facets is calculated, the influence of the simplification operation on the local geometric feature can be accurately quantified through the fine measurement of the geometric error by the error matrix, the sum of the quadratic errors can truly reflect the geometric deviation possibly caused by the simplification, and reliable error judgment basis can be provided for the subsequent simplification operation; the vertex folding cost and the new vertex are determined based on the sum of the quadratic errors, and the edge folding cost and the priority sorting are determined accordingly, so that the edge (with the minimum folding cost) with the minimum influence on the visual and geometric feature of the model can be preferentially selected, the non-key geometric element can be preferentially processed in the simplification process, and the disturbance to the important geometric structure in the region is reduced; finally, the edge simplification folding is performed according to the edge folding cost after sorting, the simplification can be sequentially performed in the order from small to large error, the topological connectivity of the model is avoided from being damaged due to unordered folding, and the operation is performed only on the edges in the growing region, so that the geometric fidelity of the feature region is maximally preserved while the non-feature region is efficiently simplified, the cooperation of “feature preservation” and “efficient simplification” is finally realized, and the problems of easy loss of local feature and easy damage of topology in the existing global simplification method are solved, so as to improve the simplification effect.

[0033] Optionally, the edge simplification folding according to the edge folding cost after sorting obtains a simplified vertex and corresponding simplified geometric topology information, and the edge simplification folding comprises the following steps. Edge vertex data corresponding to the edge folding cost with the highest priority is extracted, and pre-simplification folding is performed according to the edge vertex data to generate a pre-simplified vertex. When the pre-simplified vertex coincides with the boundary vertex of the free selection region, the edge folding cost is re-determined and priority sorting is performed. When the pre-simplified vertex does not coincide with the boundary vertex of the free selection region, the pre-simplified vertex is taken as the simplified vertex, and the corresponding simplified geometric topology information is obtained.

[0034] Specifically, before the simplified folding is performed, a pre-simplified folding is performed, and it is determined whether the optimal position of the folded simplified vertex, i.e., the new vertex, coincides with the boundary vertex. If the optimal position coincides with the boundary vertex, the step of calculating the edge folding cost is performed again. If the optimal position does not coincide with the boundary vertex, the simplified folding is performed. Thus, the boundary information of the model is avoided, and the simplified model maintains the main shape of the original model and reduces unnecessary details, and the boundary problem caused by improper folding is avoided.

[0035] Optionally, the region growing algorithm is used to determine the growing region according to the free selection region and the geometric topology information of the three-dimensional mesh model, and the region growing algorithm includes: determining a simplified region boundary of the three-dimensional mesh model according to the free selection region; expanding the initial seed vertex and iteratively determining a final seed vertex and a corresponding adjacent vertex according to the initial seed vertex and the simplified region boundary based on a region growing criterion; determining an expanded vertex of the adjacent vertex according to the final seed vertex using a quadratic error metric algorithm; obtaining geometric topology information of all the final seed vertices, the adjacent vertices and the expanded vertices in the simplified region boundary to determine the growing region.

[0036] Specifically, the free selection region is as shown in Figure 4 The three-dimensional coordinates of each vertex in the free selection region can be read by a WindowButtonUpFcn() function, so that the free selection region obtained through human-computer interaction is converted into the simplified region boundary of the three-dimensional mesh model. Then, one or more initial seed vertices and the simplified region boundary obtained through human-computer interaction are used to expand the initial seed vertex based on the region growing criterion. The initial seed vertex is used as the starting point of the region growing algorithm. If the adjacent vertex of the initial seed vertex meets the region growing criterion, the adjacent vertex is added to the current region and is regarded as a new seed point for continuous checking until there is no adjacent vertex meeting the condition. At least one final seed vertex and a corresponding adjacent vertex are obtained. Each final seed vertex has a corresponding growing region, and the number of final seed vertices corresponds to the number of growing regions. Then, the final seed vertex is used to determine the expandable vertex in the adjacent vertex using the quadratic error metric algorithm, so as to determine the range of the growing region. Finally, the geometric topology information of all the final seed vertices, the adjacent vertices and the expanded vertices in the simplified region boundary is obtained to determine the information of the growing region. The spatial range of the growing region is determined according to the coordinates, edge data and triangular face data of the final seed vertex, the adjacent vertex and the expanded vertex. The range of the growing region is as shown in the dashed line, and the curvature and normal vector data are recorded in the growing region, i.e., the information of the growing region is counted, so as to facilitate subsequent simplification. Figure 5 The curvature and normal vector data are recorded in the growing region, i.e., the information of the growing region is counted, so as to facilitate subsequent simplification.

[0037] Optionally, the region growing criterion comprises a geometric similarity condition and a topological connectivity condition, and the expanding the initial seed vertex and iteratively determining the final seed vertex and the corresponding adjacent vertex based on the region growing criterion and according to the initial seed vertex and the simplified region boundary comprises: determining a growing 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 vertex based on the growing threshold and according to 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 vertex as the candidate adjacent vertex.

[0038] Specifically, the region growing criterion comprises a geometric similarity condition and a topological connectivity condition, the geometric similarity condition refers to setting a certain geometric similarity standard, such as a curvature deviation < 5% or a normal vector angle < 15°, as the basis for judging whether the adjacent vertex can be added to the current region, and the topological connectivity condition refers to considering the topological connection relationship between the vertices to be connected to ensure that the continuity of the model is not damaged in the expansion process. First, based on the geometric similarity condition and the topological connectivity condition, the growing threshold is determined through trial or experience, the growing threshold is mainly used to control the expansion standard of the region to ensure that only the adjacent vertex meeting the specific condition can be included in the current region, then the candidate adjacent vertex of the initial seed vertex is determined, and the geometric similarity data and the topological connectivity data of the candidate adjacent vertex are determined; finally, based on the growing threshold, the geometric similarity data and the topological connectivity data are used to judge whether the candidate adjacent vertex is included in the current region, if it is included in the current region, the candidate adjacent vertex is the final seed vertex, thereby determining the final seed vertex and the corresponding adjacent vertex, and iteratively determining all the final seed vertices by taking the adjacent vertex as the candidate adjacent vertex until there is no candidate adjacent vertex meeting the condition. The growing threshold is not a single numerical value, but a set of rule systems determined by multiple parameters, which allows flexible adjustment according to different model characteristics and application requirements. In actual application, it may be necessary to find the most suitable threshold combination for a specific task through multiple trials.

[0039] Exemplarily, the geometric similarity condition can comprise a gray difference, a color space Euclidean distance and a texture gradient difference, the growing threshold can comprise a corresponding gray difference threshold, a color space Euclidean distance threshold and a texture gradient difference threshold, and the gray difference, the color space Euclidean distance and the texture gradient difference of the candidate adjacent vertex can be determined by a gray difference formula, a color space Euclidean distance formula and a texture gradient difference formula respectively, and the gray difference formula comprises: ; wherein, is the gray value difference at point (p, q), and I is the gray value function.

[0040] The color space Euclidean distance formula includes: ; wherein, is the color space Euclidean distance at point (p, q), and R, G and B are color functions.

[0041] The texture gradient difference formula includes: ; wherein, is the color space Euclidean distance at point (p, q), is the texture gradient function.

[0042] Exemplarily, the iteration can be performed by an iteration formula, which includes: ; wherein, R t is the region at time t, R t+1 is the region at time t+1, N(R t ) is the set of neighboring pixels of the current region, is the average feature value of region R t , T is the growth threshold parameter, is the value corresponding to the geometric similarity condition, for example, the gray value difference, the color space Euclidean distance value or the texture gradient difference value.

[0043] Optionally, based on the region growing criterion, the initial seed vertex is expanded according to the initial seed vertex and the simplified region boundary, and the final seed vertex and the corresponding adjacent vertex are determined iteratively, and the method further includes: 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.

[0044] Specifically, in the process of region growing, the growth threshold can be dynamically adjusted according to the change of local features (such as encountering edges, sharp features, etc.). For example, when a significant feature change (such as a curvature transition exceeding 30%) is detected, the growth threshold can be reduced to capture these features more carefully; while in a relatively smooth area, the threshold can be relaxed to speed up the processing.

[0045] Optionally, before the region growing algorithm is used to determine the growth region according to the free selection region and the geometric topological information of the three-dimensional mesh model, the method further includes: Based on the continuity requirement, the free selection area is obtained according to the human-computer interaction instruction.

[0046] Specifically, the continuity requirement means that the vertices of the free selection area obtained through human-computer interaction instructions are continuous vertices in the three-dimensional network model.

[0047] Optionally, after performing edge simplification and folding according to the sorted edge folding costs to obtain simplified vertices and corresponding simplified geometric topology information, the method further includes: A simplified three-dimensional mesh model is drawn according to the simplified vertices and the simplified geometric topology information.

[0048] Specifically, the simplified vertices and simplified geometric topology information are stored in the smf file, and the simplified three-dimensional mesh model is drawn based on the simplified vertices and simplified geometric topology information, such as Figure 6 As shown, the part within the dotted line is the simplified part.

[0049] like Figure 2 As shown, an embodiment of the present invention provides a three-dimensional mesh model simplification device, comprising: A growing module, which is used to determine the growth region based on the freely selected region and geometric topology information of the 3D mesh model using a region growing algorithm; an error module, configured to determine an error matrix of each triangular facet in the growth region according to the geometric topology 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 based on the sum of the quadratic errors, and to determine an edge folding cost and prioritize the new vertex based on the new vertex; The simplification module is used to perform edge simplification and folding according to the sorted edge folding costs to obtain simplified vertices and corresponding simplified geometric topology information.

[0050] like Figure 3 As shown, an electronic device 300 provided by an embodiment of the present invention includes a memory 310 and a processor 320; the memory 310 is used to store computer programs; the processor 320 is used to implement the three-dimensional mesh model simplification method as described above when executing the computer program.

[0051] In other words, 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 executing the computer program: The region growing algorithm is used to determine the growth region based on the freely selected region and geometric topology information of the 3D mesh model; According to the geometric topology information, an error matrix of each triangular facet in the growth region is determined, and a sum of quadratic errors of all associated triangular facets is determined according to the error matrix; According to the sum of quadratic errors, a vertex collapse cost and a new vertex are determined, and an edge collapse cost is determined and prioritized according to the new vertex; An edge simplification collapse is performed according to the prioritized edge collapse cost, to obtain a simplified vertex and corresponding simplified geometric topology information.

[0052] The embodiment of the present application provides a computer readable storage medium, and the storage medium stores a computer program. When the computer program is executed by a processor, the three-dimensional mesh model simplification method is realized.

[0053] Alternatively, a non-volatile computer readable storage medium stores a computer program. When the computer program is executed by a processor, the processor performs the following operations: An area growing algorithm is used to determine a growth region according to a free selection area and geometric topology information of a three-dimensional mesh model; According to the geometric topology information, an error matrix of each triangular facet in the growth region is determined, and a sum of quadratic errors of all associated triangular facets is determined according to the error matrix; According to the sum of quadratic errors, a vertex collapse cost and a new vertex are determined, and an edge collapse cost is determined and prioritized according to the new vertex; An edge simplification collapse is performed according to the prioritized edge collapse cost, to obtain a simplified vertex and corresponding simplified geometric topology information.

[0054] An electronic device 300 that can be a server or a client of the present application will now be described, which is an example of a hardware device that can be applied to various aspects of the present application. The electronic device 300 is intended to represent various forms of digital electronic computer devices such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computer devices. The electronic device 300 can also represent various forms of mobile devices such as personal digital processing, cellular telephones, smart phones, wearable devices, and other similar computing devices. The components, their connections, and their functions, as shown in the figures and described herein, are meant only to be examples and are not intended to limit the implementations of the present application described and / or claimed in this document.

[0055] The electronic device 300 includes a computing unit that can perform various appropriate actions and processes in accordance with a computer program stored in a read-only memory (ROM) or a computer program loaded from a storage unit into a random access memory (RAM). In the RAM, various programs and data required for device operation can also be stored. The computing unit, the ROM, and the RAM are connected to each other through a bus. An input / output (I / O) interface is also connected to the bus.

[0056] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiments can be implemented by a computer program instructing related hardware, and the program can be stored in a computer readable storage medium. When the program is executed, the processes of the above-mentioned embodiments can be included. The storage medium can be a magnetic disc, an optical disc, a read-only memory (ROM), a random access memory (RAM), or the like. In this application, the units described as separate components can or can not be physically separated, and the components shown as units can or can not be physical units, that is, they can be located in one place or distributed on multiple network units. Part or all of the units can be selected to achieve the purpose of the embodiments of the present application according to actual needs. In addition, the functional units in each embodiment of the present application can be integrated in one processing unit, or each unit can exist physically, or two or more units can be integrated in one unit. The integrated unit can be implemented in the form of hardware or in the form of a software functional unit.

[0057] Although the present application is disclosed as above, the protection scope of the present application is not limited to this. Those skilled in the art can make various changes and modifications without departing from the spirit and scope of the present application, and these changes and modifications will fall within the protection scope of the present application.

Claims

1. A three-dimensional mesh model simplification method, characterized in that: include: The region growing algorithm is used to determine the growth region based on the freely selected region and geometric topology information of the 3D mesh model; Determining an error matrix for each triangular facet in the growth region based on the geometric topology information, and determining a sum of quadratic errors of all associated triangular facets based on the error matrix; Determine the vertex folding cost and the new vertex according to the sum of the quadratic errors, and determine the edge folding cost and prioritize the new vertex according to the new vertex; Edge simplification and folding are performed according to the sorted edge folding costs to obtain simplified vertices and corresponding simplified geometric topology information.

2. The three-dimensional mesh model simplification method according to claim 1, characterized in that: The edge simplification and folding is performed according to the sorted edge folding costs to obtain simplified vertices and corresponding simplified geometric topology information, including: Extracting edge vertex data corresponding to the edge folding cost with the highest priority, performing pre-simplification folding based on the edge vertex data, and generating pre-simplified vertices; When the pre-simplified vertex coincides with a boundary vertex of the free selection area, re-determining the edge collapse cost and performing priority sorting; When the pre-simplified vertex does not coincide with a boundary vertex of the free selection area, the pre-simplified vertex is used as the simplified vertex, and the corresponding simplified geometric topology information is obtained.

3. The three-dimensional mesh model simplification method according to claim 1, characterized in that: The region growing algorithm is used to determine the growing region based on the freely selected region and geometric topology information of the three-dimensional grid model, including: Determining a simplified region boundary of the three-dimensional grid model according to the freely selectable region; Based on a region growing criterion, according to the initial seed vertex and the simplified region boundary, the initial seed vertex is expanded, and a final seed vertex and a corresponding adjacent vertex are iteratively determined; According to the final seed vertex, a quadratic error metric algorithm is used to determine an extended vertex of the adjacent vertex; The geometric topological information of all the final seed vertices, the adjacent vertices, and the expanded vertices within the boundary of the simplified region is obtained to determine the growth region.

4. The three-dimensional mesh model simplification method according to claim 3, characterized in that: The region growing criterion includes a geometric similarity condition and a topological connectivity condition. The region growing criterion-based method, according to the initial seed vertex and the simplified region boundary, expands the initial seed vertex, and iteratively determines the final seed vertex and the corresponding adjacent vertices, includes: Determining a growth threshold based on the geometric similarity condition and the topological connectivity condition; Determining candidate adjacent vertices of the initial seed vertex, and determining geometric similarity data and topological connectivity data of the candidate adjacent vertices; Based on the growth threshold, the final seed vertex and the corresponding adjacent vertices are determined according to the geometric similarity data and the topological connectivity data, and the adjacent vertices are used as the candidate adjacent vertices. The step of determining the geometric similarity data and topological connectivity data of the candidate adjacent vertices is returned to iteratively determine all the final seed vertices.

5. The three-dimensional mesh model simplification method according to claim 4, characterized in that: The method further includes: expanding the initial seed vertex based on the region growing criterion and the simplified region boundary, and iteratively determining the final seed vertex and the corresponding adjacent vertices. 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.

6. The three-dimensional mesh model simplification method according to claim 1, characterized in that: Before determining the growing region based on the freely selected region and geometric topology information of the three-dimensional mesh model using the region growing algorithm, the method further includes: Based on the continuity requirement, the free selection area is obtained according to the human-computer interaction instruction.

7. The three-dimensional mesh model simplification method according to claim 1, characterized in that: After performing edge simplification and folding according to the sorted edge folding costs to obtain simplified vertices and corresponding simplified geometric topology information, the method further includes: A simplified three-dimensional mesh model is drawn according to the simplified vertices and the simplified geometric topology information.

8. A three-dimensional mesh model simplification device, characterized in that: include: A growing module, which is used to determine the growth region based on the freely selected region and geometric topology information of the 3D mesh model using a region growing algorithm; an error module, configured to determine an error matrix of each triangular facet in the growth region according to the geometric topology 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 based on the sum of the quadratic errors, and to determine an edge folding cost and prioritize the new vertex based on the new vertex; The simplification module is used to perform edge simplification and folding according to the sorted edge folding costs to obtain simplified vertices and corresponding simplified geometric topology information.

9. An electronic device, characterized in that: including memory and processor; The memory is used to store computer programs; The processor is configured to implement the three-dimensional mesh model simplification method according to any one of claims 1 to 7 when executing the computer program.

10. A computer-readable storage medium, characterized in that The storage medium stores a computer program, and when the computer program is executed by the processor, the three-dimensional mesh model simplification method according to any one of claims 1 to 7 is implemented.

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