Smooth optimization method for large-scale scene triangular patch model

Abstract

The invention discloses a triangular patch smoothing optimization method for a large-scale scene, and the method comprises the following steps: S1, coding and storing all points, lines and planes; s2, calculating protrusion indexes of the points; s3, inputting optimization thresholds a and b; s4, determining all points needing to be optimized; s5, combining and optimizing the neighborhoods of the salient points; s6, performing optimization calculation; and S7, obtaining an optimization result. By calculating and optimizing the triangular patch model, smoothing processing of the abrupt area of the model is realized, so that after the model is optimized, abnormal point surface protrusions of the model are reduced, patch redundancy is reduced, a better display effect is kept, the operation performance of the model is greatly improved, and effect display and smoothness of live-action three-dimensional rendering and demonstration are ensured.

Description

Technical Field

[0001] The present invention relates to the field of optimization of triangular facet models in three-dimensional models, and in particular to a smoothing optimization method for triangular facet models of large-scale scenes. Background Art

[0002] In various real-life 3D systems, low-quality models will cause model distortion and fail to reflect exquisite details, greatly reducing the model experience. High-quality model rendering and demonstration will cause excessive computer resource usage, resulting in screen freezes, frame drops, and even crashes. If you want to achieve high-quality models without freezes in 3D scenes, you can't have your cake and eat it too.

[0003] The current treatment method generally uses two sets of models.

[0004] A low-quality model reduces the number of triangular facets in the model, reduces resource usage, and enables smooth browsing and operation of triangular facet models. A high-quality model increases the number of facets and improves the quality of the model. There are certain limitations. The model triangular facets cannot be simplified and refined at the same time, and the refinement and simplification operations cannot be implemented on different details of a model at the same time, which greatly reduces the display effect and application experience.

[0005] Patent document number CN110322558A discloses a mesh model simplification method based on the fireworks algorithm triangle patch folding. This application is aimed at the high shape-preserving triangle mesh simplification requirements, especially the three-dimensional model with obvious holes and boundaries, which can well guarantee the simplified shape. The fireworks algorithm and the triangle folding method are effectively combined to accelerate the simplification rate, control the simplification error, and achieve a balance between the simplification speed and the simplification quality. However, it also has the problem that while the triangle patch model is lightweight, the model distortion is poorly controlled, and it is not easy to obtain a better display effect and running performance. Summary of the invention

[0006] In order to solve the existing problems, the present invention provides a triangular patch smoothing optimization method for large-scale scenes, and the specific scheme is as follows:

[0007] A triangular patch smoothing optimization method for large-scale scenes includes the following steps:

[0008] S1, encode and store all points, lines and surfaces;

[0009] S2, calculation of the protrusion index of all points;

[0010] S3, input optimization thresholds a and b;

[0011] S4, determine all points that need to be optimized;

[0012] S5, merge and optimize the neighborhood of the protrusion point;

[0013] S6, optimization calculation;

[0014] S7, get the optimization result, dynamically adjust the optimization effect by adjusting the input thresholds a and b to meet the requirements, iterate the secondary topology optimization as needed, and repeat steps S2-S7.

[0015] Preferably, step S2 specifically includes the following steps:

[0016] S21, calculate the normal vector of the surface surrounding the point;

[0017] S22, calculating the normal vectors between adjacent faces of the face surrounding the point;

[0018] S23, calculating the angle between two surface normal vectors;

[0019] S24, normalized angle;

[0020] S25, calculate the area of ​​the triangle;

[0021] S26, calculation of protrusion index;

[0022] S27, calculating the area and protrusion index of each face;

[0023] S28, to find the total protrusion index;

[0024] S29, find the total area of ​​adjacent faces of a point;

[0025] S210, calculating the normalized neurite index.

[0026] Preferably, step S6 specifically includes the following steps:

[0027] S61, optimizing the face patch, connecting the non-adjacent vertices of the co-side triangles of the selected point and the adjacent line segments of the non-selected points of the adjacent triangles;

[0028] S62, reducing the protrusion height of the protrusion point, specifically: first calculate the average deviation of each axis of the protrusion point, then determine the deviation, and finally adjust the abnormal value.

[0029] The present invention discloses a computer-readable storage medium, on which a computer program is stored. After the computer program is run, any of the above-mentioned methods is executed.

[0030] The present invention discloses a computer system, including a processor and a storage medium. The storage medium stores a computer program. The processor reads and runs the computer program from the storage medium to execute any of the methods described above.

[0031] The beneficial effects of the present invention are:

[0032] Flexible control parameters: The introduction of two thresholds a and b provides flexibility for different types of model optimization, which can be appropriately adjusted according to the needs of the model to obtain different degrees of smoothness and planarization.

[0033] Effectiveness of the protrusion index: The protrusion index weighted by area is used to measure the "protrusion" of a point. This method not only considers the angle between the patches, but also adds the area weight, which can more accurately reflect the local geometry of the point.

[0034] It is scientific to calculate the protrusion index using face weighting, especially in 3D surface analysis. The weighted product of the area of ​​the face and the normal angle not only takes into account the angle change, but also measures its contribution to the overall protrusion degree based on the actual area of ​​the face. This method can more accurately reflect the protrusion degree of the point and avoid misjudging a single face with a large angle as a protrusion point. Therefore, this method helps to identify local protrusion features more stably.

[0035] Layered processing:

[0036] Less than threshold a: Reduce the number of patches to simplify the model. Reducing the number of patches in smooth areas (flatter, less prominent areas) can save computing resources.

[0037] Between a and b: No processing is done. The protrusions in these areas are moderate. Preserving the patches can avoid over-simplification or distortion of the local model.

[0038] Greater than threshold b: Smoothing. For areas with significant protrusions, topological patches help optimize the model, making the protrusions more natural and smoother.

[0039] Local area grouping: When processing more complex geometric shapes, different thresholds can be set for different areas of the model, especially for large-scale or complex-shaped models, so that local protrusion processing can be more finely controlled.

[0040] Multiple iterations of optimization: After adding or subtracting patches, you can repeatedly calculate the protrusion index and iterate to observe the convergence of the model. Through multiple rounds of adjustments, the model can achieve more refined smooth optimization.

[0041] Adaptive threshold: The thresholds a and b can be dynamically adjusted according to the local characteristics of the model or the global average protrusion index, so that the smoothing and protrusion processing can better adapt to the overall structure of the model. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0043] Figure 1 is a flowchart of the method of the present invention;

[0044] Figure 2 This is the original model and point numbering diagram;

[0045] Figure 3 To optimize the front normal map;

[0046] Figure 4 This is a schematic diagram of the faces ranked high in protrusion index;

[0047] Figure 5 Schematic diagram of single point neighborhood determination;

[0048] Figure 6 Determining schematic diagrams for multiple point neighborhoods;

[0049] Figure 7 (a) Schematic topology of S6.1 before optimization;

[0050] Figure 7(b) S6.1 real topology before optimization;

[0051] Figure 7(c) Schematic topology after optimization of S6.1;

[0052] Figure 7(d) The actual topology after optimization of S6.1;

[0053] Figure 8 S6.1 optimized normal map;

[0054] Fig. 9 S6.2 optimized normal map;

[0055] Fig.10 Final optimization effect comparison chart. DETAILED DESCRIPTION

[0056] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0057] The present invention uses the normal angle of the triangle face in the 3D model to determine whether the model needs to be simplified or re-topologically optimized, and achieves the effect of simplification or topological optimization for different areas of the model, thereby achieving the effect of intelligent optimization of the model. It is applied to the 3D model scene to achieve the effect of occupying the minimum resources and the best display effect.

[0058] like Figure 1 , a triangular patch smoothing optimization method for large-scale scenes, comprising the following steps:

[0059] S1, encode all points, lines and surfaces and store them. Figure 2 The point code is encoded in the form of P+serial number, such as P1; the line segment code is L+two endpoint numbers, such as L_P1P2; the surface number is F+three corner point numbers, such as F_P1P2P18.

[0060] S2, calculate the protrusion index of all points. Specifically, it includes the following steps:

[0061] S21, calculate the normal vector of the surface around the point, such as Figure 3 As shown, each blue line is the normal vector of each face. First, obtain the edge vector of the triangle around each point. Given the coordinates (xyz) of each point in the triangle, we can obtain the line vector by subtracting the point coordinates. Take the F_P49P53P54 triangle around point P53 as an example. 49 ,P 53 ,P 54 The three points are F_P49P53P54 vertices, and the three points P 49 (x 49 ,y 49 ,z 49 ), P 53 (x 53 ,y 53 ,z 53 ), P 54 (x 54 ,y 54 ,z 54 ) can be obtained by subtracting the coordinates to get the following vectors:

[0062] vector

[0063] vector

[0064] vector

[0065] Through the vector and The cross product can be used to find the normal vector of this face F_P49P53P54 for:

[0066]

[0067] S22, calculate the normal vectors between the adjacent faces of the point. Take the F_P49P53P54 face as an example, F_P49P56P54 is one of the adjacent faces, and the F_P49P56P54 vector is calculated as

[0068] P 49 , P 54 , P 56 The shared vertices of two adjacent triangles

[0069] vector

[0070] vector

[0071] Normal vector is the cross product of the two vectors above:

[0072]

[0073] S23, calculating the angle between the two surface normal vectors.

[0074] Using the vector dot product formula:

[0075]

[0076] in, is the dot product between the normal vectors, and is the magnitude (length) of the normal vector.

[0077] S24, normalized angle. The angle θ ranges from 0° (completely coplanar) to 180° (completely opposite), and is normalized using a sine function, with the normalized value = sinθ, representing the degree of protrusion of the surface.

[0078] When θ = 90°, the normalized value is 1, which means the two surfaces are perpendicular, and when θ = 0° or θ = 180°, the normalized value is 0, which means the two surfaces are parallel.

[0079] S25, calculate the area of ​​the triangle. The area of ​​triangle F_P49P53P54 can be calculated by vector cross product, defining two edge vectors:

[0080] vector

[0081] vector

[0082] Calculate the cross product

[0083] The magnitude of the normal vector is twice the area of ​​the triangle, so the area of ​​the triangle is A F_P49P53P54 for

[0084] S26, calculating the protrusion index. The protrusion index of the triangle is obtained by multiplying the normalized angle value by the area of ​​the triangle. F_P49P53P54 ·sinθ, the larger the protrusion index I is, the more significant the protrusion of the patch is; the larger the area or the more significant the angle of the patch, the greater its contribution to the overall protrusion index. Figure 4 As shown, the obviously protruding blue surface is the surface with the highest calculated protrusion index.

[0085] If the protrusion point has five adjacent triangle patches, each patch F i The protrusion index I i Now that the calculation is completed, we can use the following steps to get the normalized protrusion index.

[0086] S27, calculating the area and protrusion index of each face. i The area is denoted as A i , the protrusion index is recorded as I i , that is: I i =A i sinθ i

[0087] Among them, sinθ i It is a patch F i The normalized angle value of .

[0088] S28, calculate the total protrusion index.

[0089] Sum the protrusion indices of all patches:

[0090]

[0091] S29, calculate the total area of ​​adjacent faces of a point.

[0092] Add up the areas of the five patches to get the total area:

[0093]

[0094] S210, calculating the normalized neurite index.

[0095] The final normalized protrusion index is:

[0096] Normalized neurite index The larger the index, the more prominent the point is.

[0097] S3, input optimization thresholds a and b.

[0098] Specifically, when 0 < a < b < 1, the protruding points with a normalized protrusion index less than a are smooth enough and can be classified as Q points, and the number of patches can be appropriately reduced. The points with a normalized protrusion index greater than a and less than b do not need to be processed. The points with a normalized protrusion index greater than b and less than 1 are classified as W points and require re-topological optimization.

[0099] The present invention introduces two thresholds a and b, which provides flexibility for optimizing different types of models and can be appropriately adjusted according to the requirements of the model to obtain different smoothness and planarization degrees.

[0100] S4. Determine all the points that need to be optimized.

[0101] Specifically, the points that need to be optimized can be determined according to step S3. It is divided into two parts. The first part is abbreviated as Q, which is the protruding points with a number less than a and requires reducing the number of patches. The second part is abbreviated as W, which is the points with a normalized protrusion index greater than b and less than 1 and requires re-topological optimization.

[0102] The optimization of each point will affect the protrusion index of adjacent points. Therefore, it is more effective to avoid global processing during the optimization process and instead adopt a strategy of local and batch-by-batch optimization. The specific method is to first process the points whose protrusion index exceeds the threshold, and gradually optimize them according to a certain area division, and try to perform the optimization on the points outside the previous batch of optimized areas. This can reduce the fluctuation of the protrusion index of adjacent points caused by local modification, make each step of the optimization more accurate and reduce the chain effect. Process W first and then Q.

[0103] Determine the points that need topological optimization for Q. Each point that needs topological optimization has an optimization neighborhood (the determination rule is shown in S4.1). If there are multiple optimized topological points within the range of this optimization neighborhood, only the point with the largest protrusion index is optimized.

[0104] As Figure 5 For the determination graph of the neighborhood of the protruding point, the determination rule of the optimization neighborhood is as follows:

[0105] The determination rule of the neighborhood of the protruding point is to select the adjacent triangles of the first point and the co-edge triangles of the non-selected points adjacent to the adjacent triangles of the selected point as the optimization area of this point.

[0106] S5. Merge and optimize the neighborhood of the protruding point.

[0107] Specifically, for multiple points in the area to be optimized of a certain protruding point, these points are all merged into the range of the point with the largest protrusion index, and only the point with the largest protrusion index needs to be optimized. Other points within the optimization topology range of the point with the largest protrusion index do not need to be separately processed for topological optimization.

[0108] As Figure 6As shown in FIG. 1 , it is a neighborhood diagram of multiple protruding points. The areas enclosed by different circles represent the areas to be optimized at different points. The optimized neighborhoods cannot have overlapping areas.

[0109] By identifying the local neighborhood of each protrusion point and processing the point with the highest protrusion index, the model can be gradually optimized, while retaining the stability of adjacent points to the greatest extent, ensuring the smoothness of the optimization process.

[0110] S6, optimization calculation.

[0111] The Q-type point optimization is completed according to the optimization method of Chinese patent CN118135170A.

[0112] The W-type points are optimized in two steps. The first step is to optimize the surface patch, and the second step is to reduce the protruding height of the protruding points. This achieves the effect of topological optimization and reducing the protruding degree of the points.

[0113] S61, the first optimization step - optimizing the facets, connecting the non-adjacent vertices of the co-lateral triangles of the selected points and the adjacent line segments of the non-selected points of the adjacent triangles.

[0114] In this case, connect P53P56, P53P55, P53P41, P53P40, and P53P50. And form a new triangle face, delete the original triangle faces F_P49P53P54, F_P53P54P43, F_P52P53P43, F_P51P52P53, F_P49P51P53, F_P49P54P56, F_P43P54P55, F_P43P42P52, F_P40P51P52, and F_P49P50P51, ten faces. Form a new face structure. Reduce the normal angle and make it smoother. The effect is shown in the four figures in Figure 7 (i.e., Figure 7 (a) schematic topology before optimization, Figure 7 (b) real topology before optimization, Figure 7 (c) schematic topology after optimization, and Figure 7 (d) real topology after optimization). The final optimization effect and the normal direction of each surface are shown in Figure 8 shown.

[0115] S62, the second optimization step is to reduce the protruding height of the protruding point.

[0116] Calculate the average deviation of each axis of the protrusion point: take the neighborhood point set S = {P1, P2, ..., Pn} around the protrusion point P, and calculate the neighborhood average values ​​of the x, y, and z axes respectively

[0117]

[0118] For example, in case P53, the set of surrounding neighborhood points is S P53 = {P 51 , P 52 , P 49, P 54 , P 43}

[0119]

[0120] Determine the deviation: Calculate the deviation value of point p on each coordinate axis:

[0121]

[0122] The coordinate axis corresponding to the maximum deviation value is selected as the abnormal axis.

[0123] For example, in case P53, the deviation value

[0124]

[0125] Outlier adjustment: If Δx is the maximum deviation value, the updated coordinates are:

[0126]

[0127] For example, in case P53, the calculated z value is an abnormal special value, so the z value coordinate is reduced.

[0128]

[0129] The same method is used for the x and y coordinates. After adjustment, the point is more closely aligned with the surrounding points, reducing the protrusion, optimizing the effect and the normal direction of each surface. Fig. 9 shown.

[0130] S7, get the optimization result, adjust the input thresholds a and b, dynamically adjust the optimization effect to meet the requirements, iterate the secondary topology optimization as needed, and repeat steps S2-S7. The execution effect is as follows Fig.10 ,As shown in the optimization effect diagram, from left to right, the protrusions become smoother and smoother.

[0131] The present invention achieves smoothing of the abrupt areas of the model by optimizing the calculation of the triangular face model. Therefore, after the model is optimized, the protrusions of the model's abnormal points and faces are reduced, the redundancy of the facets is reduced, and a better display effect is maintained. The running performance of the model is greatly improved, ensuring the effect display and fluency of real-scene three-dimensional rendering and demonstration.

[0132] The present invention also has the following beneficial effects:

[0133] Flexible control parameters: The introduction of two thresholds a and b provides flexibility for different types of model optimization, which can be appropriately adjusted according to the needs of the model to obtain different degrees of smoothness and planarization.

[0134] Effectiveness of the protrusion index: The protrusion index weighted by area is used to measure the "protrusion" of a point. This method not only considers the angle between the patches, but also adds the area weight, which can more accurately reflect the local geometry of the point.

[0135] It is scientific to calculate the protrusion index using face weighting, especially in 3D surface analysis. The weighted product of the area of ​​the face and the normal angle not only takes into account the angle change, but also measures its contribution to the overall protrusion degree based on the actual area of ​​the face. This method can more accurately reflect the protrusion degree of the point and avoid misjudging a single face with a large angle as a protrusion point. Therefore, this method helps to identify local protrusion features more stably.

[0136] Layered processing:

[0137] Less than threshold a: Reduce the number of patches to simplify the model. Reducing the number of patches in smooth areas (flatter, less prominent areas) can save computing resources.

[0138] Between a and b: No processing is done. The protrusions in these areas are moderate. Preserving the patches can avoid over-simplification or distortion of the local model.

[0139] Greater than threshold b: Smoothing. For areas with significant protrusions, topological patches help optimize the model, making the protrusions more natural and smooth.

[0140] Local area grouping: When processing more complex geometric shapes, different thresholds can be set for different areas of the model, especially for large-scale or complex-shaped models, so that local protrusion processing can be more finely controlled.

[0141] Multiple iterations of optimization: After adding or subtracting patches, you can repeatedly calculate the protrusion index and iterate to observe the convergence of the model. Through multiple rounds of adjustments, the model can achieve more refined smooth optimization.

[0142] Adaptive threshold: The thresholds a and b can be dynamically adjusted according to the local characteristics of the model or the global average protrusion index, so that the smoothing and protrusion processing can better adapt to the overall structure of the model.

[0143] The present invention discloses a computer-readable storage medium and a computer system, wherein a computer-readable storage medium stores a computer program, and after the computer program is run, any of the above methods is executed. A computer system includes a processor and a storage medium, wherein the storage medium stores a computer program, and the processor reads and runs the computer program from the storage medium to execute any of the above methods.

[0144] Those skilled in the art will further appreciate that the various illustrative logic blocks, modules, circuits, and algorithm steps described in conjunction with the embodiments disclosed herein may be implemented as electronic hardware, computer software, or a combination of the two. To clearly illustrate this interchangeability of hardware and software, various illustrative components, boxes, modules, circuits, and steps are generally described above in terms of their functionality. Whether such functionality is implemented as hardware or software depends on the specific application and the design constraints imposed on the overall system. Technicians may implement the described functionality in different ways for each specific application, but such implementation decisions should not be interpreted as resulting in a departure from the scope of the present invention.

[0145] The various illustrative logic blocks, modules, and circuits described in conjunction with the embodiments disclosed herein may be implemented or performed with a general purpose processor, a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic device, discrete gate or transistor logic, discrete hardware components, or any combination thereof designed to perform the functions described herein. A general purpose processor may be a microprocessor, but in the alternative, the processor may be any conventional processor, controller, microcontroller, or state machine. The processor may also be implemented as a combination of computing devices, such as a combination of a DSP and a microprocessor, a plurality of microprocessors, one or more microprocessors in cooperation with a DSP core, or any other such configuration.

[0146] The steps of the method or algorithm described in conjunction with the embodiments disclosed herein may be embodied directly in hardware, in a software module executed by a processor, or in a combination of the two. The software module may reside in a RAM memory, a flash memory, a ROM memory, an EPROM memory, an EEPROM memory, a register, a hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art. An exemplary storage medium is coupled to a processor so that the processor can read and write information from / to the storage medium. In an alternative, a storage medium may be integrated into a processor. The processor and the storage medium may reside in an ASIC. The ASIC may reside in a user terminal. In an alternative, the processor and the storage medium may reside in a user terminal as discrete components.

[0147] In one or more exemplary embodiments, the functions described may be implemented in hardware, software, firmware, or any combination thereof. If implemented as a computer program product in software, each function may be stored on or transmitted by a computer-readable medium as one or more instructions or codes. Computer-readable media include both computer storage media and communication media, including any medium that facilitates the transfer of a computer program from one place to another. Storage media may be any available medium that can be accessed by a computer. As an example and not limitation, such a computer-readable medium may include RAM, ROM, EEPROM, CD-ROM or other optical disk storage, disk storage or other magnetic storage device, or any other medium that can be used to carry or store the desired program code in the form of an instruction or data structure and can be accessed by a computer. Any connection is also properly referred to as a computer-readable medium. For example, if the software is transmitted from a website, a server, or other remote source using a coaxial cable, a fiber optic cable, a twisted pair, a digital subscriber line (DSL), or wireless technologies such as infrared, radio, and microwaves, the coaxial cable, fiber optic cable, twisted pair, DSL, or wireless technologies such as infrared, radio, and microwaves are included in the definition of the medium. Disk and disc as used herein include compact disc (CD), laser disc, optical disc, digital versatile disc (DVD), floppy disk and Blu-ray disc, wherein disk often reproduces data magnetically, while disc reproduces data optically with lasers. Combinations of the above should also be included within the scope of computer-readable media.

[0148] The previous description of the disclosure is provided to enable any person skilled in the art to make or use the disclosure. Various modifications to the disclosure will be apparent to those skilled in the art, and the general principles defined herein may be applied to other variations without departing from the spirit or scope of the disclosure. Thus, the disclosure is not intended to be limited to the examples and designs described herein, but should be granted the widest scope consistent with the principles and novel features disclosed herein.

[0149] Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent substitutions for some of the technical features therein; and these modifications or substitutions do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A triangular patch smoothing optimization method for large-scale scenes, characterized in that: The following steps are involved: S1, encode and store all points, lines and surfaces; S2, calculation of the protrusion index of all points; S3, input optimization thresholds a and b; S4, determine all points that need to be optimized; S5, merge and optimize the neighborhood of the protrusion point; S6, optimization calculation; S7, get the optimization result, dynamically adjust the optimization effect by adjusting the input thresholds a and b to meet the requirements, iterate the secondary topology optimization as needed, and repeat steps S2-S7.

2. The method according to claim 1, characterized in that Step S2 specifically includes the following steps: S21, calculate the normal vector of the surface surrounding the point; S22, calculating the normal vectors between adjacent faces of the face surrounding the point; S23, calculating the angle between two surface normal vectors; S24, normalized angle; S25, calculate the area of ​​the triangle; S26, calculation of protrusion index; S27, calculating the area and protrusion index of each face; S28, to find the total protrusion index; S29, find the total area of ​​adjacent faces of a point; S210, calculating the normalized neurite index.

3. The method according to claim 1, characterized in that Step S6 specifically includes the following steps: S61, optimizing the face patch, connecting the non-adjacent vertices of the co-side triangles of the selected point and the adjacent line segments of the non-selected points of the adjacent triangles; S62, reducing the protrusion height of the protrusion point, specifically: first calculate the average deviation of each axis of the protrusion point, then determine the deviation, and finally adjust the abnormal value.

4. A computer-readable storage medium, characterized in that: A computer program is stored on the medium, and after the computer program is run, the method according to any one of claims 1 to 3 is executed.

5. A computer system, characterized in that: The method comprises a processor and a storage medium, wherein a computer program is stored in the storage medium, and the processor reads and runs the computer program from the storage medium to execute the method as claimed in any one of claims 1 to 3.

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