A virtual reality level simulation method based on material point method and gridding surface reconstruction

Abstract

A virtual reality level simulation method based on material point method and gridding surface reconstruction belongs to the cross field of computer graphics, physical simulation and virtual reality technology. The steps are as follows: S1, storing the material point position coordinates according to the material point material number, and obtaining the bounding box boundary of the material point by traversing the coordinates; S2, defining and selecting the grid size and boundary extension distance for reconstructing the surface, and generating the grid; S3, selecting a Gaussian function to calculate the contribution of each material point to the overall material point density field, and obtaining the parameters; S4, calculating the contribution of each material point to the overall material point density field, and accumulating to obtain the overall material point density field; S5, obtaining the isosurface of the overall material point density field, and exporting it as a general three-dimensional model of a rendering engine to obtain a simulation visual effect. The method can solve the problems that the prior art cannot process ten million material points, the reconstruction parameter selection is difficult, and the connection with a high-end rendering engine is not smooth, and realizes a virtual reality level simulation visual effect.

Description

Technical Field

[0001] This invention belongs to the interdisciplinary field of computer graphics, physical simulation and virtual reality technology. It relates to a virtual reality-level simulation method based on the material point method and meshed surface reconstruction, and more particularly to a method for efficiently reconstructing surfaces and generating virtual reality-level rendering effects for ultra-large-scale material point method (MPM) simulation data. Background Technology

[0002] The Material Point Method (MPM) has become an important tool for simulating complex physical phenomena due to its advantages in handling extreme deformation, material fracture, and flow. However, the output of MPM is a massive number of disordered discrete material points. Transforming these into continuous geometric surfaces that can be used for high-quality rendering and real-time interaction is a huge challenge. Especially in modern simulations, the number of material points often reaches tens or even hundreds of millions. Traditional surface reconstruction methods (such as global Marching-Cubes) face serious performance bottlenecks, and the computational speed significantly hinders the progress of scientific research.

[0003] Meanwhile, the post-processing presentation methods of scientific computing results generally suffer from insufficient expressiveness. Traditional pseudo-color cloud maps or coarse grid models are too abstract and bland, making it difficult to connect with the everyday visual experiences of non-professional audiences such as the public and policymakers. This hinders their intuitive understanding of complex phenomena such as geological disasters and fluid movements, severely impeding the effective dissemination of scientific knowledge. Furthermore, in critical scenarios such as project presentations, this lack of visual impact fails to match the depth of cutting-edge computing itself, significantly diminishing the value and influence of research results.

[0004] Therefore, the results of scientific computing urgently need to be enhanced through more advanced visualization techniques, transforming them from abstract numerical data into concrete, high-fidelity, and interactive visual presentations. This will not only break down cognitive barriers and improve the effectiveness of science popularization and the impact of research findings, but also provide researchers with immersive, real-time interactive exploration capabilities, thereby stimulating deeper insights into scientific principles.

[0005] For example, Chinese invention patent CN201710716840.1 discloses a visualization method for SPH particle closed surfaces based on triangulation algorithm. This visualization method performs meshing and rendering of discrete particle results from the smooth particle method, but lacks support for surface reconstruction of discrete point data of MPM. Chinese invention patent CN201810311430.3 discloses a multi-scale debris flow simulation method based on material point method. This material point method simulation method uses an octree space algorithm to capture the fluid surface, but the density field kernel function calculation process still faces the problems of kernel function parameter selection and large computational cost.

[0006] Existing technologies attempt to visualize MPM results, but several issues remain. First, directly reconstructing the global surface of a point cloud with tens of millions of points incurs enormous computational and memory demands. Second, the reconstruction quality is highly dependent on the selection of field functions (such as kernel functions) and the setting of their parameters (such as smoothing radius). However, a universal and automated method for parameter determination is currently lacking, leading to unstable reconstruction quality and the potential for holes or over-smoothing, further impacting the realism of the visual effects. Furthermore, the generated mesh models often lack seamless data interfaces with professional rendering engines (such as Unreal Engine and Unity-HDRP), making it difficult to leverage the advanced rendering features of modern GPUs, such as real-time ray tracing and nano-fiber rendering, to achieve high-fidelity, photorealistic visual effects. This results in the presentation of scientific computing results failing to align with current cutting-edge graphics technologies.

[0007] There is an urgent need for a surface reconstruction method that can efficiently process ultra-large-scale MPM data, automatically optimize reconstruction parameters, and efficiently integrate with professional rendering engines. Therefore, this invention innovatively realizes a complete framework from particle-based algorithm (including MPM) results data to virtual reality-level rendering effects. Through spatial optimization algorithms and kernel function parameter selection algorithms, it achieves efficient and high-fidelity automated generation of physically reliable material surfaces, providing a brand-new solution for scientific research results demonstration. Summary of the Invention

[0008] To address the shortcomings of existing large-scale MPM data rendering methods, this invention provides a virtual reality-level simulation method based on the material point method and meshed surface reconstruction. This method aims to solve the problems of existing technologies being unable to handle tens of millions of material points, difficulties in selecting reconstruction parameters, and poor integration with high-end rendering engines, ultimately achieving virtual reality-level simulation visual effects.

[0009] To achieve the above objectives, the technical solution adopted by the present invention is as follows:

[0010] A virtual reality-level simulation method based on the material point method and meshed surface reconstruction includes the following steps:

[0011] S1: Read the material point location coordinates and material numbers from the material point method calculation results, and classify and store the material point location coordinates according to the material number; traverse the read material point location coordinates to obtain the bounding box boundary of the material points; specifically:

[0012] S1.1, Read in the coordinates of the material point location, and simultaneously read in the material number;

[0013] S1.2: Based on S1.1, obtain the material number, classify and store the coordinates of the material points of different materials for later separation of different materials;

[0014] S1.3, Traverse the coordinates of all material points and record the material points. Minimum value of position coordinates in three directions , , and maximum value , , This yields the bounding box boundary of the matter points.

[0015] S2, based on the bounding box boundary of the material points obtained in S1, customize the mesh size and boundary extension distance for reconstructing the surface; generate a mesh based on the obtained mesh size and boundary extension distance; specifically:

[0016] S2.1, Custom Grid Size , The size can be selected according to the required rendering accuracy.

[0017] S2.2, Custom mesh boundary extension distance The boundary extension distance will extend the mesh outward by a certain distance based on the boundary of the material point bounding box, so as to leave a margin in the kernel function calculation region of the material points near the boundary; the range of the outward extension distance is set to 3 to 5 times the custom mesh size;

[0018] S2.3, the bounding box boundary of the material points obtained from S1.3, and the mesh size obtained from S2.1 and S2.2. and boundary extension distance A mesh is created, and the formula for calculating the boundary coordinates of the mesh is as follows:

[0019] (1)

[0020] (2)

[0021] (3)

[0022] (4)

[0023] (5)

[0024] (6)

[0025] In the formula, , , and , , These represent the minimum and maximum boundary values ​​in the three directions of the mesh; (brackets) This indicates rounding down to the nearest integer less than the value within the parentheses; the parentheses... This indicates taking the smallest integer greater than the value inside the parentheses, i.e., rounding up; , , and , , It is the boundary of the bounding box of the material points obtained in S1.3; It is the mesh size defined in S2.1; It is the boundary extension distance defined in S2.2.

[0026] S3, based on the mesh obtained in S2, selects a Gaussian function as the kernel function to calculate the contribution of each material point to the overall material point density field. The parameters of the kernel function are obtained using a calculation method related to the mesh size; specifically:

[0027] S3.1, the contribution of each material point in the mesh is calculated using the Gaussian function as the kernel function. The calculation formula is as follows:

[0028] (7)

[0029] (8)

[0030] In the formula, Represents grid nodes The place received the first The density contribution value of each material point; e is the natural base; It is a scaling factor that affects the steepness of the kernel function; It is the grid node to the first The distance between each material point is calculated using formula (8); , , Represents the three directional coordinates of a grid node; , , Indicates the first The three directional coordinates of a material point;

[0031] S3.2, Formula (9) provides the scaling factor. Calculation method:

[0032] (9)

[0033] In the formula, It is the mesh size defined in S2.1; This represents the kernel function value at a location 4 times the grid size away from a material point. It can be customized during the calculation process, and the value range is 0.1 to 0.5.

[0034] S4, based on the kernel function parameters obtained in S3, uses the kernel function and spatial optimization algorithm to calculate the contribution of each material point to the overall material point density field; the contributions of each material point to the overall material point density field are accumulated to obtain the overall material point density field; specifically:

[0035] S4.1, Custom kernel function affects distance This makes the computational range of the kernel function extend from the positive to the negative direction of the grid where the material point is located. Extending in all directions distance, The value should be 3 to 5 times the grid size. The kernel function calculation in formula (7) is performed only when the kernel function is the first kernel function. Centered on the grid containing the material point, with a side length of [missing information] Calculate within the cube region;

[0036] S4.2, traverse all material points, and sum the kernel functions using formula (10) to obtain the overall material point density field:

[0037] (10)

[0038] In the formula, It is a grid node The sum of contributions from the kernel functions of all matter points, i.e., the overall matter point density field; Formula (7) for the first A material point at a grid node Kernel function contribution at the location; This indicates that all material points are in the grid nodes. Sum the contribution values; This represents the first step in the process of traversing the matter points. A point of matter.

[0039] S5, based on the overall material point density field obtained in S4, uses the moving cube algorithm to obtain the isosurface of the overall material point density field. The obtained isosurface is exported as a general 3D model for the rendering engine. This general 3D model is then imported into the rendering engine for post-processing to obtain virtual reality-level simulation visual effects. Specifically:

[0040] S5.1 Obtain the isosurface according to the custom isosurface values. The calculation formula is as follows:

[0041] (11)

[0042] In the formula, It is the overall material point density field obtained in S4.2; T represents the value at the density field isosurface, and the recommended value range is 1~4;

[0043] S5.2, use the moving cube method to find the isosurface of the overall material point density field, and obtain the vertex coordinates and triangle vertex number information of the isosurface.

[0044] S5.3 takes the isosurface information obtained in S5.2 and writes it out as a 3D model file in a common format for the rendering engine. It is then imported into the rendering engine for scene setting and result rendering to obtain virtual reality-level simulation visual effects.

[0045] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0046] (1) This invention realizes an automated process from material point method calculation data to virtual reality level rendering;

[0047] (2) This invention applies a space optimization algorithm to the process of kernel function space sampling, which significantly reduces the amount of computation and greatly shortens the computation time;

[0048] (3) The kernel function parameter selection standard adopted in this invention can automatically generate high-quality isosurfaces for users, reducing the debugging workload in the parameter selection process.

[0049] In summary, this invention employs a kernel function parameter selection criterion coupled with mesh size and a spatial optimization algorithm to achieve efficient creation of continuous surfaces from discrete material point data. Through the automated framework of reading material point data, processing spatial density functions, and writing a general 3D model, researchers can quickly obtain virtual reality-level rendering effects. Attached Figure Description

[0050] Figure 1 This is a schematic diagram of the main process of the method of the present invention;

[0051] Figure 2 A schematic diagram of the discrete point cloud of material points for a water tank baffle example;

[0052] Figure 3 A schematic diagram of the material point boundary for the water tank baffle example;

[0053] Figure 4 This is a schematic diagram of the mesh generation for a water tank baffle example.

[0054] Figure 5 This is a schematic diagram of the kernel function;

[0055] Figure 6 Kernel function kurtosis parameter Select a standard schematic diagram;

[0056] Figure 7 A schematic diagram of the sampling range of a single material point in the water tank baffle example;

[0057] Figure 8A schematic diagram of the continuous triangular mesh surface extracted from the density field for the water tank baffle example;

[0058] Figure 9 A schematic diagram of importing the water tank baffle simulation model into the rendering engine;

[0059] Figure 10 A virtual reality-level effect diagram for the water tank baffle at time 0.01;

[0060] Figure 11 A virtual reality-level effect diagram for the water tank baffle at time 0.02;

[0061] Figure 12 A virtual reality-level effect diagram for the water tank baffle at time 0.04;

[0062] Figure 13 A virtual reality-level effect diagram for the water tank baffle at time 0.06;

[0063] Figure 14 A virtual reality-level effect diagram for the water tank baffle at time 0.08;

[0064] Figure 15 This is a schematic diagram of the virtual reality-level effect of the water tank baffle at time 0.10. Detailed Implementation

[0065] The present invention will be further described below with reference to the accompanying drawings and specific embodiments, but the scope of protection of the present invention is not limited thereto.

[0066] See Figure 1 A virtual reality-level simulation method based on the material point method and meshed surface reconstruction includes the following steps:

[0067] S1: Read the material point position coordinates and material numbers of the material point calculation results data, and classify and store the material point position coordinates according to the material point material number; traverse the read material point position coordinates to obtain the bounding box boundary of the material points.

[0068] S2: Based on the bounding box boundary of the material points obtained in S1, customize the mesh size and boundary extension distance for reconstructing the surface; generate the mesh based on the obtained mesh size and boundary extension distance;

[0069] S3. Based on the grid obtained in S2, the Gaussian function is selected as the kernel function for subsequent calculation of the contribution of each material point to the overall material point density field. The parameters of the kernel function are obtained by a calculation method related to the grid size.

[0070] S4. Based on the kernel function parameters obtained in S3, the contribution of each material point to the overall material point density field is calculated using the kernel function and spatial optimization algorithm; the contribution of each material point to the overall material point density field is accumulated to obtain the overall material point density field.

[0071] S5. Based on the overall material point density field obtained in S4, the moving cube algorithm is used to obtain the isosurface of the overall material point density field. The isosurface of the overall material point density field is exported as a general 3D model for the rendering engine. The general 3D model is imported into the rendering engine for post-processing to obtain virtual reality-level simulation visual effects.

[0072] Example 1: Rendering of data on the impact of a water tank dam failure on a baffle plate;

[0073] S1: Read the material point location coordinates and material numbers from the material point method calculation results, and classify and store the material point location coordinates according to the material number; traverse the read material point location coordinates to obtain the bounding box boundary of the material points; specifically:

[0074] S1.1, This embodiment includes two materials: water and a rubber baffle. The water contains approximately 50,000 substance points, and the plastic baffle contains approximately 800 substance points. The distribution of the substance points in this embodiment is as follows: Figure 2 ;

[0075] S1.2, Based on the material number obtained in S1.1, the coordinates of the material point positions of different materials are classified and stored for later separation of different materials; at the same time, the data of each time step also needs to be stored separately. There are a total of 131 time steps in this embodiment.

[0076] S1.3, Traverse the coordinates of all material points and record the material points. Minimum value of position coordinates in three directions , , and maximum value , , This yields the bounding box boundary of the matter point. This step can be omitted if the boundary is known, although it may reduce the computational scope. In this embodiment, the minimum value of the matter point position coordinates is... , , and maximum value , , The bounding box boundary of the matter points is obtained, with reference to... Figure 3 .

[0077] S2, based on the bounding box boundary of the material points obtained in S1, customize the mesh size and boundary extension distance for reconstructing the surface; generate a mesh based on the obtained mesh size and boundary extension distance; specifically:

[0078] S2.1, Custom Grid Size , The mesh size can be selected according to the desired rendering accuracy. The mesh size affects the size of the triangles that make up the isosurface, thus affecting the smoothness of the isosurface. A smaller mesh size will also increase computational overhead. The mesh size selected in this embodiment is... ;

[0079] S2.2, Custom mesh boundary extension distance The boundary extension distance extends the mesh outward by a certain distance beyond the bounding box boundary of the matter points, allowing sufficient margin for the kernel function calculation region of matter points near the boundary. The boundary extension distance in this embodiment... That is, twice the grid size;

[0080] S2.3, the bounding box boundary of the material points obtained from S1.3, and the mesh size obtained from S2.1 and S2.2. and boundary extension distance A mesh is created. In this embodiment, the boundary extension distance of the mesh is twice the mesh size, i.e. The calculated mesh boundary ensures that there are at least two meshes between a material point near the boundary and the mesh boundary itself; the formula for calculating the mesh boundary coordinates is as follows:

[0081] (1)

[0082] (2)

[0083] (3)

[0084] (4)

[0085] (5)

[0086] (6)

[0087] In the formula, , , and , , These represent the minimum and maximum boundary values ​​in the three directions of the mesh; (brackets) This indicates rounding down to the nearest integer less than the value within the parentheses; the parentheses... This indicates taking the smallest integer greater than the value inside the parentheses, i.e., rounding up; , , and , , It is the boundary of the bounding box of the material points obtained in S1.3; It is the mesh size defined in S2.1; It is the boundary extension distance defined in S2.2.

[0088] In this embodiment, the grid boundaries are respectively , , and , , The generated mesh reference Figure 4 .

[0089] S3, based on the mesh obtained in S2, selects a Gaussian function as the kernel function for subsequent calculations of the contribution of each material point to the overall material point density field. The parameters of the kernel function are obtained using a calculation method related to the mesh size; specifically:

[0090] S3.1, the Gaussian function is used as the kernel function to calculate the contribution of each material point in the grid. The two-dimensional contour plot of the Gaussian function is shown in the figure. Figure 5 The calculation formula is as follows:

[0091] (7)

[0092] (8)

[0093] In the formula, Represents grid nodes The place received the first The density contribution value of each material point; e is the natural base; It is a scaling factor that affects the steepness of the kernel function; It is the grid node to the first The distance between the material points is calculated using formula (8). , , Represents the three directional coordinates of the grid node. , , Indicates the first The three directional coordinates of a material point;

[0094] S3.2, Formula (9) provides The calculation method, Figure 6The formula (9) is shown Selection criteria:

[0095] (9)

[0096] In the formula, It is the mesh size defined in S2.1; This represents the kernel function value at a location 4 times the mesh size from a material point. In this embodiment, we take... .

[0097] S4, based on the kernel function parameters obtained in S3, uses the kernel function and spatial optimization algorithm to calculate the contribution of each material point to the overall material point density field; the contributions of each material point to the overall material point density field are accumulated to obtain the overall material point density field; specifically:

[0098] S4.1, Custom kernel function affects distance This makes the computational range of the kernel function extend from the positive to the negative direction of the grid where the material point is located. Extending in all directions distance, The value is 4 times the grid size. The kernel function calculation in formula (7) is performed only when the kernel function is the first kernel function. Centered on the grid containing the material point, with a side length of [missing information] The calculation is performed within a cubic region. The calculation range for the kernel function of a material point is referenced. Figure 7 ;

[0099] S4.2, traverse all material points, and sum the kernel functions using formula (10) to obtain the overall material point density field:

[0100] (10)

[0101] In the formula, It is a grid node The sum of contributions from the kernel functions of all matter points, i.e., the overall matter point density field; Formula (7) for the first A material point at a grid node Kernel function contribution at the location; This indicates that all material points are in the grid nodes. Sum the contribution values; This represents the first step in the process of traversing the matter points. A point of matter.

[0102] S5, based on the overall material point density field obtained in S4, uses the moving cube algorithm to obtain the isosurface of the overall material point density field. The obtained isosurface is exported as a general 3D model for the rendering engine. This general 3D model is then imported into the rendering engine for post-processing to obtain virtual reality-level simulation visual effects. Specifically:

[0103] S5.1 Obtain the isosurface according to the custom isosurface values. The calculation formula is as follows:

[0104] (11)

[0105] In the formula, This is the overall material point density field obtained in S4.2; T represents the value at the density field isosurface, which is taken in this embodiment. ;

[0106] S5.2, use the moving cube method to find the isosurfaces of the overall material point density field, obtain the vertex coordinates and triangle vertex numbers of the isosurfaces, and refer to the obtained isosurfaces. Figure 8 ;

[0107] S5.3, the isosurface information obtained in S5.2 is written out as a 3D model file in the rendering engine's common format, imported into the rendering engine for scene setting and rendering, to obtain virtual reality-level simulation visual effects, referencing... Figures 10 to 15 .

[0108] The above embodiments are merely illustrative of the implementation methods of the present invention, but should not be construed as limiting the scope of the present invention. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the present invention, and these modifications and improvements all fall within the protection scope of the present invention.

Claims

1. A virtual reality-level simulation method based on the material point method and meshed surface reconstruction, characterized in that, The virtual reality-level simulation method includes the following steps: S1: Read the material point position coordinates and material numbers of the material point calculation results data, and classify and store the material point position coordinates according to the material number of the material point; traverse the read material point position coordinates to obtain the bounding box boundary of the material point. S2: Based on the bounding box boundary of the material points obtained in S1, customize the mesh size and boundary extension distance for reconstructing the surface; generate the mesh based on the obtained mesh size and boundary extension distance; S3. Based on the grid obtained in S2, the Gaussian function is selected as the kernel function to calculate the contribution of each material point to the overall material point density field. The parameters of the kernel function are obtained by a calculation method related to the grid size. S4. Based on the kernel function parameters obtained in S3, the contribution of each material point to the overall material point density field is calculated using the kernel function and spatial optimization algorithm; the contribution of each material point to the overall material point density field is accumulated to obtain the overall material point density field. S5. Based on the overall material point density field obtained in S4, the moving cube algorithm is used to obtain the isosurface of the overall material point density field. The isosurface of the overall material point density field is exported as a general 3D model for the rendering engine. The general 3D model is imported into the rendering engine for post-processing to obtain virtual reality-level simulation visual effects.

2. The virtual reality simulation method based on the material point method and meshed surface reconstruction according to claim 1, characterized in that, Specifically, S1 refers to: S1.1, Read in the coordinates of the material point location, and simultaneously read in the material number; S1.2: Based on S1.1, obtain the material number, classify and store the coordinates of the material points of different materials for later separation of different materials; S1.3, Traverse the coordinates of all material points and record the material points. Minimum value of position coordinates in three directions , , and maximum value , , This yields the bounding box boundary of the matter points.

3. The virtual reality simulation method based on the material point method and meshed surface reconstruction according to claim 2, characterized in that, Specifically, S2 is: S2.1, Custom Grid Size , The size is determined based on the desired rendering accuracy requirements; S2.2, Custom mesh boundary extension distance The boundary extension distance will extend the mesh outward by a certain distance based on the boundary of the material point bounding box, so as to leave a margin in the kernel function calculation region of the material points near the boundary. S2.3, the bounding box boundary of the material points obtained from S1.3, and the mesh size obtained from S2.1 and S2.

2. and boundary extension distance A mesh is created, and the formula for calculating the boundary coordinates of the mesh is as follows: （1） （2） （3） （4） （5） （6） In the formula, , , and , , These represent the minimum and maximum boundary values ​​in the three directions of the mesh; (brackets) This indicates rounding down to the nearest integer less than the value within the parentheses; the parentheses... This indicates taking the smallest integer greater than the value inside the parentheses, i.e., rounding up; , , and , , It is the boundary of the bounding box of the material points obtained in S1.3; It is the mesh size defined in S2.1; It is the boundary extension distance defined in S2.

2.

4. The virtual reality simulation method based on the material point method and meshed surface reconstruction according to claim 3, characterized in that, In S2.2, the outward expansion of a certain distance range is set to 3 to 5 times the custom grid size.

5. The virtual reality simulation method based on the material point method and meshed surface reconstruction according to claim 3, characterized in that, Specifically, S3 is: S3.1, the contribution of each material point in the mesh is calculated using the Gaussian function as the kernel function. The calculation formula is as follows: （7） （8） In the formula, Represents grid nodes The place received the first The density contribution value of each material point; e is the natural base; It is a scaling factor that affects the steepness of the kernel function; It is the grid node to the first The distance between each material point is calculated using formula (8); , , Represents the three directional coordinates of a grid node; , , Indicates the first The three directional coordinates of a material point; S3.2, Formula (9) provides the scaling factor. Calculation method: （9） In the formula, d is the grid size defined in S2.1; C represents the kernel function value at a position 4 times the grid size away from a material point, which can be customized during the calculation process.

6. The virtual reality-level simulation method based on the material point method and meshed surface reconstruction according to claim 5, characterized in that, In S3.2, the kernel function value C ranges from 0.1 to 0.

5.

7. A virtual reality-level simulation method based on the material point method and meshed surface reconstruction according to claim 5, characterized in that, Specifically, S4 is: S4.1, Custom kernel function affects distance This makes the computational range of the kernel function extend from the grid containing the material point in both positive and negative directions. Extending in all directions The distance, the kernel function calculation of formula (7) is only performed on the distance of the first digit. Centered on the grid containing the material point, with a side length of [missing information] Calculate within the cube region; S4.2, traverse all material points and sum the kernel functions using formula (10) to obtain the overall material point density field: （10） In the formula, It is a grid node The sum of contributions from the kernel functions of all matter points, i.e., the overall matter point density field; Formula (7) for the first A material point at a grid node Kernel function contribution at the location; This indicates that all material points are in the grid nodes. Sum the contribution values; This represents the first step in the process of traversing the matter points. A point of matter.

8. The virtual reality-level simulation method based on the material point method and meshed surface reconstruction according to claim 7, characterized in that, In S4.1, The value should be 3 to 5 times the grid size. .

9. A virtual reality-level simulation method based on the material point method and meshed surface reconstruction according to claim 7, characterized in that, Specifically, S5 is: S5.1 Obtain the isosurface according to the custom isosurface values. The calculation formula is as follows: （11） In the formula, It is the overall matter point density field obtained in S4.2; T represents the value at the density field isosurface; S5.2, use the moving cube method to find the isosurface of the overall material point density field, and obtain the vertex coordinates and triangle vertex number information of the isosurface; S5.3 takes the isosurface information obtained in S5.2 and writes it out as a 3D model file in a common format for the rendering engine. It is then imported into the rendering engine for scene setting and result rendering to obtain virtual reality-level simulation visual effects.

10. A virtual reality-level simulation method based on the material point method and meshed surface reconstruction according to claim 9, characterized in that, In S5.1, the recommended value range for T is 1 to 4.

Images