Texture mapping method and device based on geometrical morphology

Abstract

The invention relates to the technical field of three-dimensional modeling, and discloses a texture mapping method and device based on geometric morphology, and the method comprises the steps: carrying out the data analysis of the geometric morphology of an initial three-dimensional model, and obtaining a plurality of geometric features of each surface point; dividing texture grades based on the plurality of geometric features of each surface point to obtain a plurality of texture grade areas; generating a target texture based on the plurality of geometric features of each surface point; based on the texture grade area to which each surface point belongs, mapping the target texture to the initial three-dimensional model to obtain a hybrid three-dimensional model; and based on the plurality of geometric features of each surface point, adjusting the texture of the hybrid three-dimensional model to obtain a target three-dimensional model. Texture mapping is carried out based on the geometric features of the model surface, compared with manual mapping, it is ensured that the texture of each area is highly matched with the geometric features, texture distortion and distortion are avoided, the detail expressive force and the visual effect are improved, and meanwhile the automation degree and the working efficiency of the texture mapping process are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of three-dimensional modeling, and in particular to a texture mapping method and device based on geometric morphology. Background Art

[0002] Texture mapping is a key technology for achieving surface detail in 3D visualization models in fields such as game development, film and television special effects, and architectural visualization. Existing texture mapping techniques typically involve designers manually mapping 2D texture images onto the model surface to add detail, color, and texture. However, this method often results in texture distortion and distortion when working with complex or irregular models. Furthermore, for large projects or complex models, the manual mapping process is tedious, time-consuming, and prone to errors. Summary of the Invention

[0003] In view of this, the present invention provides a texture mapping method and device based on geometric morphology to solve the problems of deformation and distortion of texture caused by manual mapping in the prior art, and the manual mapping process is cumbersome, time-consuming and error-prone.

[0004] In a first aspect, the present invention provides a texture mapping method based on geometric morphology, the method comprising:

[0005] Performing geometric data analysis on the initial three-dimensional model to obtain a plurality of geometric features corresponding to each surface point on the model surface of the initial three-dimensional model;

[0006] Based on multiple geometric features corresponding to each surface point, the texture level of the model surface of the initial three-dimensional model is divided to obtain corresponding multiple texture level areas;

[0007] Generate target texture based on multiple geometric features corresponding to each surface point;

[0008] Based on the texture level region to which each surface point belongs, the target texture is mapped to the initial 3D model to obtain a hybrid 3D model;

[0009] Based on multiple geometric features corresponding to each surface point, the texture of the hybrid 3D model is adjusted to obtain the target 3D model.

[0010] The geometric morphology-based texture mapping method provided by the embodiment of the present invention captures subtle changes in the model surface by extracting the geometric feature information of each surface point, divides the model surface into different texture level areas based on the extracted geometric features, and generates a suitable texture based on the geometric features of each surface point. The generated texture is accurately mapped back to the corresponding position of the three-dimensional model according to the divided texture level areas, and the mapped texture is further adjusted according to the geometric features, thereby improving the fit between the texture and the geometric features, enhancing the realism of the final model, and making the final generated target three-dimensional model have a highly realistic visual effect. By performing texture mapping based on the geometric features of the model surface, compared to manual mapping, it ensures that the texture of each area is highly matched with its geometric features, avoids texture distortion and distortion, improves detail expression and visual effects, and reduces the need for manual adjustment, thereby improving the degree of automation and work efficiency of the entire texture mapping process.

[0011] In an optional embodiment, before performing geometric data analysis on the initial three-dimensional model to obtain a plurality of geometric features corresponding to each surface point on the model surface of the initial three-dimensional model, the method further includes:

[0012] Perform polygon simplification on the imported 3D model to obtain a simplified 3D model;

[0013] Detecting and filling holes in the simplified three-dimensional model to obtain a filled three-dimensional model;

[0014] The non-manifold edges of the filled 3D model are detected and repaired to obtain the initial 3D model.

[0015] The geometry-based texture mapping method provided in the embodiment of the present invention pre-processes the imported three-dimensional model before performing geometric feature analysis to improve the model quality and the accuracy of subsequent analysis.

[0016] In an optional embodiment, the plurality of geometric features include Gaussian curvature and mean curvature;

[0017] Performing geometric data analysis on the initial three-dimensional model to obtain multiple geometric features corresponding to each surface point on the model surface of the initial three-dimensional model, including:

[0018] For each surface point, obtain the neighborhood point set and the neighborhood triangle interior angle set of the surface point;

[0019] Calculate the Voronoi area of a surface point based on its neighborhood point set;

[0020] Determine the Gaussian curvature of a surface point based on the set of interior angles of its neighborhood triangles and the Voronoi area;

[0021] The mean curvature of a surface point is determined based on the set of neighborhood points, the set of interior angles of neighborhood triangles, and the Voronoi area of the surface point.

[0022] The geometry-based texture mapping method provided by the embodiment of the present invention can accurately describe the curvature of the model surface by calculating the curvature of each surface point, and achieve accurate estimation of the curvature by combining the discrete difference method and Voronoi area calculation.

[0023] In an alternative embodiment, the plurality of geometric features include concavity and convexity;

[0024] Performing geometric data analysis on the initial three-dimensional model to obtain multiple geometric features corresponding to each surface point on the model surface of the initial three-dimensional model, including:

[0025] Determine the viewpoint location;

[0026] For each surface point, determine the viewpoint vector of the surface point based on the surface point and the viewpoint position;

[0027] For each adjacent facet of the surface point, determine a normal vector of the adjacent facet based on the coordinates of each vertex of the adjacent facet;

[0028] Take the weighted average of the normal vectors of all adjacent patches of the surface point to obtain the normal of the surface point;

[0029] The dot product of the normal of the surface point and the viewpoint vector is used as the concavity of the surface point.

[0030] The geometry-based texture mapping method provided by the embodiment of the present invention can accurately describe the degree of concavity or convexity of the model surface relative to the viewpoint by calculating the concavity and convexity of each surface point, thereby helping to achieve natural texture mapping.

[0031] In an optional embodiment, the texture level of the model surface of the initial three-dimensional model is divided based on multiple geometric features corresponding to each surface point to obtain corresponding multiple texture level areas, including:

[0032] Determine the weight coefficients corresponding to Gaussian curvature, mean curvature and concavity respectively;

[0033] For each surface point, a detail weight of the surface point is determined based on multiple geometric features corresponding to the surface point and their weight coefficients;

[0034] Determining a texture level to which the detail weight belongs based on a preset interval to which the detail weight of each surface point belongs;

[0035] Based on the texture level corresponding to the detail weight of each surface point, the model surface of the initial three-dimensional model is divided into multiple texture level areas.

[0036] The geometric morphology-based texture mapping method provided in an embodiment of the present invention divides the texture level of the model surface into different levels by analyzing the curvature and convexity, thereby obtaining multiple corresponding texture level areas, so as to realize refined texture mapping in different texture level areas.

[0037] In an optional embodiment, generating a target texture based on a plurality of geometric features corresponding to each surface point includes:

[0038] Based on the detail weight of each surface point, the reference amplitude and reference frequency of the noise corresponding to each surface point are adjusted to obtain the target amplitude and target frequency corresponding to each surface point;

[0039] A procedural texture algorithm is used to generate target texture based on the target amplitude and target frequency corresponding to each surface point.

[0040] The geometry-based texture mapping method provided by the embodiment of the present invention considers the influence of geometric features in the process of generating texture, so that the generated target texture is more consistent with the three-dimensional model.

[0041] In an optional embodiment, based on the texture level region to which each surface point belongs, mapping the target texture to the initial 3D model to obtain a hybrid 3D model includes:

[0042] For each surface point, determining a first texture resolution corresponding to the surface point in the target texture based on the texture level region to which the surface point belongs;

[0043] determining all adjacent texture coordinates of the surface point based on the texture coordinate corresponding to the surface point in the target texture;

[0044] determining a second texture resolution for each adjacent texture coordinate based on the texture level region to which each adjacent texture coordinate belongs;

[0045] determining a target texture resolution for the surface point based on the first texture resolution, each second texture resolution, and a detail weight of the surface point;

[0046] The resolution of each surface point in the target texture is replaced with the corresponding target texture resolution, and the target texture after the texture resolution is replaced is mapped to the initial three-dimensional model to obtain a hybrid three-dimensional model.

[0047] The geometric morphology-based texture mapping method provided by an embodiment of the present invention considers the texture coordinates of the current surface point and the adjacent texture coordinates of the texture coordinate during texture mapping, and obtains the accurate target texture resolution of each surface point based on the texture resolutions corresponding to these two texture coordinates, thereby adjusting the resolution of each surface point in the target texture to improve the texture effect.

[0048] In an optional embodiment, determining a target texture resolution of a surface point based on the first texture resolution, each second texture resolution, and a detail weight of the surface point includes:

[0049] When all second texture resolutions are consistent with the first texture resolution, determining a target texture resolution of the surface point based on the first texture resolution and the detail weight of the surface point; or

[0050] In a case where any second texture resolution is inconsistent with the first texture resolution, a target texture resolution of the surface point is determined based on the first texture resolution, the second texture resolution and the detail weight of the surface point.

[0051] The geometric morphology-based texture mapping method provided by the embodiment of the present invention ensures seamless splicing of textures by adopting different ways of determining texture resolutions based on whether texture coordinates are at texture boundaries.

[0052] In an optional embodiment, adjusting the texture of the hybrid 3D model based on multiple geometric features corresponding to each surface point to obtain a target 3D model includes:

[0053] For each first target point in the hybrid three-dimensional model whose Gaussian curvature is greater than a preset threshold, determining a target texture roughness of the first target point based on the Gaussian curvature of the first target point, a reference texture roughness, and a preset roughness adjustment coefficient;

[0054] For each second target point in the hybrid three-dimensional model whose concavity and convexity are within the preset adjustment range, determining a target texture brightness and a target texture color of the second target point based on the reference texture brightness, the reference texture color, the concavity and convexity, the preset brightness adjustment coefficient, and the preset color adjustment coefficient;

[0055] A spatial interpolation algorithm is used to adjust the texture roughness of all first target points in the hybrid three-dimensional model in the target texture to the target texture roughness, and to adjust the texture brightness and texture color of all second target points in the hybrid three-dimensional model in the target texture to the target texture brightness and target texture color, thereby obtaining a target three-dimensional model.

[0056] The geometric morphology-based texture mapping method provided in an embodiment of the present invention adjusts the texture based on geometric features so that the adjusted texture fits the model better and gives the model a more realistic visual effect. At the same time, compared with manual adjustment, the degree of automation and efficiency are improved.

[0057] In a second aspect, the present invention provides a texture mapping device based on geometric morphology, the device comprising:

[0058] An analysis module is used to perform geometric data analysis on the initial three-dimensional model to obtain a plurality of geometric features corresponding to each surface point on the model surface of the initial three-dimensional model;

[0059] A partitioning module, configured to partition the surface texture of the initial three-dimensional model based on a plurality of geometric features corresponding to each surface point, to obtain a plurality of corresponding texture level regions;

[0060] A generation module, for generating a target texture based on multiple geometric features corresponding to each surface point;

[0061] A mapping module, configured to map the target texture to the initial three-dimensional model based on the texture level region to which each surface point belongs, to obtain a hybrid three-dimensional model;

[0062] The adjustment module is used to adjust the texture of the hybrid three-dimensional model based on multiple geometric features corresponding to each surface point to obtain a target three-dimensional model.

[0063] In a third aspect, the present invention provides a computer device comprising: a memory and a processor, the memory and the processor being communicatively connected to each other, the memory storing computer instructions, and the processor executing the geometric-based texture mapping method of the first aspect or any corresponding embodiment thereof by executing the computer instructions.

[0064] In a fourth aspect, the present invention provides a computer-readable storage medium having computer instructions stored thereon, the computer instructions being used to enable a computer to execute the geometry-based texture mapping method of the first aspect or any corresponding embodiment thereof.

[0065] In a fifth aspect, the present invention provides a computer program product comprising computer instructions for causing a computer to execute the geometry-based texture mapping method of the first aspect or any corresponding embodiment thereof. BRIEF DESCRIPTION OF THE DRAWINGS

[0066] In order to more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the specific 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 any creative work.

[0067] Figure 1 is a flow chart of a geometry-based texture mapping method according to an embodiment of the present invention;

[0068] Figure 2 is a structural block diagram of a texture mapping device based on geometric morphology according to an embodiment of the present invention;

[0069] Figure 3 Schematic diagram of the hardware structure of a computer device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0070] To make the purpose, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying 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 those skilled in the art without making creative efforts shall fall within the scope of protection of the present invention.

[0071] Existing manual texture mapping techniques often cause texture deformation and distortion when processing complex or irregular models. Furthermore, the manual mapping process is cumbersome, time-consuming, and prone to errors. The geometric morphology-based texture mapping method provided by the present invention performs texture mapping based on the geometric features of the model surface. Compared to manual mapping, this method ensures that the texture of each area closely matches its geometric features, avoiding texture distortion and distortion, improving detail expression and visual effects, while reducing the need for manual adjustments and increasing the automation and efficiency of the entire texture mapping process.

[0072] According to an embodiment of the present invention, an embodiment of a texture mapping method based on geometry is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0073] In this embodiment, a texture mapping method based on geometric morphology is provided, which can be used in terminals such as computers. Figure 1 is a flow chart of a texture mapping method based on geometric morphology according to an embodiment of the present invention, such as Figure 1 As shown, the process includes the following steps:

[0074] Step S101 performs geometric data analysis on the initial 3D model to obtain multiple geometric features corresponding to each surface point on the model surface of the initial 3D model. Specifically, a surface point refers to a point located on the model surface of the 3D model. By performing geometric data analysis on the initial 3D model, the geometric features of each surface point are obtained, which can reflect the surface details of the 3D model. This surface detail can then be fully considered during subsequent texture mapping, thereby improving the model's texture rendering.

[0075] In step S102, the surface texture of the initial 3D model is divided into different texture levels based on the multiple geometric features corresponding to each surface point, obtaining corresponding multiple texture level regions. Specifically, the model surface is divided into different texture levels and corresponding regions based on the geometric features of the surface points. Regions with higher texture levels require richer texture details. Therefore, in subsequent texture mapping, the texture details required by regions with different texture levels are mapped accordingly. This optimizes the use of computing resources while ensuring that the texture details of regions with different texture levels match the actual geometric features, improving the consistency and realism of the overall visual effect.

[0076] In step S103, a target texture is generated based on the multiple geometric features corresponding to each surface point. Specifically, the geometric details of the model surface are taken into account when generating the target texture, so that the generated target texture is more closely aligned with the model surface, texture distortion is avoided, and the texture effect of the model is improved.

[0077] In step S104, based on the texture level region to which each surface point belongs, the target texture is mapped onto the initial 3D model to obtain a hybrid 3D model. Specifically, when mapping the target texture onto the initial 3D model, the texture resolution corresponding to each surface point is adjusted accordingly to meet the texture detail requirements of different texture level regions, thereby improving the texture effect while reducing resource consumption.

[0078] In step S105, the texture of the hybrid 3D model is adjusted based on the multiple geometric features corresponding to each surface point to obtain the target 3D model. Specifically, the current texture effect is further adjusted based on the geometric features of each surface point to avoid texture distortion or distortion, and compared to manual adjustment, this improves work efficiency.

[0079] The geometric morphology-based texture mapping method provided by the embodiment of the present invention captures subtle changes in the model surface by extracting the geometric feature information of each surface point, divides the model surface into different texture level areas based on the extracted geometric features, and generates a suitable texture based on the geometric features of each surface point. The generated texture is accurately mapped back to the corresponding position of the three-dimensional model according to the divided texture level areas, and the mapped texture is further adjusted according to the geometric features, thereby improving the fit between the texture and the geometric features, enhancing the realism of the final model, and making the final generated target three-dimensional model have a highly realistic visual effect. By performing texture mapping based on the geometric features of the model surface, compared to manual mapping, it ensures that the texture of each area is highly matched with its geometric features, avoids texture distortion and distortion, improves detail expression and visual effects, and reduces the need for manual adjustment, thereby improving the degree of automation and work efficiency of the entire texture mapping process.

[0080] In this embodiment, a texture mapping method based on geometric morphology is provided, which can be used in the above-mentioned terminal. The method specifically includes the following steps:

[0081] Step S201 , polygon simplification is performed on the imported 3D model to obtain a simplified 3D model. Specifically, a QEM (Quadric Error Metrics) algorithm is used to simplify the mesh of the imported 3D model, reduce redundant polygons, and maintain the shape characteristics of the model.

[0082] Step S202: Detect holes in the simplified three-dimensional model and fill them to obtain a filled three-dimensional model. Specifically, a hole filling algorithm is used to detect and fill holes in the model to ensure the integrity of the mesh.

[0083] Step S203 detects and repairs non-manifold edges in the padded 3D model to obtain the initial 3D model. Specifically, non-manifold edges are detected and repaired to avoid anomalies in subsequent calculations. Preprocessing the imported 3D model by mesh simplification, hole repair, and non-manifold edge repair improves model quality and the accuracy of subsequent analysis.

[0084] Step S204 , performing geometric data analysis on the initial three-dimensional model to obtain a plurality of geometric features corresponding to each surface point on the model surface of the initial three-dimensional model, wherein the plurality of geometric features include Gaussian curvature, mean curvature, and concavity.

[0085] Specifically, the above step S204 includes:

[0086] Step S2041: For each surface point, obtain the neighborhood point set and the neighborhood triangle interior angle set. Specifically, for each surface point, its neighborhood points are the other surface points directly adjacent to the surface point, and these surface points are combined into a neighborhood point set. Assuming that the 3D model is composed of multiple triangular patches, and each triangular patch includes multiple surface points, the other triangular patches directly adjacent to the triangular patch containing the surface point are considered neighborhood triangular patches, and the interior angles of these neighborhood triangular patches are combined into a neighborhood triangle interior angle set.

[0087] Step S2042: Calculate the Voronoi area of the surface point based on the neighborhood point set of the surface point. Specifically, for each surface point, calculate the Voronoi area of the surface point using the following formula (1).

[0088]

[0089] Among them, A vRepresents the Voronoi area of a surface point; V represents a surface point; i, j represent the index of the surface point; m represents the number of neighboring points in the neighborhood point set; Represented by the surface point V i and surface point V j The edges formed; α j ,β j Represents the edge Two connected corners.

[0090] Step S2043: Determine the Gaussian curvature of the surface point based on the set of interior angles of the neighborhood triangles of the surface point and the Voronoi area. Specifically, the Gaussian curvature reflects the intrinsic curvature of the model surface and can be calculated using the following formula (2).

[0091]

[0092] Where K represents the Gaussian curvature of the surface point; A v represents the Voronoi area of the surface point; n represents the number of interior angles in the domain triangle interior angle set of the surface point; p represents the pth interior angle in the domain triangle interior angle set of the surface point.

[0093] In step S2044, the average curvature of the surface point is determined based on the neighborhood point set, the neighborhood triangle interior angle set, and the Voronoi area of the surface point. Specifically, the average curvature reflects the overall curvature trend of the model surface and can be calculated using the following formula (3).

[0094]

[0095] Where H represents the average curvature of the surface point; A v represents the Voronoi area of the surface point; V represents the surface point; i, j represent the index of the surface point; m represents the number of neighboring points in the neighborhood point set; Represented by the surface point V i and surface point V j The edges formed; α j ,β j Represents the edge Two connected corners.

[0096] Step S2045, determine the viewpoint position. Specifically, the viewpoint position is a reference point in the three-dimensional scene, which represents the position of the "observer". For example, in a three-dimensional model display of an architectural design, if you want to observe the entire interior layout from a room inside the building, then the observation point set in the room is the viewpoint position; if you observe the exterior of the building from a certain angle outside the building, the observation point outside the building is also the viewpoint position. The choice of viewpoint position can be set by the designer according to the specific application scenario and the desired effect. Different viewpoint positions will result in different observed surface concavities of the model, which in turn affects the calculation results of the concavity.

[0097] Step S2046: For each surface point, determine the viewpoint vector of the surface point based on the surface point and the viewpoint position. Specifically, determine the viewpoint vector formed by each surface point and the viewpoint position using the following equation (4).

[0098]

[0099] in, represents the viewpoint vector of the i-th surface point; Indicates the viewpoint position; represents the coordinates of the i-th surface point.

[0100] Step S2047: For each adjacent patch of the surface point, determine the normal vector of the adjacent patch based on the coordinates of each vertex of the adjacent patch. Specifically, for each surface point, consider the patch directly adjacent to the triangular patch where the surface point is located as the adjacent patch. Based on the three vertices of each adjacent patch, calculate the normal vector of the adjacent patch using the following equation (5).

[0101]

[0102] in, Represents the normal vector of the adjacent face; Represents the coordinates of the three vertices of the adjacent face.

[0103] In step S2048, the normal vectors of all adjacent facets of the surface point are weighted averaged to obtain the normal of the surface point. Specifically, a surface point is typically adjacent to multiple facets, so the normal of the surface point is the weighted average of the normal vectors of all adjacent facets. The weight corresponding to each normal vector can be set arbitrarily and is not limited in this embodiment of the present invention.

[0104] In step S2049, the dot product of the surface point's normal and the viewpoint vector is used as the surface point's concavity. Specifically, concavity describes the degree of concavity or convexity of the model surface relative to the viewpoint position. It is an important parameter for achieving natural texture mapping and can be calculated using the following equation (6). Optionally, a threshold can be set and the calculated concavity can be compared with the threshold to determine whether the surface point's location belongs to a concave, flat, or convex area.

[0105]

[0106] Among them, B i represents the concavity of the i-th surface point; represents the normal of the i-th surface point; Represents the viewpoint vector of the i-th surface point.

[0107] Step S205 : Based on the multiple geometric features corresponding to each surface point, the texture level of the model surface of the initial three-dimensional model is divided to obtain corresponding multiple texture level areas.

[0108] Specifically, the above step S205 includes:

[0109] Step S2051: Determine the weight coefficients corresponding to Gaussian curvature, mean curvature, and concavity. Specifically, the designer can set the weight coefficients, such as 0.4, 0.4, and 0.2, to adjust the influence of mean curvature, Gaussian curvature, and concavity on the detail weight.

[0110] In step S2052, for each surface point, a detail weight of the surface point is determined based on the multiple geometric features corresponding to the surface point and their weight coefficients. Specifically, the detail weight of each surface point is determined by the following equation (7). A higher detail weight indicates a richer texture detail.

[0111] W i =A×|H i |+B×|K i |+C×|B i | (7)

[0112] Among them, W i represents the detail weight of the i-th surface point; A, B, C represent the weight coefficients corresponding to the mean curvature, Gaussian curvature and concavity respectively; H i ,K i ,B i Represents the mean curvature, Gaussian curvature, and concavity of the i-th surface point.

[0113] Step S2053 determines the texture level to which each surface point's detail weight belongs based on the preset interval to which the detail weight belongs. Specifically, the designer can set multiple preset intervals, each corresponding to a texture level. The texture level corresponding to each surface point's detail weight is determined based on the preset interval within which the detail weight falls. Alternatively, a statistical analysis model can be used to set appropriate preset intervals based on the distribution of detail weights across all surface points to ensure a reasonable division of texture levels.

[0114] In step S2054, the surface of the initial 3D model is divided into multiple texture level regions based on the texture level corresponding to the detail weight of each surface point. Specifically, surface points belonging to the same texture level are grouped into a region, thereby obtaining a texture level region corresponding to each texture level, thereby achieving refined texture mapping in regions of different texture levels.

[0115] Step S206: generating a target texture based on the multiple geometric features corresponding to each surface point.

[0116] Specifically, the above step S206 includes:

[0117] In step S2061, based on the detail weight of each surface point, the reference amplitude and reference frequency of the noise corresponding to each surface point are adjusted to obtain the target amplitude and target frequency corresponding to each surface point. Specifically, when generating texture, a corresponding texture is generated at the position corresponding to each surface point, thereby obtaining the texture of the entire model surface. More specifically, assuming that Perlin noise is used to generate the texture corresponding to each surface point, the reference frequency and reference amplitude of the noise corresponding to each surface point in the texture can be adjusted according to the detail weight of the surface point using the following formula (8) to meet the requirements for texture detail. If the detail weight is high, the texture detail can be increased, and if the detail weight is low, the frequency and amplitude can be reduced to simplify the texture.

[0118]

[0119] Among them, Amplitude i Indicates the target amplitude of the noise; Amplitude basic Indicates the reference amplitude of the noise; λ, μ are preset adjustment coefficients, which can be set by yourself; W i Indicates detail weight; Frequency i Indicates the target frequency of the noise; Frequency basic Indicates the base frequency of the noise.

[0120] Step S2062, using a procedural texture algorithm, generates a target texture based on the target amplitude and target frequency corresponding to each surface point. Specifically, the procedural texture algorithm includes multiple algorithms that can generate textures, such as Perlin noise, Simplex noise, and Voronoi texture. Among them, Perlin noise is suitable for generating natural, continuous textures, such as wood grain, clouds, etc.; Simplex noise is an improved Perlin noise, which has higher computational efficiency and fewer artifacts, and is suitable for high-complexity texture generation; Voronoi texture is a generation method based on the Voronoi diagram, which divides the space by random seed points to generate polygonal texture units, and is suitable for textures such as stone and cell structures. When generating the target texture, different algorithms can be selected at different positions of the model to improve the texture effect while reducing resource consumption. Optionally, since the procedural texture algorithms are all existing algorithms, when generating the texture, it is only necessary to replace the amplitude and frequency used by the target amplitude and target frequency to generate a texture that meets the texture details. The specific process will not be repeated here.

[0121] Step S207 : Mapping the target texture to the initial three-dimensional model based on the texture level region to which each surface point belongs, to obtain a hybrid three-dimensional model.

[0122] Specifically, the above step S207 includes:

[0123] In step S2071, for each surface point, the first texture resolution corresponding to that surface point in the target texture is determined based on the texture level region to which the surface point belongs. Specifically, different texture level regions correspond to different texture image resolutions. Assume three texture levels: G1, G2, and G3, corresponding to texture level region G1, texture level region G2, and texture level region G3, respectively. In the generated target texture, a texture with a resolution of 2048x2048 is generated for texture level region G1, a texture with a resolution of 1024x1024 is generated for texture level region G2, and a texture with a resolution of 512x512 is generated for texture level region G3. If surface point A is in texture level region G1, its first texture resolution is 2048x2048.

[0124] Step S2072: Based on the texture coordinates corresponding to the surface point in the target texture, all adjacent texture coordinates of the surface point are determined. Specifically, each surface point has a corresponding point in the target texture. The coordinates of this point are also the texture coordinates, and the coordinates directly adjacent to the texture coordinate are the adjacent texture coordinates.

[0125] Step S2073: Determine the second texture resolution of each adjacent texture coordinate based on the texture level region to which each adjacent texture coordinate belongs. Specifically, the second texture resolution is determined based on the texture level region of the surface point corresponding to each adjacent texture coordinate on the model surface. Assume that two adjacent texture coordinates of surface point A correspond to texture level regions G1 and G2, respectively. Therefore, their second texture resolutions are 2048x2048 and 1024x1024, respectively.

[0126] Step S2074 : determining a target texture resolution of the surface point based on the first texture resolution, each second texture resolution, and the detail weight of the surface point.

[0127] In some optional implementations, the above step S2074 includes:

[0128] Step a1: When all second texture resolutions are consistent with the first texture resolution, determine the target texture resolution of the surface point based on the first texture resolution and the detail weight of the surface point. Specifically, if each second texture resolution is consistent with the first texture resolution, it means that the texture coordinate of the surface point and each adjacent texture coordinate are in the same texture level area, that is, the surface point is not located at a texture boundary. In this case, the target texture resolution of the surface point can be determined according to the following formula (9).

[0129] T final (x,y)=σ(x,y)·T first (x,y)+(1-σ(x,y))·T second (x,y) (9)

[0130] Among them, T final (x, y) represents the target texture resolution of the texture coordinates corresponding to the surface point; σ(x, y) represents the weight coefficient of the texture coordinates corresponding to the surface point, which is determined by the detail weight; T first (x, y) represents the first texture resolution; T second (x, y) represents the second texture resolution. If each second texture resolution is consistent with the first texture resolution, the first texture resolution is used for calculation.

[0131] Alternatively, in step a2, if any second texture resolution is inconsistent with the first texture resolution, a target texture resolution of the surface point is determined based on the first texture resolution, the second texture resolution, and the detail weight of the surface point. Specifically, if any second texture resolution is inconsistent with the first texture resolution, it means that the texture coordinate of the surface point is located at a texture boundary, and a smooth texture transition is required. Therefore, the second texture resolution that is inconsistent with the first texture resolution is substituted into the above formula (9) to determine the target texture resolution of the texture coordinate.

[0132] In step S2075, the target texture resolution of each surface point is replaced with the corresponding target texture resolution. The target texture with the replaced texture resolution is then mapped to the initial 3D model to obtain a hybrid 3D model. Specifically, the target texture resolution of the texture coordinate corresponding to each surface point is replaced with the original texture resolution, achieving a smooth transition at texture boundaries and resulting in a more realistic hybrid 3D model.

[0133] Step S208 : adjusting the texture of the hybrid three-dimensional model based on the multiple geometric features corresponding to each surface point to obtain a target three-dimensional model.

[0134] Specifically, the above step S208 includes:

[0135] Step S2081, for each first target point in the hybrid three-dimensional model whose Gaussian curvature is greater than a preset threshold, the target texture roughness of the first target point is determined based on the Gaussian curvature of the first target point, the reference texture roughness and the preset roughness adjustment coefficient. Specifically, in order to improve the authenticity of the hybrid three-dimensional model, the texture can be further optimized according to the geometric features. First, each surface point in the hybrid three-dimensional model is traversed to obtain all first target points whose Gaussian curvature is greater than the preset threshold, and curvature-driven adjustment is performed on these first target points. More specifically, the target texture roughness of the first target point is determined by the following formula (10), and the expressiveness of the geometric details is enhanced by increasing the roughness of the texture.

[0136] R(x,y)=R basic +γ·|K(x,y)| (10)

[0137] Among them, R(x,y) represents the target texture roughness of the surface point at the corresponding texture coordinate; R basic Represents the baseline texture roughness, that is, the current texture roughness; γ represents the preset roughness adjustment coefficient, which can be set by yourself; K(x,y) represents the Gaussian curvature.

[0138] Step S2082: For each second target point in the hybrid three-dimensional model whose concavity and convexity are within a preset adjustment range, the target texture brightness and target texture color of the second target point are determined based on the second target point's baseline texture brightness, baseline texture color, concavity and convexity, preset brightness adjustment coefficient, and preset color adjustment coefficient. Specifically, each surface point in the hybrid three-dimensional model is traversed to obtain all second target points whose concavity and convexity are within the preset adjustment range, and concavity and convexity driven adjustment is performed on these second target points. More specifically, the target texture brightness and target texture color of the second target point are determined using the following equation (11). By reducing the brightness and color saturation in concave areas and increasing the brightness and color saturation in convex areas, the three-dimensional effect of the surface is enhanced.

[0139]

[0140] Where L(x,y) represents the target texture brightness of the surface point at the corresponding texture coordinate; L basic Indicates the baseline texture brightness, that is, the current texture brightness; δ indicates the preset brightness adjustment coefficient, which can be set by yourself; B(x, y) indicates the concavity of the surface point; C(x, y) indicates the target texture color of the surface point at the corresponding texture coordinate; C basic Represents the baseline texture color, that is, the current texture color; ε represents the preset color adjustment coefficient, which can be set by yourself.

[0141] In step S2083, a spatial interpolation algorithm is used to adjust the texture roughness of all first target points in the hybrid 3D model within the target texture to the target texture roughness, and to adjust the texture brightness and texture color of all second target points in the hybrid 3D model within the target texture to the target texture brightness and target texture color, thereby obtaining a target 3D model. Specifically, a spatial interpolation method, such as bilinear interpolation, is used to adjust the target texture roughness, target texture brightness, and target texture color obtained by the aforementioned adjustments for all first and second target points in the hybrid 3D model, thereby achieving smooth texture adjustment and avoiding abrupt changes, thereby obtaining a target 3D model and improving detail expression and visual quality.

[0142] The geometric morphology-based texture mapping method provided by the embodiment of the present invention captures subtle changes in the model surface by extracting the geometric feature information of each surface point, divides the model surface into different texture level areas based on the extracted geometric features, and generates a suitable texture based on the geometric features of each surface point. The generated texture is accurately mapped back to the corresponding position of the three-dimensional model according to the divided texture level areas, and the mapped texture is further adjusted according to the geometric features, thereby improving the fit between the texture and the geometric features, enhancing the realism of the final model, and making the final generated target three-dimensional model have a highly realistic visual effect. By performing texture mapping based on the geometric features of the model surface, compared to manual mapping, it ensures that the texture of each area is highly matched with its geometric features, avoids texture distortion and distortion, improves detail expression and visual effects, and reduces the need for manual adjustment, thereby improving the degree of automation and work efficiency of the entire texture mapping process.

[0143] In this embodiment, a texture mapping device based on geometric morphology is also provided. The device is used to implement the above-mentioned embodiments and preferred embodiments. The details that have been described will not be repeated here. As used below, the term "module" can be a combination of software and / or hardware that implements a predetermined function. Although the devices described in the following embodiments are preferably implemented in software, implementation using hardware, or a combination of software and hardware, is also possible and contemplated.

[0144] This embodiment provides a texture mapping device based on geometric morphology, such as Figure 2 As shown, including:

[0145] The analysis module 201 is used to perform geometric data analysis on the initial three-dimensional model to obtain a plurality of geometric features corresponding to each surface point on the model surface of the initial three-dimensional model.

[0146] The division module 202 is configured to divide the surface texture of the initial three-dimensional model into different levels based on the multiple geometric features corresponding to each surface point, and obtain corresponding multiple texture level regions.

[0147] The generating module 203 is configured to generate a target texture based on a plurality of geometric features corresponding to each surface point.

[0148] The mapping module 204 is configured to map the target texture to the initial 3D model based on the texture level region to which each surface point belongs, to obtain a hybrid 3D model.

[0149] The adjustment module 205 is configured to adjust the texture of the hybrid 3D model based on multiple geometric features corresponding to each surface point to obtain a target 3D model.

[0150] In some optional embodiments, before the analysis module 201, the device further includes:

[0151] The simplification module is used to simplify the polygons of the imported 3D model to obtain a simplified 3D model.

[0152] The filling module is used to detect holes in the simplified three-dimensional model and fill them to obtain a filled three-dimensional model.

[0153] The repair module is used to detect the non-manifold edges of the filled three-dimensional model and repair them to obtain the initial three-dimensional model.

[0154] In some optional embodiments, the plurality of geometric features includes Gaussian curvature and mean curvature.

[0155] The analysis module 201 includes:

[0156] The first acquisition unit is configured to acquire, for each surface point, a neighborhood point set and a neighborhood triangle interior angle set of the surface point.

[0157] The first calculation unit is configured to calculate the Voronoi area of the surface point based on a neighborhood point set of the surface point.

[0158] The first determining unit is configured to determine the Gaussian curvature of the surface point based on a set of interior angles of a neighborhood triangle of the surface point and a Voronoi area.

[0159] The second determining unit is configured to determine the average curvature of the surface point based on a neighborhood point set of the surface point, a neighborhood triangle interior angle set, and a Voronoi area.

[0160] In some optional embodiments, the plurality of geometric features includes concavity and convexity.

[0161] The analysis module 201 further includes:

[0162] The third determining unit is used to determine the viewpoint position.

[0163] The fourth determining unit is configured to determine, for each surface point, a viewpoint vector of the surface point based on the surface point and the viewpoint position.

[0164] The fifth determining unit is configured to determine, for each adjacent facet of the surface point, a normal vector of the adjacent facet based on the coordinates of each vertex of the adjacent facet.

[0165] The second calculation unit is used to perform weighted averaging on the normal vectors of all adjacent facets of the surface point to obtain the normal of the surface point.

[0166] The sixth determining unit is configured to take the dot product of the normal of the surface point and the viewpoint vector as the concavity of the surface point.

[0167] In some optional implementations, the partitioning module 202 includes:

[0168] The coefficient determination unit is used to determine the weight coefficients corresponding to the Gaussian curvature, the mean curvature and the concavity and convexity respectively.

[0169] The weight determination unit is configured to determine, for each surface point, a detail weight of the surface point based on a plurality of geometric features corresponding to the surface point and their weight coefficients.

[0170] The level determination unit is configured to determine the texture level to which the detail weight of each surface point belongs based on the preset interval to which the detail weight belongs.

[0171] The dividing unit is used to divide the model surface of the initial three-dimensional model into multiple texture level areas based on the texture level corresponding to the detail weight of each surface point.

[0172] In some optional implementations, the generation module 203 includes:

[0173] The first adjustment unit is configured to adjust a reference amplitude and a reference frequency of noise corresponding to each surface point based on a detail weight of each surface point, so as to obtain a target amplitude and a target frequency corresponding to each surface point.

[0174] The generating unit is used for generating a target texture based on a target amplitude and a target frequency corresponding to each surface point by using a procedural texture algorithm.

[0175] In some optional implementations, the mapping module 204 includes:

[0176] The seventh determining unit is configured to determine, for each surface point, a first texture resolution corresponding to the surface point in the target texture based on the texture level region to which the surface point belongs.

[0177] The coordinate determining unit is configured to determine all adjacent texture coordinates of the surface point based on the texture coordinates corresponding to the surface point in the target texture.

[0178] The eighth determining unit is configured to determine the second texture resolution of each adjacent texture coordinate based on the texture level region to which each adjacent texture coordinate belongs.

[0179] A ninth determining unit is configured to determine a target texture resolution of the surface point based on the first texture resolution, each second texture resolution, and the detail weight of the surface point.

[0180] The mapping unit is used to replace the resolution of each surface point in the target texture with the corresponding target texture resolution, and map the target texture after the texture resolution is replaced to the initial three-dimensional model to obtain a hybrid three-dimensional model.

[0181] In some optional implementations, the ninth determining unit includes:

[0182] The first determining subunit is configured to determine the target texture resolution of the surface point based on the first texture resolution and the detail weight of the surface point when all the second texture resolutions are consistent with the first texture resolution. Or,

[0183] The second determining subunit is configured to determine a target texture resolution of the surface point based on the first texture resolution, the second texture resolution, and the detail weight of the surface point when any second texture resolution is inconsistent with the first texture resolution.

[0184] In some optional implementations, the adjustment module 205 includes:

[0185] The first adjustment unit is used to determine, for each first target point in the hybrid three-dimensional model whose Gaussian curvature is greater than a preset threshold, a target texture roughness of the first target point based on the Gaussian curvature of the first target point, a reference texture roughness and a preset roughness adjustment coefficient.

[0186] The second adjustment unit is used to determine, for each second target point in the hybrid three-dimensional model whose concavity and convexity are within a preset adjustment range, a target texture brightness and a target texture color of the second target point based on the reference texture brightness, reference texture color, concavity and convexity of the second target point, a preset brightness adjustment coefficient, and a preset color adjustment coefficient.

[0187] The third adjustment unit is used to use a spatial interpolation algorithm to adjust the texture roughness of all first target points in the hybrid three-dimensional model in the target texture to the target texture roughness, and adjust the texture brightness and texture color of all second target points in the hybrid three-dimensional model in the target texture to the target texture brightness and target texture color, so as to obtain a target three-dimensional model.

[0188] The further functional description of each of the above modules and units is the same as that of the above corresponding embodiments and will not be repeated here.

[0189] The geometric-based texture mapping device in this embodiment is presented in the form of a functional unit, where the unit refers to an ASIC (Application Specific Integrated Circuit) circuit, a processor and memory that executes one or more software or fixed programs, and / or other devices that can provide the above functions.

[0190] The embodiment of the present invention also provides a computer device having the above Figure 2 The geometry-based texture mapping device is shown.

[0191] See also Figure 3 , Figure 3 is a structural diagram of a computer device provided by an optional embodiment of the present invention, such as Figure 3 As shown, the computer device includes: one or more processors 10, memory 20, and interfaces for connecting various components, including high-speed interfaces and low-speed interfaces. Various components utilize different buses to communicate with each other and can be installed on a common mainboard or installed in other ways as needed. The processor can process the instructions executed in the computer device, including instructions stored in the memory or on the memory to display the graphical information of the GUI on an external input / output device (such as, a display device coupled to the interface). In some optional embodiments, if necessary, multiple processors and / or multiple buses can be used together with multiple memories and multiple memories. Equally, multiple computer devices can be connected, and each device provides part of the necessary operations (for example, as a server array, a group of blade servers, or a multi-processor system). Figure 3 A processor 10 is taken as an example.

[0192] The processor 10 may be a central processing unit, a network processor, or a combination thereof. The processor 10 may further include a hardware chip. The hardware chip may be an application-specific integrated circuit, a programmable logic device, or a combination thereof. The programmable logic device may be a complex programmable logic device, a field programmable gate array, a general purpose array logic, or any combination thereof.

[0193] The memory 20 stores instructions that can be executed by at least one processor 10, so as to enable at least one processor 10 to execute the method shown in the above embodiment.

[0194] The memory 20 may include a program storage area and a data storage area, wherein the program storage area may store an operating system and application programs required for at least one function. The data storage area may store data created according to the use of the computer device, etc. In addition, the memory 20 may include a high-speed random access memory and may also include a non-transient memory, such as at least one disk storage device, a flash memory device, or other non-transient solid-state storage device. In some optional embodiments, the memory 20 may optionally include a memory remotely arranged relative to the processor 10, and these remote memories may be connected to the computer device via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0195] The memory 20 may include volatile memory, such as random access memory. The memory may also include non-volatile memory, such as flash memory, a hard disk, or a solid-state drive. The memory 20 may also include a combination of the above types of memory.

[0196] The computer device further includes an input device 30 and an output device 40. The processor 10, the memory 20, the input device 30 and the output device 40 may be connected via a bus or other means. Figure 3 The bus connection is taken as an example.

[0197] The input device 30 can receive input digital or character information and generate key signal input related to user settings and function control of the computer device, such as a touch screen, a keypad, a mouse, a trackpad, a touch pad, an indicator stick, one or more mouse buttons, a trackball, a joystick, etc. The output device 40 can include a display device, an auxiliary lighting device (e.g., an LED), and a tactile feedback device (e.g., a vibration motor). The above-mentioned display device includes but is not limited to a liquid crystal display, a light emitting diode, a display, and a plasma display. In some optional embodiments, the display device can be a touch screen.

[0198] The embodiment of the present invention also provides a computer-readable storage medium. The above-mentioned method according to the embodiment of the present invention can be implemented in hardware, firmware, or implemented as a computer code that can be recorded in a storage medium, or is implemented as a computer code that is originally stored in a remote storage medium or a non-temporary machine-readable storage medium and is downloaded through a network and will be stored in a local storage medium, so that the method described herein can be stored in such software processing on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. Among them, the storage medium can be a magnetic disk, an optical disk, a read-only storage memory, a random access memory, a flash memory, a hard disk or a solid-state drive, etc. Further, the storage medium can also include a combination of the above-mentioned types of memory. It can be understood that a computer, a processor, a microprocessor controller or programmable hardware includes a storage component that can store or receive software or computer code. When the software or computer code is accessed and executed by a computer, a processor or hardware, the method shown in the above embodiment is implemented.

[0199] A portion of the present invention may be applied as a computer program product, such as a computer program instruction, which, when executed by a computer, can call or provide the method and / or technical solution according to the present invention through the operation of the computer. Those skilled in the art should understand that the form in which the computer program instruction exists in a computer-readable medium includes, but is not limited to, a source file, an executable file, an installation package file, etc. Accordingly, the way in which the computer program instruction is executed by the computer includes, but is not limited to: the computer directly executes the instruction, or the computer compiles the instruction and then executes the corresponding compiled program, or the computer reads and executes the instruction, or the computer reads and installs the instruction and then executes the corresponding installed program. Here, the computer-readable medium may be any available computer-readable storage medium or communication medium that can be accessed by the computer.

[0200] Although the embodiments of the present invention have been described with reference to the accompanying drawings, those skilled in the art may make various modifications and variations without departing from the spirit and scope of the present invention. Such modifications and variations are all within the scope defined by the appended claims.

Claims

1. A texture mapping method based on geometric morphology, characterized in that: The method comprises: Performing geometric data analysis on the initial three-dimensional model to obtain a plurality of geometric features corresponding to each surface point on the model surface of the initial three-dimensional model; Dividing the surface of the initial three-dimensional model into texture levels based on a plurality of geometric features corresponding to each surface point to obtain a plurality of corresponding texture level regions; Generate target texture based on multiple geometric features corresponding to each surface point; Mapping the target texture to the initial three-dimensional model based on the texture level region to which each surface point belongs to obtain a hybrid three-dimensional model; Based on a plurality of geometric features corresponding to each surface point, the texture of the hybrid three-dimensional model is adjusted to obtain a target three-dimensional model.

2. The method according to claim 1, characterized in that Before performing geometric data analysis on the initial three-dimensional model to obtain a plurality of geometric features corresponding to each surface point on the model surface of the initial three-dimensional model, the method further includes: Perform polygon simplification on the imported 3D model to obtain a simplified 3D model; Detecting and filling holes in the simplified three-dimensional model to obtain a filled three-dimensional model; The non-manifold edges of the filled three-dimensional model are detected and repaired to obtain the initial three-dimensional model.

3. The method according to claim 1, characterized in that The plurality of geometric features include Gaussian curvature and mean curvature; The geometric data analysis of the initial three-dimensional model is performed to obtain a plurality of geometric features corresponding to each surface point on the model surface of the initial three-dimensional model, including: For each surface point, obtain a set of neighborhood points and a set of interior angles of neighborhood triangles of the surface point; Calculating the Voronoi area of the surface point based on a neighborhood point set of the surface point; Determining the Gaussian curvature of the surface point based on a set of interior angles of neighborhood triangles of the surface point and a Voronoi area; The average curvature of the surface point is determined based on a set of neighborhood points, a set of interior angles of neighborhood triangles, and a Voronoi area of the surface point.

4. The method according to claim 3, characterized in that The plurality of geometric features include concavity and convexity; The performing of geometric data analysis on the initial three-dimensional model to obtain a plurality of geometric features corresponding to each surface point on the model surface of the initial three-dimensional model further includes: Determine the viewpoint location; For each surface point, determining a viewpoint vector for the surface point based on the surface point and the viewpoint position; For each adjacent facet of the surface point, determining a normal vector of the adjacent facet based on the coordinates of each vertex of the adjacent facet; Taking a weighted average of the normal vectors of all adjacent facets of the surface point to obtain a normal of the surface point; The dot product of the normal of the surface point and the viewpoint vector is used as the concavity of the surface point.

5. The method according to claim 4, characterized in that The method of dividing the surface texture of the initial three-dimensional model into different levels based on the multiple geometric features corresponding to each surface point to obtain the corresponding multiple texture level regions includes: Determine the weight coefficients corresponding to Gaussian curvature, mean curvature and concavity respectively; For each surface point, determining a detail weight of the surface point based on a plurality of geometric features corresponding to the surface point and their weight coefficients; Determining a texture level to which the detail weight of each surface point belongs based on a preset interval to which the detail weight belongs; Based on the texture level corresponding to the detail weight of each surface point, the model surface of the initial three-dimensional model is divided into the multiple texture level areas.

6. The method according to claim 5, characterized in that The generating of the target texture based on the multiple geometric features corresponding to each surface point includes: Based on the detail weight of each surface point, the reference amplitude and reference frequency of the noise corresponding to each surface point are adjusted to obtain the target amplitude and target frequency corresponding to each surface point; A procedural texture algorithm is used to generate the target texture based on the target amplitude and target frequency corresponding to each surface point.

7. The method according to claim 5, characterized in that The step of mapping the target texture to the initial three-dimensional model based on the texture level region to which each surface point belongs to obtain a hybrid three-dimensional model includes: For each surface point, determining a first texture resolution corresponding to the surface point in the target texture based on the texture level region to which the surface point belongs; determining all adjacent texture coordinates of the surface point based on a texture coordinate corresponding to the surface point in the target texture; determining a second texture resolution for each adjacent texture coordinate based on the texture level region to which each adjacent texture coordinate belongs; determining a target texture resolution for the surface point based on the first texture resolution, each second texture resolution, and a detail weight of the surface point; The resolution of each surface point in the target texture is replaced with the corresponding target texture resolution, and the target texture after the texture resolution is replaced is mapped to the initial three-dimensional model to obtain the hybrid three-dimensional model.

8. The method according to claim 7, characterized in that The determining, based on the first texture resolution, each second texture resolution, and the detail weight of the surface point, a target texture resolution of the surface point includes: In a case where all second texture resolutions are consistent with the first texture resolution, determining a target texture resolution of the surface point based on the first texture resolution and the detail weight of the surface point; or In a case where any second texture resolution is inconsistent with the first texture resolution, a target texture resolution of the surface point is determined based on the first texture resolution, the second texture resolution, and the detail weight of the surface point.

9. The method according to claim 4, characterized in that The step of adjusting the texture of the hybrid three-dimensional model based on the multiple geometric features corresponding to each surface point to obtain a target three-dimensional model includes: For each first target point in the hybrid three-dimensional model whose Gaussian curvature is greater than a preset threshold, determining a target texture roughness of the first target point based on the Gaussian curvature of the first target point, a reference texture roughness, and a preset roughness adjustment coefficient; For each second target point in the hybrid three-dimensional model whose concavity and convexity are within a preset adjustment range, determining a target texture brightness and a target texture color of the second target point based on the reference texture brightness, the reference texture color, the concavity and convexity, the preset brightness adjustment coefficient, and the preset color adjustment coefficient; A spatial interpolation algorithm is used to adjust the texture roughness of all first target points in the hybrid three-dimensional model in the target texture to the target texture roughness, and to adjust the texture brightness and texture color of all second target points in the hybrid three-dimensional model in the target texture to the target texture brightness and target texture color, so as to obtain the target three-dimensional model.

10. A texture mapping device based on geometric morphology, characterized in that: The device comprises: An analysis module is used to perform geometric data analysis on the initial three-dimensional model to obtain a plurality of geometric features corresponding to each surface point on the model surface of the initial three-dimensional model; a partitioning module, configured to partition the surface of the initial three-dimensional model into texture levels based on a plurality of geometric features corresponding to each surface point, to obtain a plurality of corresponding texture level regions; A generation module, for generating a target texture based on multiple geometric features corresponding to each surface point; a mapping module, configured to map the target texture to the initial three-dimensional model based on the texture level region to which each surface point belongs, to obtain a hybrid three-dimensional model; The adjustment module is used to adjust the texture of the hybrid three-dimensional model based on multiple geometric features corresponding to each surface point to obtain a target three-dimensional model.

Images