A tree surface texture modeling method, system, terminal and storage medium based on programmatic generation

Abstract

The present invention relates to the field of three-dimensional modeling technology, and discloses a tree surface texture modeling method, system, terminal, and storage medium based on programmatic generation, including: obtaining a tree surface texture image, and constructing a digital expression model of the tree texture based on the tree surface texture image; adjusting the model parameters of the digital expression model based on a programmatic generation tool for the tree surface texture to generate a three-dimensional tree model with tree surface textures of various characteristics; and outputting a three-dimensional tree model corresponding to the tree three-dimensional modeling task. The present invention, through a three-layer structure model, can transform originally complex and difficult-to-quantify natural textures into a digital expression method with clear hierarchy, operability, and strong controllability, and can achieve highly realistic and controllable texture modeling and adjustment using a programmatic generation tool, and can flexibly generate diverse and highly detailed tree surface textures according to different tree species or different forms of the same tree species.

Description

Technical Field

[0001] The present invention relates to the technical field of three-dimensional modeling, and in particular to a tree surface texture modeling method, system, terminal and storage medium based on programmatic generation. Background Art

[0002] Realistic 3D models combine precise 3D geometry, realistic texture details, and rich semantic attributes, demonstrating their individuality, solidity, structure, and semantics. Natural scenes are common in cities, and whether it's the branching forms of trees or the growth patterns of flowers and plants, they are crucial elements for realistic 3D representation. As a primary component of natural scenes, the quality of their modeling significantly impacts the overall visual realism of the scene.

[0003] The formation of tree surface texture is influenced by a variety of factors, including genetic factors determined by the genes of different tree species and environmental factors such as climate and soil. Tree surface texture covers a wide range and is rich in detail. Accurate tree surface texture reconstruction can effectively enhance the realism of tree 3D modeling. However, due to the wide variety of tree species, the complex and diverse bark characteristics, and the fact that bark changes at different growth stages and environmental conditions, realistic reconstruction of tree surface texture remains a technical challenge in tree 3D modeling.

[0004] In general, existing tree surface texture modeling methods have the following two problems:

[0005] (1) Resolution limitation. The photos collected by the sensor have a clear resolution, for example, 、 , which makes the texture image converted from the photo blurred when used for tree reconstruction, affecting the visual effect of model rendering;

[0006] (2) Static and unchangeable. Most existing tree models are static models, that is, once the model is completed, the texture and shape of the model are fixed and cannot be freely adjusted according to different tree species and their characteristics.

[0007] Therefore, the existing technologies have problems of poor rendering effect and low scalability, and these technologies need to be improved. Summary of the Invention

[0008] The technical problem to be solved by the present invention is that, in response to the defects of the existing technology, the present invention provides a tree surface texture modeling method, system, terminal and storage medium based on programmatic generation to solve the problems of poor rendering effect and low scalability of the existing tree surface texture modeling method.

[0009] The technical solutions adopted by the present invention to solve the technical problems are as follows:

[0010] In a first aspect, the present invention provides a method for modeling tree surface texture based on programmatic generation, comprising:

[0011] Acquire a tree surface texture image, and construct a digital expression model of the tree texture based on the tree surface texture image;

[0012] Based on a programmatic generation tool for tree surface texture, the model parameters of the digital expression model are adjusted to generate a three-dimensional tree model with various characteristic tree surface textures;

[0013] Output the 3D tree model corresponding to the tree 3D modeling task.

[0014] In one implementation, obtaining a tree surface texture image and constructing a digital expression model of the tree texture based on the tree surface texture image includes:

[0015] Obtain tree surface texture images of various texture types;

[0016] Classifying features in the tree surface texture image, describing attribute information corresponding to each feature, and setting control parameters corresponding to each attribute information;

[0017] According to the three-level structure model of features, attributes and parameters, a digital expression model of the tree texture is constructed.

[0018] In one implementation, the classifying of features in the tree surface texture image, describing attribute information corresponding to each feature, and setting control parameters corresponding to each attribute information includes:

[0019] Classifying the tree texture in the tree surface texture image into a plurality of key features; wherein the key features include: one or a combination of smoothness features, lenticel features, groove features, ridge features, crack features, scale features, and stripe features;

[0020] Describe each key feature according to shape, quantity, position, depth and color to obtain corresponding attribute information;

[0021] According to the role of each attribute information in the tree three-dimensional modeling task and the threshold range of the modeling scene, the control parameters corresponding to each attribute information are set.

[0022] In one implementation, the programmatic generation tool based on tree surface texture adjusts the model parameters of the digital expression model to generate a three-dimensional tree model with various characteristics of tree surface texture, including:

[0023] Taking nodes as the basic units of texture generation, attributes corresponding to various features are generated through the connection and combination of nodes. By adjusting the parameters corresponding to various attributes, the construction and dynamic adjustment of texture features are realized, and a three-dimensional tree model with tree surface textures of various characteristics is generated.

[0024] In one implementation, generating attributes corresponding to various features by connecting and combining nodes, and adjusting parameters corresponding to various attributes to achieve the construction and dynamic adjustment of texture features include:

[0025] Use shape nodes and deformation nodes to generate shape attributes and adjust the corresponding shape parameters;

[0026] The sampler node is used to generate quantity attributes, and the feature density is set by adjusting the tile quantity parameter. The randomness of the feature quantity and distribution is controlled by the mask parameter to simulate the sparse or dense changes of lenticels and cracks in nature.

[0027] The sampler node is used to generate position attributes, and the position distribution of features on the trunk surface is controlled by X and Y coordinate parameters. Combined with the random seed parameter, the feature position is randomly distributed within a certain range.

[0028] The depth attribute is generated using the Histogram node and the Levels node. The grayscale distribution and curve shape are adjusted to shape the concave and convex surface of the bark and enhance the three-dimensional effect of the texture.

[0029] Uniform Color Node, Gradient Map Node, and Hue-Saturation-Brightness Node are used to generate color attributes, and the basic color and local changes of the bark are set by adjusting the color parameters.

[0030] In one implementation, the generating of shape attributes using shape nodes and deformation nodes and adjusting corresponding shape parameters includes:

[0031] Based on the shape node, the size of the basic shape is adjusted by the width and height parameters, and the shape and size of the complex geometric features are adjusted by the size and direction parameters;

[0032] Based on the deformation node, irregular changes in the basic shape are achieved by controlling the frequency and amplitude of the noise, thereby increasing the naturalness and detail level of the texture.

[0033] In one implementation, the generating of attributes corresponding to various features by connecting and combining nodes, and adjusting parameters corresponding to various attributes to achieve the construction and dynamic adjustment of texture features, further includes:

[0034] Adjust the color value of the smooth feature to change the color, use the Position Random parameter to change the distribution of lenticels, adjust the bumpiness of the grooves and ridges with the Height Offset parameter, control the number of cracks to affect the roughness of the bark, and choose different scale base shapes with the Pattern parameter.

[0035] In a second aspect, the present invention provides a tree surface texture modeling system based on programmatic generation, comprising:

[0036] A digital model building module is used to obtain a tree surface texture image and build a digital expression model of the tree texture based on the tree surface texture image;

[0037] A three-dimensional tree model module is used to adjust the model parameters of the digital expression model based on the programmatic generation tool of the tree surface texture to generate a three-dimensional tree model with various characteristics of the tree surface texture;

[0038] The output module is used to output the three-dimensional tree model corresponding to the tree three-dimensional modeling task.

[0039] In a third aspect, the present invention provides a terminal comprising: a processor and a memory, wherein the memory stores a program for modeling tree surface textures based on programmatic generation, and when the program for modeling tree surface textures based on programmatic generation is executed by the processor, it is used to implement the operation of the method for modeling tree surface textures based on programmatic generation as described in the first aspect.

[0040] In a fourth aspect, the present invention also provides a computer-readable storage medium, which stores a tree surface texture modeling program based on programmatic generation, and when the tree surface texture modeling program based on programmatic generation is executed by a processor, it is used to implement the operation of the tree surface texture modeling method based on programmatic generation as described in the first aspect.

[0041] The present invention adopts the above technical solution to achieve the following effects:

[0042] The present invention provides a "feature-attribute-parameter" three-layer structure model, which abstracts and decomposes the complex bark texture. First, the basic visual expression of the texture is extracted as a feature unit, and then the attribute description of the feature is performed based on shape, quantity, position, depth, and color. Each attribute is further quantitatively controlled by setting specific parameters. Through this three-layer structure model, the originally complex and difficult-to-quantify natural texture can be converted into a digital expression with clear hierarchy, operability, and strong controllability; the present invention provides a programmatic node model based on tree surface texture. The model controls the five major attributes of shape, quantity, position, depth, and color through a node-based process for different categories of tree surface texture features. The programmatic node model can be used to achieve texture modeling and adjustment with high realism and strong controllability, and can flexibly generate diversified and highly detailed tree surface textures according to different tree species or different forms of the same tree species. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the structures shown in these drawings without paying any creative work.

[0044] Figure 1 It is a flow chart of the tree surface texture modeling method based on programmatic generation in the present invention.

[0045] Figure 2 It is a flow chart of the tree surface structure analysis and programmatic generation model in the present invention.

[0046] Figure 3 yes Figure 2 Schematic diagram of the procedural node sequence for the smoothing feature.

[0047] Figure 4 yes Figure 2 Schematic diagram of programmed node sequence for mesoporous features.

[0048] Figure 5 It is a schematic diagram of the seven types of tree texture characteristics in the present invention.

[0049] Figure 6 It is a schematic diagram of the digital presentation of parameters to attributes in the present invention.

[0050] Figure 7 It is a schematic diagram of the digital presentation of attributes to features in the present invention.

[0051] Figure 8 It is a functional principle diagram of a terminal in one implementation of the present invention.

[0052] The purpose, features and advantages of the present invention will be further described with reference to the accompanying drawings and in conjunction with the embodiments. DETAILED DESCRIPTION

[0053] In order to make the purpose, technical solutions and advantages of the present invention more clear and distinct, the present invention is further described in detail below with reference to the accompanying drawings and examples. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0054] Exemplary Methods

[0055] The formation of tree surface texture is influenced by a variety of factors, including genetic factors determined by the genes of different tree species and environmental factors such as climate and soil. Tree surface texture covers a wide range and is rich in detail. Accurate tree surface texture reconstruction can effectively enhance the realism of tree 3D modeling. However, due to the wide variety of tree species, the complex and diverse bark characteristics, and the fact that bark changes at different growth stages and environmental conditions, realistic reconstruction of tree surface texture remains a technical challenge in tree 3D modeling.

[0056] In general, existing tree surface texture modeling methods have the following two problems:

[0057] (1) Resolution limitation. The photos collected by the sensor have a clear resolution, for example, 、 , which makes the texture image converted from the photo blurred when used for tree reconstruction, affecting the visual effect of model rendering;

[0058] (2) Static and unchangeable. Most existing tree models are static models, that is, once the model is completed, the texture and shape of the model are fixed and cannot be freely adjusted according to different tree species and their characteristics.

[0059] Therefore, the existing technologies have problems of poor rendering effect and low scalability, and these technologies need to be improved.

[0060] In response to the above technical problems, an embodiment of the present invention provides a tree surface texture modeling method based on programmatic generation, the method comprising: obtaining a tree surface texture image, and constructing a digital expression model of the tree texture based on the tree surface texture image; adjusting the model parameters of the digital expression model based on a programmatic generation tool for the tree surface texture, and generating a three-dimensional tree model with tree surface textures of various characteristics; and outputting a three-dimensional tree model corresponding to the tree three-dimensional modeling task. The present invention, through a three-layer structure model, can transform the originally complex and difficult-to-quantify natural texture into a digital expression with clear hierarchy, operability, and strong controllability, and can achieve highly realistic and controllable texture modeling and adjustment using a programmatic generation tool, and can flexibly generate diverse and highly detailed tree surface textures according to different tree species or different forms of the same tree species.

[0061] like Figure 1 As shown, an embodiment of the present invention provides a tree surface texture modeling method based on programmatic generation, comprising the following steps:

[0062] Step S100 : obtaining a tree surface texture image, and constructing a digital expression model of the tree texture according to the tree surface texture image.

[0063] In this embodiment, the purpose is to create a tree surface texture model that is highly realistic and supports dynamic adjustment, such as Figure 2 As shown, it specifically includes two tasks:

[0064] First, through structural analysis, we deeply analyze the visual composition of tree surface texture, decompose the tree surface texture into seven types of features: smoothness, lenticels, grooves, ridges, cracks, scales, and stripes, and create a characteristic structural system for different types of tree textures.

[0065] Secondly, the above features are numerically simulated through shape, number, position, depth, and color attributes to establish a three-level programmatic generation model of "feature-attribute-parameter" to restore various details of tree surface texture, such as elliptical lenticels, dark brown grooves, multi-circle stripes, etc. This embodiment develops a programmatic generation tool for tree surface texture based on this method, such as Figure 3~Figure 4 As shown, Figure 3 yes Figure 2 Schematic diagram of the procedural node sequence for the smoothing feature. Figure 4 yes Figure 2 A schematic diagram of the procedural node sequence for the lenticel feature. By adjusting model parameters using procedural generation tools, tree surface textures with various characteristics can be generated, meeting the needs of realistic tree texture modeling and supporting tree 3D modeling tasks in real-world 3D construction.

[0066] Specifically, in one implementation of this embodiment, step S100 includes the following steps:

[0067] Step S101, obtaining tree surface texture images of various texture types;

[0068] Step S102, classifying the features in the tree surface texture image, describing the attribute information corresponding to each feature, and setting the control parameters corresponding to each attribute information;

[0069] Step S103 : constructing a digital expression model of the tree texture according to a three-level structure model of features, attributes, and parameters.

[0070] In this embodiment, tree surface texture images of various texture types are obtained from existing public databases (e.g., forestry remote sensing image libraries, bark texture databases); alternatively, tree surface texture images of various texture types are directly acquired through ground photography, drone acquisition, remote sensing data extraction, and the like; then, based on these different types of tree surface texture images, a three-layer structural framework of "features-attributes-parameters" is constructed to decompose the complex tree surface texture into visual elements that can be quantified and manipulated. The first layer of features is a set of external manifestations of the trunk surface texture, the second layer of attributes can describe the first layer of features from different aspects such as shape and color, and the third layer of parameters is used to achieve precise control of the second layer of attributes. The three elements of "features-attributes-parameters" are interrelated and together constitute a digital expression model of tree texture.

[0071] Specifically, in one implementation of this embodiment, step S102 includes the following steps:

[0072] Step S102a, classifying the tree texture in the tree surface texture image into a plurality of key features; wherein the key features include: one or a combination of smoothness features, lenticel features, groove features, ridge features, crack features, scale features, and stripe features;

[0073] Step S102b, describing each key feature according to shape, quantity, position, depth, and color to obtain corresponding attribute information;

[0074] Step S102c: setting control parameters corresponding to each attribute information according to the role of each attribute information in the three-dimensional tree modeling task and the threshold range of the modeling scene.

[0075] In this embodiment, at the feature level, the tree texture is subdivided into seven key features, including: smoothness, lenticel, groove, ridge, crack, scale, and stripe (e.g. Figure 5Because different tree species have different genetic factors and growing environments, their textures vary significantly. For example, young trees in humid environments typically have a smooth texture with only a few lenticels scattered in localized areas; whereas older trees grown in arid areas often have deeper cracks on their trunk surfaces.

[0076] The specific characteristics are as follows: the smooth feature is a uniform surface without obvious bumps; the lenticel feature is mostly linear or elliptical stomata; the groove feature and the ridge feature are paired features, appearing as depressions and protrusions respectively; the crack feature is the texture feature formed by the natural cracking of the bark, and the morphology ranges from small cracks to larger cracks; the scale feature presents a layered fish-scale or tile-like structure; the stripe feature is a striped structure caused by bark shedding or growth differences.

[0077] For the tree surface texture images of the above-mentioned various texture types, texture features can be extracted from these tree surface texture images through texture feature extraction tools (for example, ENVI+IDL tool, a remote sensing image processing and analysis tool; Python library, a set of pre-written code collections containing reusable functions, classes, modules and tools); then, pre-trained models (for example, ResNet model, a residual network model; VGG model, a convolutional neural network model) are used to perform transfer learning on the tree surface texture images, and combined with annotation tools (for example, LabelImg tool, a target detection annotation tool; CVAT tool, a target detection dataset annotation tool) to complete texture classification annotation, thereby obtaining the image sets corresponding to the above-mentioned seven key features.

[0078] In this embodiment, at the attribute level, each texture feature is described from five aspects: shape, quantity, position, depth, and color. The shape attribute defines the geometric appearance of the corresponding feature, for example, the lenticels feature is linear or elliptical, and the scale feature is polygonal; the quantity attribute reflects the distribution density and quantity variation of the corresponding feature; the position attribute determines the distribution pattern of the corresponding feature on the trunk surface; the depth attribute describes the degree of concavity of the corresponding feature, for example, the depth of the groove feature or the protrusion height of the ridge feature; and the color attribute reflects the color characteristics of different tree species and local areas of bark, for example, the dark brown bark of pine trees or the white bark of birch trees. By mixing one or more attributes, various features can be constructed.

[0079] For the image sets corresponding to the above seven key features, by analyzing the differences in genetic factors and growth environment among different tree species, the attribute-level description schemes corresponding to the above seven key features are determined, thereby obtaining multiple attribute description information of each texture feature.

[0080] In this embodiment, parameters are key variables used to control properties. For example, shape properties can be controlled through style, scale, size, and rotation parameters; quantity properties can be controlled through tile count and input count parameters; position properties can be adjusted through position randomness and offset parameters; depth properties can be set through intensity and contrast parameters; and color properties can be set through RGB values ​​(i.e., color values), hue, saturation, and brightness parameters.

[0081] In this embodiment, based on factors such as tree species, growth environment, and age, combined with historical tree surface texture modeling data (ie, empirical data), an attribute-parameter comparison table is obtained, as shown in Table 1.

[0082] Table 1 Attribute-parameter comparison table

[0083]

[0084] The attribute-parameter comparison table shown in Table 1 above shows the parameter variables of different attributes, their corresponding functions in the tree surface texture modeling tool (i.e., the procedural generation tool for tree surface texture), and the corresponding adjustable value range; for example, the style parameter of the shape attribute is used to select the basic pattern type, and the corresponding value range is rectangle, circle, parabola, etc.; the random mask parameter of the quantity attribute is used to randomly hide a certain proportion of units, and the corresponding value range is (0, 1); the displacement parameter of the position attribute is used to globally or locally translate the unit, and the corresponding value range is (0, 1); the position parameter of the depth attribute is used to control the midpoint position of the grayscale range after contraction (i.e., the span between the minimum and maximum values ​​determined by the Range parameter, where the Range parameter is a parameter used to limit the numerical range), and the corresponding value range is (0, 1); the saturation parameter of the color attribute is used to adjust the saturation of the input color and affect the vividness of the color, and the corresponding value range is (0, 1).

[0085] In this embodiment, according to the above-mentioned attribute-parameter comparison table, some parameter control effects also include: adjusting the RGB value of the smoothing feature to change the color, using the position random parameter to change the distribution pattern of the lenticels, adjusting the concave and convex feeling of the grooves and ridges through the height offset parameter, controlling the number of cracks to affect the roughness of the bark, and selecting different basic shapes of scales through the pattern parameter.

[0086] In this embodiment, by classifying the features in the tree surface texture image, describing the attribute information corresponding to each feature, and setting the control parameters corresponding to each attribute information, a digital expression model of the tree texture can be constructed according to the three-level structure model of feature-attribute-parameter; specifically, the digital expression model of the tree texture is constructed as follows: based on the digital twin platform, using a three-dimensional visualization engine, the feature layer is constructed through multimodal feature fusion, and then the attribute layer is constructed through a quantitative parameter system, and the parameter layer is constructed through a programmable control interface.

[0087] This embodiment provides a three-layered "feature-attribute-parameter" model that abstracts and decomposes complex bark textures. First, the basic visual representation of the texture is extracted as feature units. These features are then attributed based on shape, quantity, position, depth, and color. Each attribute is then quantitatively controlled by setting specific parameters. This three-layered model transforms complex, difficult-to-quantify natural textures into a clearly hierarchical, actionable, and controllable digital representation.

[0088] like Figure 1 As shown, an embodiment of the present invention provides a tree surface texture modeling method based on programmatic generation, comprising the following steps:

[0089] Step S200 : Based on a programmatic tree surface texture generation tool, the model parameters of the digital expression model are adjusted to generate a three-dimensional tree model with various characteristic tree surface textures.

[0090] This embodiment further proposes a procedural tree texture generation method based on a node network. Nodes are used as the basic units for texture generation. Each node includes an input port, a parameter panel, and an output port. By connecting and combining nodes, texture features can be constructed and dynamically adjusted, thereby generating a 3D tree model with various characteristic tree surface textures.

[0091] Specifically, in one implementation of this embodiment, step S200 includes the following steps:

[0092] In step S201, nodes are used as the basic units for texture generation. Attributes corresponding to various features are generated by connecting and combining nodes. Parameters corresponding to various attributes are adjusted to achieve the construction and dynamic adjustment of texture features, thereby generating a three-dimensional tree model with tree surface textures of various features.

[0093] Specifically, in one implementation of this embodiment, step S201 includes the following steps:

[0094] Step S201a: Generate shape attributes using shape nodes and deformation nodes, and adjust corresponding shape parameters.

[0095] Specifically, in one implementation of this embodiment, the shape attributes are generated using shape nodes and deformation nodes, and the corresponding shape parameters are adjusted, including: based on the shape node, adjusting the size of the basic shape through width and height parameters, and adjusting the shape and size of complex geometric features through size and direction parameters; based on the deformation node, by controlling the noise frequency and amplitude, irregular changes in the basic shape are achieved, thereby increasing the naturalness of the texture and the level of detail.

[0096] Step S201b: Generate quantity attributes using a sampler node, set feature density by adjusting the tile quantity parameter, and control the randomness of feature quantity and distribution through mask parameters to simulate the sparse or dense variations of lenticels and cracks in nature;

[0097] Step S201c, using the sampler node to generate position attributes, and controlling the position distribution of features on the trunk surface through X and Y coordinate parameters, combined with random seed parameters, to achieve random distribution of feature positions within a certain range;

[0098] Step S201d: Use the histogram node and the color level node to generate the depth attribute, and adjust the grayscale distribution and curve shape to shape the concave and convex feeling of the bark surface and enhance the three-dimensional effect of the texture;

[0099] Step S201e: Generate color attributes using a uniform color node, a gradient map node, and a hue-saturation-brightness node, and set the basic color and local changes of the bark by adjusting color parameters.

[0100] In this example, when generating a 3D tree model using a procedural generation tool based on tree surface textures, shape attributes are primarily controlled through a combination of shape nodes and deformation nodes. The shape node determines the basic shape of the feature (e.g., rectangle or circle), and adjusts its size through width and height parameters. For complex geometric features (e.g., diamonds or polygons), adjustments are made through parameters such as size and orientation.

[0101] This embodiment introduces noise (e.g., Perlin noise, a natural noise generation algorithm) into the deformation node. By controlling the noise frequency and amplitude, irregular changes in the basic shape are achieved, thereby increasing the naturalness and detail level of the texture, as shown in Table 2.

[0102] Table 2 Attribute Adjustment

[0103]

[0104] In this embodiment, the quantity attribute is controlled by the sampler node. Specifically, the feature density is set by adjusting the tile quantity parameter, and the randomness of the feature quantity and distribution is controlled by the mask parameter to simulate the sparse or dense changes of elements such as pores and cracks in nature.

[0105] In this embodiment, the position attribute is controlled by the sampler node; specifically, the position distribution of the features on the trunk surface is controlled by the X and Y coordinate parameters, and the random seed parameter is combined to achieve random distribution of the feature positions within a certain range to avoid overly regular arrangement.

[0106] In this example, the depth attribute is controlled using histogram nodes and color scale nodes. Specifically, by adjusting the grayscale distribution and curve shape, the surface of the bark is shaped to create a sense of relief and enhance the three-dimensional effect of the texture. For example, by varying the grayscale value, the depth of the grooves or ridges is controlled to enhance the visual sense of relief.

[0107] In this example, color properties are set using Uniform Color nodes, Gradient Map nodes, and HSL nodes (i.e., Hue-Saturation-Lightness nodes) to define the base color and local variations of the bark. The base color can be customized to the tree species, for example, dark brown (R=101, G=67, B=33) for pine and white (R=255, G=255, B=255) for birch. HSL nodes are then used to refine contrast, saturation, and brightness. Furthermore, blend nodes are used to create areas of moss or localized discoloration, giving the texture a richer and more natural feel.

[0108] like Figure 6-7 As shown, Figure 6 It is a digital representation diagram of parameters and attributes. Figure 7 This embodiment summarizes the attribute composition of seven types of tree texture features. Each attribute is implemented through different node groups in the digitization program, thereby mapping to the corresponding feature.

[0109] In this embodiment, through node-based modeling and parameter optimization of each texture feature in the five attribute dimensions of shape, quantity, position, depth and color, it is possible to generate highly realistic and detailed tree surface textures to meet the needs of high-quality three-dimensional modeling and visual expression.

[0110] Specifically, in one implementation of this embodiment, step S200 further includes the following steps:

[0111] Step S202: Adjust the color value of the smooth feature to change the color, use the position random parameter to change the distribution of the lenticels, adjust the concave and convex feeling of the grooves and ridges through the height offset parameter, control the number of cracks to affect the roughness of the bark, and select different basic shapes of scales through the pattern parameter.

[0112] In this embodiment, in addition to the above-mentioned control methods, other parameter control methods include: adjusting the RGB value of the smooth feature to change the color, using the position random parameter to change the distribution pattern of the lenticels, adjusting the concave and convex feeling of the grooves and ridges through the height offset parameter, controlling the number of cracks to affect the roughness of the bark, and selecting different basic shapes of scales through the pattern parameter.

[0113] This embodiment provides a procedural node-based model for tree surface textures. This model uses a node-based process to control five key attributes: shape, quantity, position, depth, and color, targeting the surface texture characteristics of different tree types. This procedural node-based model enables highly realistic and controllable texture modeling and adjustment, enabling the flexible generation of diverse and highly detailed tree surface textures for different tree species or varying forms within the same tree species.

[0114] like Figure 1 As shown, an embodiment of the present invention provides a tree surface texture modeling method based on programmatic generation, comprising the following steps:

[0115] Step S300: outputting a three-dimensional tree model corresponding to the three-dimensional tree modeling task.

[0116] In this embodiment, through the above-mentioned programmatic generation tool of tree surface texture, the five major attributes of shape, quantity, position, depth and color are controlled through a node-based process for the surface texture characteristics of trees of different categories, and texture modeling and adjustment with high realism and strong controllability are performed to output a three-dimensional tree model corresponding to the tree three-dimensional modeling task; the three-dimensional tree model output by the programmatic generation tool in this embodiment has a good rendering effect, and the texture can be adjusted according to different parameter requirements to achieve high scalability.

[0117] In addition to the aforementioned "feature-attribute-parameter" three-layer structure model and the programmatic node model for tree surface texture, this embodiment also utilizes traditional image processing algorithms (e.g., fractal noise, texture synthesis, and image stitching) for texture generation. Furthermore, bark texture can be simulated through local texture sample expansion, automatic stitching, or rule-based noise overlay methods to achieve tree surface texture model generation. Alternatively, 3D scanning technology can be used to obtain texture data from real tree trunk surfaces, construct a bark texture template library, and then directly apply it to the 3D model surface through texture mapping or mapping, rather than through feature decomposition and parameterized control.

[0118] It's worth noting that the "feature-attribute-parameter" model and procedural generation method proposed in this example are not only applicable to modeling tree surface textures but can also be extended to modeling other natural surface textures, such as leaves, water, and the ground, showing great potential for widespread application. By adjusting the feature classification and parameter system, rapid reconstruction and control of surface textures of different materials can be achieved.

[0119] Furthermore, the method in this embodiment is not only applicable to the generation of 3D tree models, but is also well-suited for applications in fields such as virtual reality and game development, and is highly valuable for projects requiring the batch generation of highly realistic natural texture content. Furthermore, this embodiment can be integrated with AI (artificial intelligence) content generation technology to further enable intelligent generation and automated control of natural texture content.

[0120] This embodiment achieves the following technical effects through the above technical solution:

[0121] This embodiment provides a "feature-attribute-parameter" three-layer structure model, which abstracts and decomposes the complex bark texture. First, the basic visual expression of the texture is extracted as a feature unit, and then the attribute description of the feature is performed based on shape, quantity, position, depth, and color. Each attribute is further quantitatively controlled by setting specific parameters. Through this three-layer structure model, the originally complex and difficult-to-quantify natural texture can be converted into a digital expression with clear hierarchy, operability, and strong controllability; this embodiment provides a programmatic node model based on tree surface texture. The model controls the five major attributes of shape, quantity, position, depth, and color through a node-based process for different categories of tree surface texture features. The programmatic node model can be used to achieve highly realistic and controllable texture modeling and adjustment, and can flexibly generate diverse and highly detailed tree surface textures according to different tree species or different forms of the same tree species.

[0122] Exemplary devices

[0123] Based on the above embodiment, the present invention further provides a tree surface texture modeling system based on programmatic generation, comprising:

[0124] A digital model building module is used to obtain a tree surface texture image and build a digital expression model of the tree texture based on the tree surface texture image;

[0125] A three-dimensional tree model module is used to adjust the model parameters of the digital expression model based on the programmatic generation tool of the tree surface texture to generate a three-dimensional tree model with various characteristics of the tree surface texture;

[0126] The output module is used to output the three-dimensional tree model corresponding to the tree three-dimensional modeling task.

[0127] This embodiment achieves the following technical effects through the above technical solution:

[0128] This embodiment provides a "feature-attribute-parameter" three-layer structure model, which abstracts and decomposes the complex bark texture. First, the basic visual expression of the texture is extracted as a feature unit, and then the attribute description of the feature is performed based on shape, quantity, position, depth, and color. Each attribute is further quantitatively controlled by setting specific parameters. Through this three-layer structure model, the originally complex and difficult-to-quantify natural texture can be converted into a digital expression with clear hierarchy, operability, and strong controllability; this embodiment provides a programmatic node model based on tree surface texture. The model controls the five major attributes of shape, quantity, position, depth, and color through a node-based process for different categories of tree surface texture features. The programmatic node model can be used to achieve highly realistic and controllable texture modeling and adjustment, and can flexibly generate diverse and highly detailed tree surface textures according to different tree species or different forms of the same tree species.

[0129] Based on the above embodiment, the present invention further provides a terminal, whose principle block diagram can be shown as follows: Figure 8 shown.

[0130] The terminal includes: a processor, memory, interface, display screen and communication module connected via a system bus; wherein the processor of the terminal is used to provide computing and control capabilities; the memory of the terminal includes a computer-readable storage medium and an internal memory; the computer-readable storage medium stores an operating system and a computer program; the internal memory provides an environment for the operation of the operating system and computer program in the computer-readable storage medium; the interface is used to connect to external devices; the display screen is used to display corresponding information; and the communication module is used to communicate with a cloud server or other devices.

[0131] When the computer program is executed by a processor, it is used to implement the operation of a tree surface texture modeling method based on programmatic generation.

[0132] It will be understood by those skilled in the art that Figure 8 The principle block diagram shown in the figure is only a block diagram of a partial structure related to the solution of the present invention, and does not constitute a limitation on the terminal to which the solution of the present invention is applied. The specific terminal may include more or fewer components than shown in the figure, or combine certain components, or have a different component arrangement.

[0133] In one embodiment, a terminal is provided, which includes: a processor and a memory, wherein the memory stores a tree surface texture modeling program based on programmatic generation, and when the tree surface texture modeling program based on programmatic generation is executed by the processor, it is used to implement the operations of the above-mentioned tree surface texture modeling method based on programmatic generation.

[0134] In one embodiment, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores a tree surface texture modeling program based on programmatic generation, and when the tree surface texture modeling program based on programmatic generation is executed by a processor, it is used to implement the operations of the above-mentioned tree surface texture modeling method based on programmatic generation.

[0135] Those skilled in the art will appreciate that all or part of the processes in the above-described method embodiments can be implemented by instructing the relevant hardware through a computer program. The computer program can be stored in a non-volatile storage medium. When executed, the computer program can include the processes in the above-described method embodiments. Any reference to memory, storage, database, or other media used in the various embodiments provided herein may include both non-volatile and volatile memory.

[0136] In summary, the present invention provides a tree surface texture modeling method, system, terminal, and storage medium based on programmatic generation, including: obtaining a tree surface texture image, and constructing a digital expression model of the tree texture based on the tree surface texture image; adjusting the model parameters of the digital expression model based on a programmatic generation tool for the tree surface texture to generate a three-dimensional tree model with tree surface textures of various characteristics; and outputting a three-dimensional tree model corresponding to the tree three-dimensional modeling task. Through a three-layer structure model, the present invention can transform the originally complex and difficult-to-quantify natural texture into a digital expression with clear hierarchy, operability, and strong controllability, and utilize programmatic generation tools to achieve highly realistic and controllable texture modeling and adjustment, and can flexibly generate diverse and highly detailed tree surface textures based on different tree species or different forms of the same tree species.

[0137] It should be understood that the application of the present invention is not limited to the above examples. For those skilled in the art, improvements or changes can be made based on the above description. All these improvements and changes should fall within the scope of protection of the claims attached to the present invention.

Claims

1. A tree surface texture modeling method based on program generation, characterized in that: include: Acquire a tree surface texture image, and construct a digital expression model of the tree texture based on the tree surface texture image; Based on a programmatic generation tool for tree surface texture, the model parameters of the digital expression model are adjusted to generate a three-dimensional tree model with various characteristic tree surface textures; Output the 3D tree model corresponding to the tree 3D modeling task; The programmatic generation tool based on tree surface texture adjusts the model parameters of the digital expression model to generate a three-dimensional tree model with various characteristics of tree surface texture, including: Using nodes as the basic unit of texture generation, the corresponding attributes of various features are generated by connecting and combining nodes. By adjusting the parameters corresponding to various attributes, the texture features are constructed and dynamically adjusted to generate a three-dimensional tree model with various characteristics of tree surface textures. The connection and combination of nodes generates attributes corresponding to various features, and the parameters corresponding to various attributes are adjusted to achieve the construction and dynamic adjustment of texture features, including: Use shape nodes and deformation nodes to generate shape attributes and adjust the corresponding shape parameters; The sampler node is used to generate quantity attributes, and the feature density is set by adjusting the tile quantity parameter. The randomness of the feature quantity and distribution is controlled by the mask parameter to simulate the sparse or dense changes of lenticels and cracks in nature. The sampler node is used to generate position attributes, and the position distribution of features on the trunk surface is controlled by X and Y coordinate parameters. Combined with the random seed parameter, the feature position is randomly distributed within a certain range. The depth attribute is generated using the Histogram node and the Levels node. The grayscale distribution and curve shape are adjusted to shape the concave and convex surface of the bark and enhance the three-dimensional effect of the texture. Uniform Color Node, Gradient Map Node, and Hue-Saturation-Brightness Node are used to generate color attributes, and the basic color and local changes of the bark are set by adjusting the color parameters.

2. The tree surface texture modeling method based on program generation according to claim 1 is characterized in that: The step of obtaining a tree surface texture image and constructing a digital expression model of the tree texture based on the tree surface texture image includes: Obtain tree surface texture images of various texture types; Classifying features in the tree surface texture image, describing attribute information corresponding to each feature, and setting control parameters corresponding to each attribute information; According to the three-level structure model of features, attributes and parameters, a digital expression model of the tree texture is constructed.

3. The tree surface texture modeling method based on program generation according to claim 2 is characterized in that: The classifying of features in the tree surface texture image, describing attribute information corresponding to each feature, and setting control parameters corresponding to each attribute information include: Classifying the tree texture in the tree surface texture image into a plurality of key features; wherein the key features include: one or more combinations of smooth features, lenticel features, groove features, ridge features, crack features, scale features, and stripe features; Describe each key feature according to shape, quantity, position, depth and color to obtain corresponding attribute information; According to the role of each attribute information in the corresponding tree 3D modeling task and the threshold range of the modeling scene, the control parameters corresponding to each attribute information are set.

4. The tree surface texture modeling method based on program generation according to claim 1 is characterized in that: The method of using shape nodes and deformation nodes to generate shape attributes and adjusting corresponding shape parameters includes: Based on the shape node, the size of the basic shape is adjusted by the width and height parameters, and the shape and size of the complex geometric features are adjusted by the size and direction parameters; Based on the deformation node, by controlling the noise frequency and amplitude, irregular changes in the basic shape are achieved, thereby increasing the naturalness and detail level of the texture.

5. The tree surface texture modeling method based on program generation according to claim 1 is characterized in that: The method of generating attributes corresponding to various features by connecting and combining nodes, and implementing the construction and dynamic adjustment of texture features by adjusting parameters corresponding to various attributes, further includes: Adjust the color value of the smooth feature to change the color, use the Position Random parameter to change the distribution of lenticels, adjust the bumpiness of the grooves and ridges with the Height Offset parameter, control the number of cracks to affect the roughness of the bark, and choose different scale base shapes with the Pattern parameter.

6. A system for modeling tree surface textures based on program generation, used to implement the method for modeling tree surface textures based on program generation according to any one of claims 1 to 5, characterized in that: include: A digital model building module is used to obtain a tree surface texture image and build a digital expression model of the tree texture based on the tree surface texture image; A three-dimensional tree model module is used to adjust the model parameters of the digital expression model based on the programmatic generation tool of the tree surface texture to generate a three-dimensional tree model with various characteristics of the tree surface texture; The output module is used to output the three-dimensional tree model corresponding to the tree three-dimensional modeling task.

7. A terminal, characterized in that: include: A processor and a memory, wherein the memory stores a tree surface texture modeling program based on programmatic generation, and when the tree surface texture modeling program based on programmatic generation is executed by the processor, it is used to implement the operation of the tree surface texture modeling method based on programmatic generation as described in any one of claims 1 to 5.

8. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a tree surface texture modeling program based on programmatic generation, and when the tree surface texture modeling program based on programmatic generation is executed by a processor, it is used to implement the operation of the tree surface texture modeling method based on programmatic generation as described in any one of claims 1 to 5.

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