72 results about "Texture model" patented technology

The invention relates to the field of commodity visualization, in particular to a commodity display interaction visualization method and device. The method comprises the following steps: collecting a multi-azimuth image of a commodity, carrying out three-dimensional texture modeling, and constructing a three-dimensional texture mapping model; performing material light rendering on the three-dimensional texture mapping model to generate a material rendering result; collecting an environment detection image of a commodity display environment, and performing environment illumination adaptation compensation on a material rendering result to obtain an illumination compensation rendering commodity; carrying out attribute information visual layout on the illumination compensation rendering commodity to obtain a commodity visual space; and carrying out interaction response animation analysis according to the commodity visualization space, carrying out multi-target parallel rendering, and executing commodity interaction visualization operation. The form and surface details of the commodity in the real world are accurately restored, the visual reality sense is improved, and the interactive experience feeling of browsing the commodity by a user is enhanced.

The invention provides a hull surface defect detection system based on machine vision, and relates to the technical field of data processing. The image correction module is used for carrying out illumination equalization processing and geometric distortion correction; the region construction module is used for identifying a defect-free stable region and generating reference region data which comprises a brightness model and a texture model; the candidate generation module is used for detecting a region where texture interruption or abnormal bright spots exist locally to form candidate defect data, and the candidate defect data comprise pixel positions and local contrast parameters; the stability judgment module is used for carrying out projection matching in the multiple frames of images and simultaneously carrying out joint comparison with the brightness model and the texture model of the reference area data to form real defect data and false defect data; the result output module is used for generating a detection result containing defect coordinates, defect contours, image frame numbers and interference sample prompts; the accuracy of hull surface defect detection is improved.

The invention relates to the technical field of three-dimensional scanning, and discloses a binocular three-dimensional scanning system and method with a texture mapping function. The system comprises a depth data acquisition module, a depth data correction module, a point cloud topology construction module, a texture mapping engine module and a mapping optimization module. The depth data acquisition module synchronously acquires a left view and a right view of a target object through a binocular camera array, and generates an original depth image data set; the depth data correction module performs abnormal value detection on the original depth data, extracts discrete distribution through aggregation boundary analysis and replaces abnormal values; the point cloud topology construction module generates a three-dimensional point cloud spatial topology structure, and associates point cloud coordinates with left view pixel positions; the texture mapping engine module projects left view RGB data to the point cloud to generate an initial texture map; and the mapping optimization module verifies the local color consistency through the generative adversarial network, and outputs an optimized three-dimensional texture model. The system improves the texture precision and authenticity of the three-dimensional model.

The invention discloses a metal product defect detection method and system based on image recognition. The method comprises the following steps: firstly, executing reflection disturbance digestion processing on a surface image of a to-be-detected metal product to obtain a reflection digestion image; obtaining surface reference texture features of the defect-free metal product, and generating a reference texture model on the basis of a texture distribution rule, a gray average value and texture continuity; performing defect texture gradient separation on the reflection resolution image based on a reference texture model, positioning an abnormal region, and performing segmentation to obtain a suspected defect texture region; performing boundary pixel reconstruction on the suspected defect texture region to obtain a defect texture reconstruction region; obtaining a target defect texture region through local variance enhancement and neighborhood correlation analysis; and finally, the overexposure area is positioned, gray inverse stretching processing is performed, abnormal pixel group feature clustering is performed on the preprocessed defect area, and then a surface defect detection result is output, so that the precision and accuracy of metal product surface defect detection are improved, and the false detection rate is reduced.

The invention discloses a multi-modal lightweight forest fire detection method suitable for a vertical take-off and landing fixed-wing unmanned aerial vehicle platform, and the method comprises the steps: carrying out the feature alignment and weight fusion of a visible light image and an infrared image, which are synchronously collected by an unmanned aerial vehicle, through a multi-modal early fusion method, and obtaining a fusion feature; carrying out lightweight down-sampling and multi-scale feature extraction on the fusion features to obtain a multi-scale feature set; performing cross-scale feature fusion on the multi-scale feature set by adopting a bidirectional feature pyramid network, and extracting dynamic change information to obtain time sequence features; performing multi-scale context enhancement on the time sequence features by using a spatial pyramid method based on cavity convolution, and performing global context and local texture modeling by using a lightweight Transform module to obtain high-level semantic features; and based on the high-level semantic features, a target detection frame, fire level classification, prediction uncertainty estimation and a pixel-level fire probability graph are generated in parallel through a multi-task output header.

The invention discloses a medical image segmentation method based on boundary constraint. The method comprises the following steps: S1, obtaining a medical image amplitude spectrum and a medical image phase spectrum; s2, forming a structure sensing frequency domain disturbance result; s3, obtaining a frequency domain enhanced medical image; s4, calculating structure perception medical image boundary enhanced attention output features; s5, performing global relation modeling and local texture modeling on the frequency domain enhanced medical image sequence; s6, generating an edge heat map; and S7, performing bidirectional gating interaction on the edge heat map and the output of the linear self-attention encoder by adopting a double-domain interaction fusion strategy to generate a double-domain interaction fusion feature map, performing up-sampling and convolution processing on the double-domain interaction fusion feature map in a decoder, and outputting a boundary enhancement segmentation feature map. According to the invention, the structure perception capability of the model in a medical image scene with fuzzy anatomical structure boundary and low texture contrast is effectively improved.

The invention discloses a printed matter quality visual inspection system based on image features, and relates to the technical field of printed matter quality visual inspection. Comprising an image partitioning module, a gray level standardization and discretization module, a gray level co-occurrence matrix construction module, a co-occurrence matrix normalization module, a texture feature extraction module, a texture density analysis module, a feature point density regulation and control module and a feature point extraction and fusion module. The image partitioning module is used for carrying out gridding division on the whole printed matter image according to a set region size to form a plurality of equal-area sub-regions; and the grayscale standardization and discretization module is used for converting each sub-region image into a grayscale image and unifying an image data expression form. According to the method, dynamic matching of the number of feature points and the local texture density is realized through regionalized texture modeling, false detection of a high texture region is inhibited, defect perception of a low texture region is enhanced, and feature distribution balance and expression integrity are improved, so that the defect detection efficiency is optimized.

The invention discloses a hierarchical image rain removal method based on enhanced rain stripe perception. According to the method, for the problem of image quality degradation in a rainy day environment, an enhanced rain stripe perception feature enhancement module E-RAFEM is designed, two core components of a learnable direction filter and rain stripe texture modeling are integrated, and the directivity and linear texture features of rain stripes are accurately modeled. An encoder-decoder backbone network based on Transform is adopted, and E-RAFEM modules are embedded in the first three encoding levels, so that a hierarchical rain stripe processing mechanism is formed. A progressive multi-scale fusion mechanism is introduced, multi-scale features are captured through convolution kernels with different expansion rates, and gradual integration is carried out in a progressive mode to avoid information loss. According to the enhanced rain stripe sensing network ERA-Net provided by the invention, high-quality recovery of images under various complex rainy day conditions is realized through accurate rain stripe feature modeling and hierarchical processing strategies.

The invention discloses a Hololens-oriented single-view texture-free object pose estimation method, belongs to the technical field of augmented reality, and solves the problem that an existing Hololens pose estimation method cannot perform accurate pose estimation on a texture-free model through single-view data under the conditions of no marker and no pre-training. According to the method, multi-modal data acquisition, self-supervised segmentation, self-adaptive point cloud denoising and geometrically-driven pose optimization are integrated, and accurate pose estimation is carried out on a texture-free object only through single-view-angle data under the condition that no marker exists and pre-training is not carried out. Through multi-modal data and spatial transformation, a point cloud reconstructed by a Depth image is mapped to a visual angle of an RGB camera, so that the point cloud is conveniently filtered by using a target mask obtained on the RGB image. Meanwhile, rendering templates under multiple visual angles are generated by rendering the CAD model, obstacles caused by no texture of the target model are avoided, meanwhile, the CAD model does not depend on pre-training, and the zero sample learning ability is provided.

The invention discloses a welding quality online detection and optimization method and system, and the method comprises the steps: carrying out laser scanning, and obtaining inertial navigation data of a mobile robot; performing motion compensation and multi-frame alignment on the original point cloud data through the inertial navigation data; starting an image acquisition device to carry out image acquisition; performing spatial alignment on the visible light image and the alignment point cloud data through inertial navigation data; visible light texture data obtained by aligning the image sequence is mapped to the 3D point cloud model, and the welding quality is detected through the 3D texture model; and according to the welding quality detection result, welding parameters are adjusted in a self-adaptive mode. The problems of image blurring and dislocation caused by track deviation or environment vibration in long-distance movement detection in a non-special site are effectively solved, the geometric accuracy and stability of point cloud data are remarkably improved, and a reliable foundation is laid for subsequent detection.

The invention discloses a short wave infrared small sample three-dimensional Gaussian three-dimensional reconstruction method based on knowledge assistance. The method comprises the following steps: firstly, converting a three-dimensional structure model into a point cloud, carrying out structure perception point cloud fusion on the point cloud and an original target point cloud, and then carrying out initial training to realize geometric construction based on structure prior; then generating a short-wave infrared simulation image based on the three-dimensional model, and performing target instance driven cross-domain adaptation training on a pre-trained diffusion model to obtain a cross-domain diffusion model; and finally, in combination with a cross-domain diffusion model and a structure prior driven geometric construction result, triple constraints are added to reconstruct a three-dimensional image. Through staged modeling under structure guidance, the fuzzy or artifact risk caused by directly introducing misaligned or error accumulated geometric information into the texture stage is avoided, and a stable structure guidance image with clear components is provided for the texture modeling stage; and the generation stability and the structural fidelity of the diffusion model under complex observation conditions are effectively enhanced.

The application relates to the technical field of image processing, in particular to a thermal imaging visualization method and system for energy efficiency analysis of a roasting process. The method comprises the following steps: acquiring a thermal imaging image of a roasting kiln body surface, and calculating local complexity indexes and local anisotropy indexes of each pixel point in the thermal imaging image; extracting a multi-scale and multi-direction texture feature set of each pixel point in the thermal imaging image; calculating scale weights of each scale and direction weights of each direction, and performing weighted fusion on the texture feature set to obtain a fusion feature vector of each pixel point; establishing a normal texture model based on the fusion feature vector, and calculating an abnormal score image of the thermal imaging image to determine an abnormal heat loss area and perform visualization. Through the technical scheme, different scale texture changes can be effectively identified, and the detection accuracy of the abnormal heat loss area is improved.

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.

This invention provides a weight training method and system for generating geological lithological texture models based on LoCon, belonging to the field of geological lithological texture generation technology. It employs an LDM model as the large model framework for weight training and texture generation. Convolutional layers are integrated into the convolutional weights of the residual blocks in the U-Net model within the LoCon model. The low-rank matrix of the LoCon model is added to the pre-trained weight matrix in the U-Net model to construct the generated geological lithological texture model. The model is trained using a training set. The trained weights are output, loaded into the large model framework for inference, and the training parameters are adjusted based on the inference results. Training is repeated until the preset inference result requirements are met. An API is provided to create an interface between the LDM large model framework and the trained weight matrix. This fills a gap in the geological industry's material library and lowers the technical threshold for geologists to participate in digitization.

The invention relates to the technical field of three-dimensional visualization, in particular to a three-dimensional visualization collaborative design method under a complex terrain, and solves the technical problem of poor three-dimensional modeling effect in the prior art. The method comprises the following steps: acquiring point cloud data of a target object scanned by a plurality of scanning sites, and acquiring a multi-view image of the target object acquired by image acquisition equipment; the multi-view image comprises a plurality of target images collected by the target object in different view directions; identifying an abnormal brightness area from the multi-view image, and repairing the abnormal brightness area to generate a repaired multi-view image; and performing three-dimensional reconstruction and image texture mapping based on the repaired multi-view image and the point cloud data to generate a three-dimensional scene texture model.

The invention relates to the technical field of image processing, in particular to a textile material surface defect detection system based on machine vision, the system comprises a processor and a memory, the processor executes a computer program of the memory to realize the following steps: obtaining a spectrogram of a textile material to be detected and the amplitude of each frequency component in the spectrogram, dividing the spectrogram into at least two initial sub-regions, acquiring a peak value prominence degree, an amplitude distribution obvious degree and a dominant frequency existence index of any initial sub-region, and according to the peak value prominence degree, the amplitude distribution obvious degree and the dominant frequency existence index of any initial sub-region, determining the dominant frequency existence index of any initial sub-region; adjusting any initial sub-region to obtain a target sub-region; and obtaining the target sub-region corresponding to each initial sub-region, obtaining the dominant frequency in each target sub-region, constructing the normal texture model, and carrying out surface defect detection on the to-be-woven material, so that the detection result of the surface defect of the textile material is improved.

The invention relates to the technical field of man-machine interaction, and discloses a multi-modal AI and emotional intention recognition three-dimensional content real-time generation method, which integrates a multi-modal AI collaborative generation module, a dynamic emotional intention recognition module, a three-dimensional content real-time generation module and a holographic software and hardware collaborative module, the method comprises the following steps: realizing an immersive holographic interaction and multi-modal AI collaborative generation module, constructing a unified semantic space by adopting CLIP + VATT, analyzing an instruction by adopting LLM to generate parameters, generating 2D content by adopting Diffusion, converting NeRF into a textured 3D model in real time, and realizing sketch / semantic driven parameterization generation by combining ControlNet and LoRA. According to the scheme of the invention, the breakthrough efficiency can be improved, and the AI driven automatic generation technology is realized; a traditional 3D content production process which needs to be completed in several days can be compressed to be completed in several minutes by means of collaborative optimization of models such as Point-E, NeRF and Diffusion and combining ControlNet structured control and LoRA low-rank fine tuning, and the modeling efficiency is integrally improved.

Embodiments of this application provide a method for generating a three-dimensional texture model of a city based on composite data, and a device. The method includes: obtaining satellite remote sensing data of a target region, where the satellite remote sensing data is used to determine a three-dimensional framework model and spatial coordinates of a to-be-measured object; obtaining video data within a preset range of the spatial coordinates of the to-be-measured object, where the video data is used to determine a surface texture of the to-be-measured object; and generating a three-dimensional texture model of the to-be-measured object based on the surface texture and the three-dimensional framework model of the to-be-measured object, where the three-dimensional texture model of the to-be-measured object is used to generate a three-dimensional texture model of the target region. The satellite remote sensing data and the video data within the preset range of the spatial coordinates of the to-be-measured object are obtained at low costs, so that costs and difficulty of constructing the three-dimensional texture model of the city are reduced, and costs and difficulty of updating the three-dimensional texture model of the city are reduced.

This application discloses a method and system for detecting defects in metal products based on image recognition. The method first performs reflection perturbation elimination processing on the surface image of the metal product to be detected to obtain a reflection elimination image; then, it acquires the surface reference texture features of the defect-free metal product, and generates a reference texture model based on texture distribution patterns, gray-level mean, and texture continuity; next, it performs defect texture gradient separation on the reflection elimination image based on the reference texture model, locates abnormal regions, and segments them to obtain suspected defect texture regions; then, it performs boundary pixel reconstruction on the suspected defect texture regions to obtain defect texture reconstruction regions; the target defect texture region is obtained through local variance enhancement and neighborhood correlation analysis; finally, it locates overexposed regions and performs gray-level inverse stretching processing, and outputs the surface defect detection results after clustering abnormal pixel group features of the preprocessed defect regions, thereby improving the accuracy and precision of surface defect detection of metal products and reducing the false detection rate.

The invention discloses a palm vein recognition method and system based on multispectral imaging and deep learning and a storage medium, and relates to the field of biological feature recognition, and the method comprises the steps: obtaining the multi-dimensional information of a palm through the synchronous collection of near-infrared, short-wave infrared and visible light wave band images, and then carrying out the adaptive weighted fusion and enhancement processing of a multispectral image, thereby achieving the recognition of the palm vein. In the core recognition step, a double-branch deep learning model is adopted, one branch extracts static structure features from the vein feature map, the other branch analyzes blood flow dynamic features from a time sequence image, and finally the static structure features and the blood flow dynamic features are fused for integrated living body detection and identity recognition. Through three-dimensional vein texture modeling and a dynamic living body detection anti-counterfeiting mechanism, faking attacks of photos, silica gel molds and the like are completely eradicated, and the problems that in a complex environment, performance of a traditional method is reduced, faking attacks are likely to happen, and speed and precision are difficult to consider at the same time are effectively solved.

The application discloses a hierarchical image rain removal method based on enhanced rain streak perception. In view of the image quality degradation problem in rainy environment, an enhanced rain streak perception feature enhancement module E-RAFEM is designed, which integrates a learnable direction filter and a rain streak texture modeling two core components, and accurately models the directionality and linear texture features of rain streaks. A Transformer-based encoder-decoder backbone network is used, and the E-RAFEM module is embedded in the first three encoding levels to form a hierarchical rain streak processing mechanism. An incremental multi-scale fusion mechanism is introduced, multi-scale features are captured through different expansion rate convolution kernels, and an incremental method is used to gradually integrate to avoid information loss. The enhanced rain streak perception network ERA-Net proposed by the application realizes high-quality recovery of images under various complex rainy conditions through accurate rain streak feature modeling and hierarchical processing strategy.

The present invention discloses a method for generating digital models for displaying tourist attractions, comprising the following steps: obtaining three-dimensional data of the tourist attraction, performing high-precision modeling on the three-dimensional data to obtain an initial three-dimensional model; based on the initial three-dimensional model, using a face reduction algorithm to reduce the geometric faces of the initial three-dimensional model to obtain a simplified three-dimensional model; setting multi-level model detail parameters according to preset display platform performance parameters, and generating multi-level models with different degrees of precision based on the simplified three-dimensional model and the multi-level model detail parameters; compressing the texture data in the multi-level model using a texture compression algorithm to obtain a compressed texture model; and determining a final three-dimensional model from the multi-level model based on the performance indicators of the compressed texture model, loading the final three-dimensional model into the memory of the display platform for real-time rendering and display. The present invention solves the problem of large data volumes and low rendering efficiency in scenic area three-dimensional models.

The invention relates to the technical field of three-dimensional modeling, and discloses a tree surface texture modeling method and system based on programmed generation, a terminal and a storage medium, and the method comprises the steps: obtaining a tree surface texture image, and constructing a digital expression model of tree texture according to the tree surface texture image; adjusting model parameters of the digital expression model based on a programmed generation tool of the tree surface textures, and generating a three-dimensional tree model of the tree surface textures with various characteristics; and outputting a three-dimensional tree model corresponding to the tree three-dimensional modeling task. Through the three-layer structure model, the original complex and difficult-to-quantify natural texture can be converted into a digital expression mode with clear hierarchy, operability and high controllability, and texture modeling and adjustment with high reality sense and high controllability can be realized by utilizing a programmed generation tool; diversified and high-detail-degree tree surface textures can be flexibly generated according to different tree species or different forms of the same tree species.

To provide a lump palpation model which allows a user to experience the simulated sensation of breast cancer lumps by gently pressing and stroking the model with fingers, and which can be handily used while taking a bath, etc.SOLUTION: A lump palpation model 1 for allowing a user to experience the simulated feel of lumps present inside a body through tactile sensation is provided, the lump palpation model comprising a main body 2 having a mounting surface 21 for mounting the model and a touching surface 20 to be touched for a simulated experience, and simulated lumps 3 simulating lumps embedded in the main body 2, and being configured to float on water. The mounting surface 21 has an adhesive surface that can be adhered to a wall or the like.SELECTED DRAWING: Figure 2

The application provides a hull surface defect detection system based on machine vision, and relates to the technical field of data processing.The system comprises an image acquisition module, an image correction module for performing illumination balancing and geometric distortion correction, a region construction module for identifying a stable region without defects, generating reference region data, including a brightness model and a texture model, a candidate generation module for detecting a region with local texture interruption or abnormal bright spots, forming candidate defect data, including pixel position and local contrast parameters, a stability determination module for performing projection matching in multiple images and simultaneously performing joint comparison with the brightness model and the texture model of the reference region data, forming real defect data and false defect data, and a result output module for generating a detection result containing defect coordinates, defect contours, image frame numbers and interference sample prompts.The application improves the accuracy of hull surface defect detection.

The learning data generation apparatus (100) includes: a 3D model acquisition unit (110) that acquires 3D model information representing a 3D model of an object; a local image acquisition unit (120) that acquires local image information representing an image region of an object captured in a captured image, i.e., a local image; a texture coordinate acquisition unit (130) that acquires 2D texture coordinates for texture mapping of the local image to the 3D model based on the local image information and the 3D model information; a rendering condition acquisition unit (140) that acquires rendering condition information representing rendering conditions, which are conditions for rendering a textured 3D model for which texture mapping of the local image to the 3D model has been performed based on the 2D texture coordinates; a 2D image acquisition unit (150) that acquires 2D image information representing a 2D image by rendering the textured 3D model based on the rendering condition information; and a learning data output unit (190) that outputs 2D image information.

The invention relates to the technical field of image processing, in particular to a thermal imaging visualization method and system for roasting process energy efficiency analysis, and the method comprises the steps: obtaining a thermal image of the surface of a roasting kiln body, and calculating a local complexity index and a local anisotropy index of each pixel point in the thermal image; extracting a multi-scale and multi-direction texture feature set of each pixel point in the thermal image; calculating a scale weight of each scale and a direction weight of each direction, and performing weighted fusion on the texture feature set to obtain a fusion feature vector of each pixel point; and establishing a normal texture model based on the fused feature vector, and calculating an abnormal score graph of the thermal image to determine an abnormal heat loss area and perform visualization. According to the technical scheme, texture changes of different scales can be effectively identified, and the detection precision of the abnormal heat loss area is improved.

The invention belongs to the technical field of image recognition, and particularly relates to a garbage classification method and system based on image enhancement, and the method comprises the steps: carrying out the preprocessing of an obtained garbage image, and sampling a reference region block according to a local color variance, so as to extract a reference color model and a reference texture model; calculating a color consistency index, a texture homology index and a structure regularity index of each pixel point; determining a self-adaptive color weight and a self-adaptive texture weight according to a color statistical characteristic and a texture distribution characteristic in the reference region block; and based on the self-adaptive color weight, the self-adaptive texture weight and a preset structure weight, carrying out weighted fusion on the three indexes to generate a material authenticity map. According to the method, the enhancement effect on the complex stained junk image can be remarkably improved by fusing the multi-dimensional features and adopting the self-adaptive weight.

The invention discloses an analysis processing method based on epidermis texture graphic data, and relates to the technical field of epidermis texture data analysis, and the method comprises the steps: carrying out the three-dimensional scanning of a target epidermis to obtain an initial texture model, connecting three adjacent surface data points based on a Delaunay triangulation algorithm to form a triangulation analysis unit, and carrying out the triangulation analysis of the target epidermis. Calculating a normal vector included angle standard deviation of adjacent triangular patches in the triangular analysis unit where the texture reference point is located as a texture direction change rate, judging whether the texture direction change rate is greater than or equal to a preset threshold value, and performing skin curvature feature extraction on the current triangular analysis unit with first analysis intensity, according to the first analysis intensity or the second analysis intensity, epidermal frequency feature extraction is carried out on the current triangular analysis unit, so that different analysis intensities are intelligently switched, the key direction of feature extraction is flexibly adjusted, it is ensured that high-precision analysis is obtained in a key complex area, meanwhile, the overall calculation burden is effectively reduced, and the processing efficiency is improved.

The present invention provides a method, device, equipment and storage medium for generating an assembled building block model. The method includes: obtaining a source picture and a model rendering picture to be pasted with a texture, and generating an initial texture rendering picture according to the source picture and the model rendering picture to be pasted with a texture through a deep learning diffusion model; taking the initial texture rendering picture as a target texture rendering picture, and performing matte extraction processing on the target texture rendering picture to obtain a matte bitmap; taking the matte bitmap as a projection texture, and performing projection mapping on the projection texture according to a preset block structure of the assembled building block model to automatically paste the texture on the assembled building block model, so as to obtain an assembled building block model with the texture pasted. This method combines deep learning technology and image processing technology to realize the full-automatic generation process from user input to the final texture model, significantly improving the efficiency and quality of building block texture design and solving the problem of time-consuming and laborious traditional manual design methods.