105 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 an editable digital human modeling method and device based on neural texture and three-dimensional Gaussian, and the method comprises the steps: carrying out the modeling of the expression, posture and appearance of a digital human through a three-dimensional Gaussian splashing model according to the motion sequence video data of a target object, and obtaining a Gaussian model of the digital human; modeling the expression, posture and appearance of the digital human through the neural texture model to obtain a neural texture model of the digital human; pruning the Gaussian model, and performing hybrid rendering according to a rendered image output by the pruned Gaussian model and a rendered image output by the neural texture model of the digital human to obtain a hybrid rendered image; and optimizing a Gaussian model of the digital human based on the mixed rendered image and the action sequence video data to obtain an editable digital human model. According to the method, the storage overhead and the structural complexity of the digital human model are reduced, and the high-frequency details and editing efficiency of the skin surface of the digital human model are improved.

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 discloses a planting plot space structure reconstruction method, system and device and a storage medium, and relates to the technical field of remote sensing application. The method comprises the following steps: acquiring a remote sensing image of a target planting area; performing partition deconstruction according to the remote sensing image to obtain a plurality of farming areas and corresponding area types; the region types comprise paddy fields, regular planting plots, fuzzy planting plots, terraced fields and garden plots; selecting a corresponding image extraction model according to the region type, and predicting the image of each farming region by using the image extraction model to obtain a predicted intensity map; the prediction intensity map comprises an edge prediction intensity map and a surface prediction intensity map; the image extraction model comprises a conventional edge model, a fine edge model and a texture model; performing grid vectorization processing on the prediction intensity map to obtain a pattern spot vector surface; and performing vector fusion processing on each pattern spot vector surface to obtain plot pattern spot data. According to the invention, the extraction precision of the planting plot can be 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 provides a rural infrastructure three-dimensional modeling method and system based on laser point cloud, and the method comprises the steps: collecting point cloud data at a rural infrastructure site through employing a standing laser scanner, and obtaining the space information of the infrastructure; preprocessing the collected point cloud data; the point cloud data is automatically segmented and processed by using a track extraction technology; performing circle fitting and automatic fine denoising on the segmented point cloud data; correcting the scanning intensity value of the point cloud based on a distance-intensity model; extracting and smoothing the central axis of the rural infrastructure, and providing a basis for the construction of a three-dimensional model; a lining plane image of the rural infrastructure is formed through a three-dimensional point cloud technology, and a basis is provided for texture mapping; rapid browsing and storage of the three-dimensional texture model of the rural infrastructure are realized, and subsequent use and analysis are facilitated. According to the invention, the acquisition and preprocessing process of the point cloud data can be optimized, the data quality is ensured, and the modeling efficiency and precision are improved.

The invention relates to the technical field of data identification, and provides an article simulation modeling method and device for AI visual identification, and the method comprises the steps: scanning an article, obtaining point cloud data, carrying out the noise reduction of the point cloud data, inputting the point cloud data into a preset point cloud segmentation model, and obtaining the segmentation point cloud data and feature point cloud data of the article, extracting geometric features of the article based on the segmentation point cloud data to obtain a geometric feature parameter set of the article, performing feature matching on the geometric feature parameter set and the feature point cloud data to obtain a simulation three-dimensional model of the article and a simulation texture model of the article, and inputting the simulation three-dimensional model and the simulation texture model into a preset AI visual recognition system, and identifying and classifying the simulation articles. By generating the simulation three-dimensional model and the simulation texture model and inputting the simulation three-dimensional model and the simulation texture model into the AI visual recognition system, the problems that in actual operation, the quality of point cloud data is prone to being affected by environmental factors, data noise is large, the modeling effect is affected, and consequently precision is insufficient or matching is inaccurate are solved.

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.

Aspects of this technical solution can obtain, according to a plurality of cameras oriented toward the surface of a three-dimensional (3D) model having a surface including a two-dimensional (2D) texture model, input according to corresponding views from the plurality of cameras of the 2D texture model on the surface of the 3D model, and generate, according to the input and according to a model configured to generate a two-dimensional (2D) image, an output including a 2D texture for the 3D model, the output responsive to receiving an indication of the 3D model and the 2D texture.

The invention belongs to the technical field of strip mine slope instability monitoring and early warning, and discloses a slope surface layer three-dimensional texture model beyond visual range imaging device and method.The slope surface layer three-dimensional texture model beyond visual range imaging device comprises a plurality of imaging systems, each imaging system comprises a beyond visual range imaging assembly, and the beyond visual range imaging assemblies are installed on a four-direction rotating electric control rack; the beyond-visual-range imaging assembly is in information interaction connection with a processing terminal; the beyond-visual-range imaging assembly comprises a long-focus lens barrel, the long-focus lens barrel is sequentially provided with a front fixing set and a rear fixing set in the light propagation direction, and a focusing module is arranged between the front fixing set and the rear fixing set. The front fixing set comprises a visible light filter and a near-infrared filter which are movably arranged. The system is simple in structure, convenient to use, high in automation degree, high in imaging precision and small in error, facilitates the construction of a three-dimensional texture model of the surface layer of the side slope, and is of great significance in knowing the state of the surface layer of the side slope, evaluating the stability of the side slope, predicting the change of the side slope and improving the monitoring efficiency.

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 three-dimensional model texture resolution consistency improving method and system based on any-scale super-division, and the method comprises the steps: dividing a three-dimensional geometric module in a grid format into a plurality of triangular surface clusters, and generating an initial texture image set; a scale factor is introduced to represent the texture resolution of the triangular surface cluster, and then a super-resolution factor of the triangular surface cluster is calculated based on the scale factor; according to the super-resolution factor, adaptively improving the resolution of a texture block corresponding to each triangular surface cluster by using a super-resolution network of any scale based on reference information; and after each texture image block completes any-scale super-resolution processing, packaging the texture image blocks after super-resolution to generate a new texture image set, and generating a final three-dimensional texture model based on the new texture image set. According to the method, the texture resolution can be improved, the consistency of the texture resolutions of the adjacent triangular surface clusters can be ensured, and the three-dimensional model with the consistent texture resolution and the vivid visual effect can be finally generated.

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.

A 3D building model generator has a server that constructs a 3D texture model for each floor in a building by extruding a 2D floor plan to 3D using user-inputted heights, and adding photos captured by a smartphone or mobile device as textures to image planes for surfaces in the 3D model. The server displays on the smartphone a floor-plan portion, or a virtual 3D display based on the camera orientation with a facing wall highlighted and confirmed by a smartphone user. The user moves mapping points overlaid upon the photo to wall corners to map the photo to the facing surface in the model. The user flags rooms and exits such as doors and elevators as walkable. The server stacks the 3D texture model for the floors and connects them into a walkable network of the rooms and exits that are flagged as walkable. Hashing removes duplicate floorplans.

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.

An embodiment of the present application discloses a method, device, computer device, and readable storage medium for generating a three-dimensional model. The method includes obtaining multiple sets of three-line array pushbroom data within a target area; extracting corresponding points between each set of the three-line array pushbroom data, and fusing the corresponding points to obtain multiple sets of fused points; fusing and connecting each set of the three-line array pushbroom data according to the fused points to obtain a corresponding stereo image pair set for the target area; extracting white film buildings corresponding to the stereo image pair set; constructing a textureless model of the target area according to multiple sets of the three-line array pushbroom data, and attaching the white film buildings to the textureless model to generate the three-dimensional model. The method for generating a three-dimensional model provided by the embodiment of the present application can improve the accuracy of model generation.

A method is performed on a movable scanner having one or more optical sensors. The method includes scanning an object having a surface using the movable scanner. The scanning generates color data from a plurality of orientations of the movable scanner relative to the object. The method also includes generating a pixel map of the surface of the object using at least the color data, the pixel map including, for each respective pixel of a plurality of pixels: a color value for a corresponding point on the surface of the object; and a value of a non-color property of the corresponding point on the surface of the object.

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.

Aspects of this technical solution can obtain, according to a plurality of cameras oriented toward the surface of a three-dimensional (3D) model having a surface including a two-dimensional (2D) texture model, input according to corresponding views from the plurality of cameras of the 2D texture model on the surface of the 3D model, and generate, according to the input and according to a model configured to generate a two-dimensional (2D) image, an output including a 2D texture for the 3D model, the output responsive to receiving an indication of the 3D model and the 2D texture.

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.