459 results about "Geometric consistency" patented technology

The invention provides a news scene three-dimensional reconstruction and visualization method based on multi-source remote sensing data, and relates to the technical field of three-dimensional reconstruction, and the method comprises the steps: extracting multi-modal data feature points through employing a spatial position weighted iterative nearest point algorithm and a tensor projection algorithm; after bidirectional attention deep neural network processing and local geometric consistency constraint screening, registration multi-source remote sensing data is generated, data enhancement is carried out through a variational auto-encoder network and a generative adversarial network, the enhanced data is used for depth information generation and texture feature migration, an initial three-dimensional grid model is generated based on a Poisson reconstruction algorithm, and the initial three-dimensional grid model is constructed; and carrying out topological optimization by combining multi-view projection consistency constraint and a grid optimization algorithm to obtain an optimized three-dimensional scene model, setting a space division function, carrying out space division, constructing a grade model and a depth region, generating a scene semantic segmentation map, and updating a visual scene.

The invention provides a reconstruction method of a three-dimensional reconstruction model based on two-dimensional Gaussian splashing, which comprises the following steps: S1, carrying out sparse reconstruction on an input image sequence through a multi-view stereoscopic vision algorithm to generate an initial sparse three-dimensional point cloud and a corresponding camera pose parameter; s2, inputting an improved two-dimensional Gaussian radiation field by using the sparse three-dimensional point cloud and the camera pose as information; s3, dynamically screening a visible anchor point subset based on the current view angle parameter, and generating a rendered image through a differentiable rendering pipeline; s4, calculating a loss function of the rendering image of the training track and the input image to optimize a reconstruction scene; and S5, starting a special visualization tool, and inputting a rendering result. According to the method, by introducing a trimmable anchor point parameterization framework and a multi-scale feature fusion mechanism, light-weight and high-precision three-dimensional scene modeling is achieved, and the problems that traditional 2D Gaussian sputtering is insufficient in multi-view geometric consistency, storage overhead and weak texture region reconstruction and an existing 2D Gaussian splashing method is insufficient in self-adaptive mechanism are solved.

The invention discloses a three-dimensional Gaussian sputtering scene reconstruction method based on structure perception refined Gaussian, and aims to solve the problems of Gaussian drift, edge blur, structure artifacts and the like of a reconstruction model due to the fact that sparse point cloud contains outliers, Gaussian morphology and normal are mismatched and a multi-dimensional optimization target is lacked in an existing three-dimensional Gaussian sputtering reconstruction method. A key frame is extracted by collecting target scene video data, sparse three-dimensional point clouds are reconstructed by using an SfM algorithm, a depth map and a normal map are generated through a Lotus model, three-dimensional Gaussian distribution is initialized after the sparse point clouds are filtered, a Gaussian covariance matrix is adjusted by using a normal consistency regular term, and the sparse point clouds are extracted. And after structure attribute analysis is carried out, a comprehensive scoring function is constructed to screen Gaussian points, and finally, a combined training framework including luminosity, normal consistency and structure continuity loss is adopted to optimize and generate a three-dimensional Gaussian scene model. The method is mainly applied to the field of three-dimensional reconstruction and multi-view rendering, and scene reconstruction precision and geometric consistency can be improved.

The invention provides a scene reconstruction method based on delayed rendering and three-dimensional Gaussian. The method comprises the following steps: S1, generating initial three-dimensional point cloud data based on a multi-view image; s2, constructing a trainable structural body for three-dimensional Gaussian modeling; s3, normal initialization and residual optimization are carried out on the Gaussian ellipsoid primitives, depth consistency constraint is combined, and a differentiable and learnable normal reconstruction mechanism is realized, so that the geometric expression ability of illumination modeling is enhanced; s4, introducing a reflection training mechanism based on ambient light and a reflection direction, and generating a Gaussian attribute based on a visual angle; and S5, a final image is generated through a differentiable Gaussian sputtering rendering algorithm, and optimization is carried out through pixel loss of the final image and a real image. According to the method, the reality sense and geometric consistency of the Gaussian sputtering model under the complex illumination condition are remarkably improved, and the technical problems of unreal rendering effect, inaccurate surface normal estimation, weak propagation capability and the like of the existing three-dimensional Gaussian sputtering model under the complex illumination condition are solved.

The invention discloses a video style migration attitude detection method based on a graph neural network, which belongs to the technical field of video processing, and specifically comprises the following steps: preprocessing a video data stream; constructing a dynamic space-time association network, mapping pixels of the video frame into space-time diagram nodes, and converting the original video frame into a target artistic style by aggregating the space-time features of the nodes layer by layer; in the style migration process, a dynamic mask is generated for the area where the human body joint points are located, and the geometric consistency of the joint structures in stylized output is restrained; extracting joint point coordinates of each frame from the stylized video, constructing a space-time trajectory sequence, and generating a feature vector containing space-time dependence; performing multi-dimensional comparison on the feature vector and a preset action mode library, calculating a track offset and a mode matching degree, judging that the action is abnormal according to a threshold value, and outputting a detection result; according to the invention, the cooperative performance of style migration and anomaly detection is improved.

The invention discloses a three-dimensional human body reconstruction method based on an implicit neural network and a diffusion model, and belongs to the field of three-dimensional reconstruction. According to the invention, a diffusible density volume representation is constructed, and identity-based rendering is realized by using a diffusion model. By adopting parameterized three-dimensional distribution representation, Pareto optimum, balanced realistic rendering and feasible prediction are realized in an unbalanced optimization framework. In order to increase the rendering training speed without sacrificing the quality, mixed multi-resolution hash grid features are adopted to enhance the directional feature representation capability. Meanwhile, a multi-scale structural similarity constraint (MSSC) is provided to maintain identity distinguishability and ensure geometric consistency, so that synthesis with high calculation efficiency and satisfactory visual effect is realized. According to the method, the three-dimensional human body model can be reconstructed from a single image based on limited geometric and appearance information, the method is excellent in topology credibility and visual fidelity, and a more flexible and efficient solution is provided for an actual application scene.

A high-fidelity three-dimensional reconstruction method for a specular reflection plane comprises the steps of decomposing pixel colors into diffuse reflection and specular reflection components based on a 3D Gaussian sphere, introducing a dynamic reflection ratio parameter and a spherical harmonic function coefficient to respectively represent two reflection characteristics, and simulating light multi-reflection behaviors through weight fusion of cumulative projection and a reflection ratio map. Secondly, in combination with monocular inverse depth calibration, depth smoothing constraint of color gradient weighting and edge mutual exclusion loss, geometric consistency is enhanced, and artifacts are suppressed; a progressive multi-resolution training strategy is further adopted, low resolution is gradually optimized to complete resolution, reflection parameters are activated in stages, 3D Gaussian sphere overgrowth and floating artifacts are inhibited, and efficiency and precision are balanced. According to the method, the reconstruction fidelity of the specular reflection scene is remarkably improved while the real-time rendering advantage of the 3DGS is reserved, and the method is suitable for the high-precision modeling fields of virtual reality, augmented reality and the like.

The invention provides a multi-view three-dimensional point cloud reconstruction method and device based on DPE-SE depth estimation, and relates to the technical field of computer vision and three-dimensional reconstruction. The method comprises the following steps: acquiring multi-view image data; preprocessing the image; inputting the preprocessed image into a DPE-SE-based depth estimation model, carrying out key constraint on an edge region through a semantic edge guiding mechanism, carrying out adaptive propagation updating on a weak texture region, realizing accurate depth estimation, and generating a multi-view depth result; then geometric consistency check and multi-scale depth fusion are performed on a multi-view depth result, and a dense depth map is constructed; and finally, performing three-dimensional back projection reconstruction and point cloud optimization processing, and outputting high-quality point cloud data containing three-dimensional coordinates and confidence information. According to the method, the problems of edge mismatching and depth voids are remarkably improved in complex illumination, weak texture and shielding environments, the continuity and structural integrity of the point cloud are improved, and technical support is provided for unmanned aerial vehicle surveying and mapping, building detection and digital twin modeling.

The invention relates to a humanoid robot double-arm body operation method based on reinforcement learning, belongs to the field of robot cooperative control, and is characterized in that a strategy of trajectory block prediction and time integration fusion is used for double-arm cooperative control, and continuity and stability of double-arm operation are improved; in reinforcement learning control, a three-dimensional pose track generation and correction module is introduced, condition generation and denoising correction of a three-dimensional space are carried out on a track block layer, and geometric consistency and naturalness of a generated track are guaranteed; the invention further provides a reinforcement learning optimization framework and a simulation-reality migration process, and through system integration of reward, value guidance and migration processes, strategy deployability and safety are guaranteed; compared with the prior art, the method has the advantages that the naturalness, the collaboration and the success rate of double-arm operation can be remarkably improved, the generalization ability is high, and the good simulation-to-reality migration ability is achieved.

The invention discloses an unmanned aerial vehicle aerial photography-based unbounded scene three-dimensional reconstruction method, and relates to the technical field of image processing. According to the technical scheme, the method comprises the following steps: obtaining a multi-view image set of a target area, and constructing a spatial topology association network; image features are extracted based on a Hessian matrix and an SIFT operator, and scene partitions are generated through Euclidean distance matching; 3D Gaussian sputtering optimization is carried out on each partition, reusable 3D basic assets are synchronously generated, and the basic assets comprise structural components and texture features; based on a spatial transformation rule defined by a programmed code, carrying out dynamic combination on the basic assets to generate a hierarchical three-dimensional model; performing geometric consistency verification in a potential space through joint optimization of a diffusion model and a differentiable renderer; and assembling the optimized hierarchical model into the unbounded three-dimensional scene according to the spatial layout parameters. The method aims at solving the problems that three-dimensional modeling is high in resource consumption and insufficient in geometric accuracy in a large-scale scene.

The invention provides a three-dimensional human body posture estimation method and system based on multi-view visual information fusion and a storage medium, and the method comprises the steps: 1, designing the front half part of a model into Ender Layers with the same layer number as a Transform decoder at a multi-view feature fusion layer, carrying out the data enhancement of an input multi-view original image, and carrying out the reconstruction of the Ender Layers in the multi-view feature fusion layer; inputting the CNN Backbone with the shared weight to extract an initial feature map; 2, introducing a micro-reprojection optimization mechanism, deeply fusing the multi-view geometric consistency constraint into a model training process, and guiding the model to predict a three-dimensional attitude end to end; and step 3, constructing a dynamic projection compensation module. The method has the beneficial effects that the method is particularly suitable for capturing human body posture information in a multi-person interaction scene, the shielding problem and depth estimation ambiguity in a single view angle can be effectively overcome, and the robustness, precision and efficiency of three-dimensional human body posture estimation are remarkably improved.

The invention provides a live-action three-dimensional model quality inspection method and system fused with deep learning, and the method comprises the steps: building a live-action three-dimensional model through obtaining multispectral imaging data of a target scene; multispectral imaging data is optimized, standardized input data is generated, on the basis of the standardized input data, correlation characteristics among multispectral bands are extracted through a semantic segmentation network driven by deep learning, a pixel-level segmentation result is generated in combination with geometric structure characteristics of a live-action three-dimensional model, verification is carried out based on a preset topological continuity rule, and the segmentation result is obtained. Generating a multispectral feature associated data set, and performing quality inspection on the live-action three-dimensional model according to a comparison result of geometric feature segmentation deviations among different wavebands and a preset tolerance range to obtain a quality inspection result; according to the method, the refined quality inspection of the live-action three-dimensional model is realized, the abnormal region with the fractured or distorted geometric structure is effectively identified and positioned, and the geometric consistency and the overall quality of the live-action three-dimensional model under the complex illumination condition are improved.

The invention relates to the technical field of electronic component detection, in particular to an IC (integrated circuit) carrier plate detection method based on surface state image extraction, which comprises the following steps: acquiring a gray image, analyzing structural parameters, extracting gradient features, detecting boundary disturbance, integrating the image, calculating an abnormal score, identifying a defect position area, extracting features and outputting an identification result. According to the invention, by analyzing the structure parameters of the bonding pad in the gray level image, calculating the edge line segment, the center coordinate and the spacing, and constructing the two-dimensional coordinate system, the regional positioning reference is enabled to have geometric consistency, the coordinate mapping is combined with the gradient direction change frequency and the continuous aggregation point, the boundary disturbance identification precision is improved, and the image division is executed based on the disturbance region. According to the method, non-functional region mixing is effectively avoided, a clustering and probability model is introduced after region gray level statistics, a deviation scoring mechanism is constructed, gray level feature abnormity is accurately recognized, the discrimination capability of small-amplitude and low-contrast defects is improved, and the selectivity and target focusing performance of feature detection are enhanced.

The invention discloses a dynamic Gaussian scene reconstruction method based on depth regularization. The method comprises the following steps: step (1), initializing a group of 3D Gaussian sets by using an SfM point cloud, wherein each Gaussian body is described by parameters such as a central position and a covariance matrix; (2) introducing a control network decoupling motion and a geometric structure, performing offset calculation by using a time variable and a Gaussian center position, and controlling dynamic transformation of a Gaussian body; (3) introducing a loss function of scale-independent depth alignment based on local depth normalization and Pearson correlation; and (4) a new loss function is obtained through calculation according to the rendered image, and the Gaussian field and the control network are optimized through reverse gradient return. Experiments on dynamic scene data sets such as D-NeRF and NeRF-DS show that the method is superior to an existing method in the aspects of reconstruction quality, geometric consistency and new view rendering effect, the stability and detail fidelity of the dynamic scene are improved, and the method has important research value and application prospect.

The invention belongs to the technical field of three-dimensional scene reconstruction, and particularly discloses a multi-view three-dimensional Gaussian densification method and system for adaptive density control, and the method comprises the following steps: collecting a multi-view original image, and carrying out the preprocessing of the multi-view original image; complexity features are extracted, a pixel-level complexity heat map is generated, and a globally unified three-dimensional complexity field is constructed; performing back projection on the reconstruction residual error, high-frequency inconsistency and depth / geometric consistency cost of each view angle, generating three-dimensional error popularity, determining a candidate newly-added set and a candidate pruned set, generating a weak label to train a lightweight multilayer perceptron classifier, outputting a ternary probability corresponding to newly-added / pruned / maintained, and obtaining a new / pruned / maintained three-dimensional perceptron classifier; and performing Gaussian densification operation on the newly added region. By adopting the technical scheme, fine point adding is carried out on the complex area, effective pruning is carried out on the simple area, and meanwhile, the synthesis quality, the global consistency and the calculation efficiency of the new view angle are improved.

The invention discloses an automobile seat framework machining defect detection method based on machine vision, and particularly relates to the technical field of defect detection. The method comprises the following steps: constructing a multi-angle image acquisition and edge reflection modeling module aiming at complex defects such as weld joint pseudo soldering, microcracks, hole site deviation and collapse deformation, extracting weld joint continuity, edge integrity and hole site geometric consistency characteristics by using a deep neural network, and generating a structural feature vector; defect type recognition and credibility scoring are completed through small sample anomaly modeling and Gaussian mixture model classification, sub-pixel-level coordinate labeling of defect positions is achieved in combination with a Gaussian fitting algorithm, a defect positioning map is output, traceability analysis and severity grading are conducted based on historical data comparison, and the defect positioning accuracy is improved. The method is suitable for industrial online detection and quality closed-loop control.

The invention discloses a non-static scene reconstruction method and system based on multi-modal occlusion perception scoring. The method comprises the following steps: generating a geometric consistency distribution diagram, static feature points and geometric prior masks through three-dimensional reconstruction of a multi-view image; fusing the geometric prior mask and a semantic segmentation model to extract a semantic mask and a fused semantic feature map; guiding the image segmentation model to generate candidate masks based on static feature point positive point prompt and occlusion area negative frame prompt, and optimizing the consistency by using a grid complementary fusion method; constructing a multi-modal shielding scoring module, and fusing the multi-source features to output a binary static mask; and utilizing a static mask to constrain neural radiation field training, inhibiting dynamic interference and optimizing static scene reconstruction. According to the method, the static region is sensed cooperatively through multi-modal information, the robustness and accuracy of mask generation are improved, the interference of dynamic elements on neural radiation field modeling is effectively inhibited, and high-quality three-dimensional image reconstruction and new view synthesis of a non-static scene are realized.

The invention discloses an interactive three-dimensional Gaussian editing method and system based on three-dimensional geometry consistent attention priori. The method comprises the following steps: firstly, generating a multi-view source image through a three-dimensional Gaussian splashing technology, performing preliminary editing by using a diffusion model, and interactively selecting a key view; then, a CLIP model is adopted to screen reference views with consistent semantics, and three-dimensional geometric consistent attention priori is constructed; and finally, dynamically fusing the attention of the three-dimensional priori and the two-dimensional diffusion model through an adaptive cross-dimensional attention fusion network to realize a consistent editing result of multiple views. According to the method, user interactive key view selection, reference view semantic screening and attention fusion based on 3D constraint are introduced, so that the problems of multi-view inconsistency and local detail loss in the existing three-dimensional editing process are effectively solved, and the geometric consistency and detail reduction capability of the editing effect are remarkably improved.

The invention belongs to the technical field of three-dimensional scene reconstruction, and particularly provides a shielding object three-dimensional reconstruction method and system based on millimeter wave radar and vision fusion. The method comprises the following steps: acquiring radar point cloud data and visible light image data of a target object; performing space-time alignment on the radar point cloud data and the visible light image data, and establishing a space mapping relation of multi-modal data; generating a three-dimensional geometric structure of the shielded part through a geometric reconstruction model based on three-dimensional space information penetrating through the shielding object in the radar point cloud data; based on the context features of the visible light image data, generating surface textures of the occluded area through a texture completion model; and fusing the three-dimensional geometric structure and the surface texture, and outputting a three-dimensional reconstruction model containing complete information of the occlusion region. Geometric consistency and texture authenticity of virtual-real fusion are guaranteed through space-time alignment and multi-modal data mapping, and the reliability of three-dimensional modeling in a complex shielding scene is remarkably improved.

The invention provides a traffic scene multi-target detection method and system based on deep learning, and relates to the technical field of traffic, and the method comprises the steps: carrying out the semantic prior driven multi-scale feature extraction of a multi-frame image, and carrying out the point-by-point fusion; obtaining a motion field through optical flow estimation and feature similarity calculation, and executing motion compensation to obtain a moving target mask; obtaining a static target boundary by using boundary regression decoupling and geometric consistency constraint; and finally, moving and static target results are combined, and quadratic regression is executed based on consistency evaluation. The dynamic and static targets in the traffic scene can be effectively detected, the boundary regression precision is improved, and the false detection rate caused by shielding is reduced.

The invention belongs to the technical field of computer vision, and discloses a human motion capture method based on mask perceptual graph convolution and skeleton prior, and the method comprises the steps: obtaining an input image and a corresponding human mask; extracting features of the image and the mask and coding; constructing a mask perception graph convolutional network, constructing an adjacent matrix of the graph convolutional network by using mask information, and applying mask constraint loss to enhance the expression ability of the human body region features of the image; a skeleton prior decoupling network is constructed, skeleton and vertex information of an SMPL model is used as prior, a cross attention mechanism is combined, and multi-modal data enhancement of SMPL skeleton node features is guided through image features; and finally outputting a three-dimensional joint position coordinate and a shape grid vertex coordinate. According to the method, the local feature consistency is enhanced through mask perception image convolution, the geometric consistency is improved in combination with skeleton prior, and the precision of three-dimensional human body motion capture under monocular vision is effectively improved.

The invention discloses a mechanical arm calibration method and system based on multilayer geometric residual modeling, solves the problem that geometric structure information carried by a track is not considered in the mechanical arm calibration process in the prior art, and has the beneficial effect of improving calibration accuracy. According to the specific scheme, the mechanical arm calibration method based on multi-layer geometric residual modeling comprises the steps that the actual position of the tail end of a mechanical arm is obtained in the process that the mechanical arm moves according to a preset track; a continuous preset track is divided into a plurality of track sections with local geometric consistency, the initial theoretical position of the tail end of the mechanical arm is calculated in each track section according to the initial kinematics parameter vector, and multiple types of residual errors are constructed according to the theoretical position of the tail end of the mechanical arm and the obtained actual position of the tail end of the mechanical arm; and obtaining a global parameter increment, and combining the global parameter increment with the initial kinematics parameter vector or the kinematics parameter vector of the previous round to obtain a kinematics parameter vector of a new round of iteration.

The invention provides a robot indoor navigation method and system, and the method comprises the steps: obtaining an RGB image frame sequence collected by a camera, and estimating a camera pose and a sparse three-dimensional landmark point of an image frame through a depth block visual odometer; based on the camera pose and the sparse three-dimensional landmark point, dense geometric enhancement is carried out on the image frame through a deep neural network of a transformer architecture to obtain dense geometric prior information; training a neural radiation field according to the initial camera pose, the dense geometric prior information and the image frame data, and performing joint optimization on neural radiation field parameters and the camera pose based on luminosity consistency, geometric consistency and camera pose constraints; based on image frames collected in real time, novel view synthesis is carried out through the optimized nerve radiation field, and robot navigation is carried out based on a novel view. According to the scheme, accurate estimation of the camera pose and high-quality reconstruction of the three-dimensional scene can be realized, the computing resource overhead is low, and the real-time performance can be guaranteed.

The invention relates to the technical field of three-dimensional point cloud data processing, and discloses a model and method for forest scene airborne and ground platform laser point cloud registration, and the method comprises the following steps: carrying out the preprocessing of point cloud data; model construction, wherein tree position key point extraction, vertical layering virtual point construction and tree vertical layering aggregation descriptor construction are carried out in sequence; during vertical layering virtual point construction, the length of a predefined vertical axis is adopted, it is ensured that the vertical axes of all trees are evenly divided into N layers within the same height range, and the center point of each layer serves as a virtual construction point; feature matching: fault-tolerant matching selection, geometric consistency screening and random sampling consistency are carried out in sequence; and point cloud registration: calculating a rotation matrix based on the matching pair and carrying out ICP precise registration in sequence. The method does not depend on complete and accurate extraction of tree position key points, the constructed combination features are high in description capability, and robustness to regularly distributed woods is high.

The invention discloses a three-dimensional scene reconstruction method and system based on monocular depth estimation, and belongs to the technical field of computer vision and three-dimensional reconstruction, and the method comprises the steps: carrying out the multi-scale feature coding of a monocular RGB image through a mixed attention depth coding module, and obtaining the hierarchical depth feature representation; carrying out autoregression depth decoding through a self-adaptive edge perception depth decoding module to generate an initial depth map; a depth confidence map is calculated through a geometric consistency constraint optimization module and is fed back to a coding module for iterative optimization, and a refined depth map is output; and three-dimensional Gaussian ellipsoid scene representation is constructed through the Gaussian ellipsoid scene reconstruction module. According to the invention, high-precision depth estimation and high-quality three-dimensional reconstruction are realized by constructing a depth-coupled closed-loop cooperative system.

The invention discloses a single view reconstruction and rendering method, which belongs to the field of view reconstruction and rendering, and comprises the following steps of: firstly, generating multi-view feature representation with strong geometric consistency from a single input image by introducing an image diffusion module of a cross attention mechanism; a point cloud reconstruction module with self-attention and cross-attention is utilized, and multi-view information is fused to reconstruct an accurate three-dimensional point cloud; and finally, constructing differentiable three-dimensional Gaussian representation based on the point cloud, rendering the differentiable three-dimensional Gaussian representation, and outputting a new view angle image, a normal map and a depth map. According to the system, a staged training strategy is adopted, and a composite loss function including multi-scale bidirectional consistency smooth loss and feature consistency loss is innovatively used for optimization. According to the method, the problems of low geometric accuracy, multi-view inconsistency, detail missing and the like in single-view reconstruction are effectively solved, and the method can be widely applied to the fields of virtual reality, digital twinning, cultural heritage digitization and the like.

The invention belongs to the technical field of intelligent manufacturing, relates to the directions of numerical control machining, industrial robot trajectory control, complex curved surface path planning and the like, and provides a topology maintenance type curved surface trajectory optimization algorithm fusing surface curvature induction and multi-domain coupling optimization. The method comprises the steps of curved surface preprocessing, initial path construction, multi-domain coupling optimization, topology maintenance, trajectory output and the like. The method comprises the following steps: extracting local curvature characteristics of a free-form surface, adaptively arranging path points, constructing a multi-target optimization model containing indexes such as path uniformity, normal consistency and track fairness, and ensuring that the path direction is consistent with the normal height of the surface through an optimization process. The algorithm adopts an evolutionary optimization strategy to globally adjust the path, and introduces a topology maintenance mechanism to ensure that the original curved surface structure is not damaged in the optimization process. The method is suitable for CNC machining, industrial robot trajectory control and three-dimensional modeling, and has the advantages of being reasonable in path distribution, high in geometric consistency, stable in optimization effect and the like.

The invention discloses an underground pipeline intelligent detection and mapping method based on image recognition, and the method comprises the following steps: 1, obtaining continuous video images, associating feature matching pairs of adjacent key frames, and forming a pose parameter set; 2, inputting the key frame into an improved YOLO-World detection network, and outputting a detection result set; 3, obtaining a geometric consistency matching set according to the detection result set; 4, performing multi-view triangularization on the geometric consistent matching set to form a weight factor; 5, introducing a weight factor, and executing incremental beam adjustment optimization on the pose parameter set and the three-dimensional sparse point set to obtain a sparse semantic point cloud; and step 6, outputting an underground pipe network topology map. According to the invention, high-precision and high-robustness intelligent identification and topological mapping in a complex underground pipeline environment are realized.

The invention provides an injection molding part surface flaw detection method and device based on double-model collaboration, and the method comprises the steps: carrying out the local abnormal reflection feature analysis of a multi-view optical image collected on the surface of an injection molding part based on a first detection model, and obtaining a candidate flaw region; performing spatial positioning in the multi-view optical image based on the candidate flaw area to obtain a multi-view candidate image segment; performing surface geometric continuity analysis on the multi-view candidate image segments based on a second detection model to obtain a three-dimensional surface geometric consistency result; carrying out authenticity discrimination on the candidate flaw area based on a three-dimensional surface geometric consistency result to obtain a real flaw area; wherein the first detection model is used for capturing an optical response model of local abnormal reflection characteristics; the second detection model is a stereoscopic vision model for analyzing the geometric continuity of the multi-view lower surface. According to the invention, the false alarm rate of surface defect detection of the injection molded part under a complex surface condition is reduced.

The invention discloses a dynamic object pose estimation method and system based on multi-ArUco code confidence weighting, and provides a method which does not need a fixed calibration layout, supports random placement of multiple codes, dynamically solves a geometrical relationship between an object and a calibration code, establishes a double confidence mechanism by combining image recognition quality and geometrical consistency, and can estimate the pose of a dynamic object based on multi-ArUco code confidence weighting. And by utilizing multi-frame data fusion, the dynamic target pose can be stably estimated with high precision in complex scenes such as dynamic, inclination, shielding and the like, and accurate positioning in a dynamic environment is realized. The problems that in a dynamic environment, the ArUco code is frequently shielded, the pose is unstable, and the relative relation between the ArUco code and an object is difficult to calibrate are solved.