338 results about "Three dimensional surface" patented technology

The invention relates to the technical field of PCB detection, and discloses a PCB defect detection method and system, and the method comprises the steps: collecting multispectral imaging data through an image collection module, and generating an original image data set; the defect analysis server receives the synchronous imaging data to construct a three-dimensional surface topology matrix; in combination with the original image data set and the real-time imaging data, performing multi-scale decomposition on the three-dimensional surface topological matrix, extracting texture features, positioning a defect region, outputting defect type space distribution features through a layered recognition model, and updating the original image data set; and dynamically calibrating the detection parameters according to the feature categories. The system comprises an image acquisition module group, a data transmission module, a three-dimensional modeling module, a defect identification module and a parameter calibration module. According to the scheme, the accuracy, comprehensiveness and efficiency of defect detection are improved, and the detection requirements of modern PCB production are met.

The invention provides a three-dimensional micro-nano spherical multi-parameter synchronous mapping method based on a longitude and latitude type scanning track, and belongs to the technical field of micro-nano device and cell surface physical property characterization methods. In combination with an eccentric distance real-time compensation control algorithm, latitude scanning path parameters are established, online compensation of eccentric displacement is realized in the process of rotating along the latitude, and a normal bending deformation signal of a probe micro-cantilever is used as feedback through an upper computer, so that displacement control of a scanning positioning platform is realized. And thus, synchronous scanning imaging of the surface morphology and the nano-mechanical property of the sample is realized. A probe integrated with a protuberant tip structure is used as a micro-force sensor, a novel controllable vector angle single-point servo detection method is combined, a normal bending signal of the probe is used as feedback, and synchronous measurement of the three-dimensional surface topography and the three-dimensional surface mechanical property of a micro-nano sphere sample is realized along a longitude and latitude scanning path.

The invention relates to the technical field of photoetching machine calibration, and discloses a photoetching machine calibration method, device and equipment based on multi-view vision, and the method comprises the following steps: carrying out imaging and phase sensitive detection analysis on a mask plane and a wafer plane through a four-path optical beam splitting system to obtain four groups of mask-wafer initial alignment position information; performing scanning white light interference edge enhancement processing to obtain three-dimensional surface contour data; performing wavelet transform processing on the three-dimensional surface contour data, and performing dynamic registration on the four-path optical beam splitting system to obtain multi-view visual feature registration data; a comprehensive error model including mechanical errors, optical errors and environmental errors is established, real-time correction and error compensation are carried out on a six-degree-of-freedom motion platform of the photoetching machine, a multi-view vision calibration result of the photoetching machine is obtained, the influence of environmental vibration and thermal drift on calibration precision is effectively eliminated, and the calibration accuracy of the photoetching machine is improved. The optical system is ensured to be always in the optimal imaging state, and the calibration precision is improved.

The invention discloses an automatic planning method and system for a curved surface path of a grinding robot, and the method comprises the following steps: S1, constructing a to-be-machined three-dimensional curved surface model and a triangular mesh model, and relates to the technical field of automatic control of robots for surface machining in the manufacturing industry. According to the method, a complex three-dimensional surface coverage path planning problem of a workpiece is converted into two low-complexity sub-problems of key point sampling of a two-dimensional path planning domain and three-dimensional shortest path searching on a triangular mesh model, a small number of sampling points are sampled only on the two-dimensional path planning domain, and the sampling process depends on a spatial index framework; according to the method, global traversal of disordered point clouds is avoided, three-dimensional coordinates are positioned through nearest neighbor search of a KD tree algorithm, adjacent three-dimensional ordered key points are sequentially connected through a graph search algorithm, and a continuous machining path is formed, so that the operand is greatly reduced, real-time response of path planning is facilitated, and the problems that traditional online planning is long in time consumption and difficult in real-time response are solved.

The invention relates to a method and system for measuring the three-dimensional morphology of a metal hydrophobic surface based on white light microscopic interference, and belongs to the field of precision measurement, and the method comprises the steps: firstly, measuring a sample, scanning the surface of the sample, and synchronously collecting a white light interference image sequence containing surface contour information; adopting a bicubic interpolation algorithm to carry out interpolation processing on the acquired white light interferogram group, and constructing a high-resolution interference image sequence; analyzing the time-frequency domain interference signal through fast Fourier transform, effectively inhibiting noise interference in combination with a filtering technology, and accurately extracting an envelope curve of the interference signal; and carrying out Gaussian fitting on the envelope peak by adopting a least square method and positioning a peak value coordinate, and finally reconstructing a high-precision three-dimensional surface topography. According to the method, through advantage complementation of the bicubic interpolation algorithm and the fast Fourier transform method, the transverse resolution is remarkably improved while the longitudinal resolution is kept, and an effective technical means is provided for accurate characterization of the three-dimensional morphology of the metal hydrophobic surface structure.

The invention relates to the technical field of lens correction, and discloses an intelligent correction method and system for the optical performance of a functional lens, and the method comprises the steps: obtaining the original data of the lens, and carrying out the preprocessing of the original data of the lens, and obtaining the surface data of the lens; performing surface parameter calculation according to the lens surface data to obtain three-dimensional surface parameters; performing weighted residual calculation according to the three-dimensional surface parameters to obtain a residual matrix; performing spatial correlation analysis according to the three-dimensional surface parameters and the residual matrix to obtain an error region and a normal region; performing compensation analysis according to the residual matrix, the error region and the normal region to obtain a parameter compensation amount; and performing multi-objective optimization according to the parameter compensation amount to obtain an optimal compensation parameter, and correcting the lens according to the optimal compensation parameter. The method has the following effect that the correction accuracy of the optical performance of the functional lens can be improved.

The present invention relates to the field of mine surveying and mapping technology, and in particular to a mine surveying and mapping method and system based on unmanned aerial vehicle remote sensing technology, the method comprising: obtaining terrain data of a mine; generating a flight trajectory of an unmanned aerial vehicle; using an unmanned aerial vehicle to collect image data and hyperspectral data of the mine; constructing a three-dimensional surface model of the mine; obtaining geological structure data and mineral distribution data of the mine; and generating a three-dimensional structural model of the mine based on the geological structure data, mineral distribution data and the three-dimensional surface model. The present invention generates a flight trajectory of an unmanned aerial vehicle to ensure that the unmanned aerial vehicle takes photos at a reasonable flight altitude and speed in different areas, thereby reducing repeated coverage and maximizing regional coverage. By generating a three-dimensional structural model, detailed geological structure and mineral distribution data can be obtained without actually entering the mining area, greatly reducing the safety risks and economic costs that may be caused by on-site surveys.

The invention relates to the technical field of intelligent mechanical arm control, in particular to a mechanical arm grabbing method and system based on image processing, and the method comprises the steps: collecting an image of a target object needing to be grabbed by a mechanical arm through a visual sensor, carrying out the preprocessing and multi-scale decomposition of the collected image of the target object, screening a region of a potential grabbing point, and carrying out the recognition of the target object; and generating a capture point candidate area map. According to the invention, through processing and multi-scale decomposition of the target object image, noise is eliminated, an illumination area is balanced, a clearer target feature image is generated, and environmental adaptability is enhanced. And in combination with texture change detection and gradient analysis, the feature intensity and boundary characteristics of the object are accurately extracted, and more accurate grabbing point screening is realized. Furthermore, the three-dimensional surface form projection is in a two-dimensional form, and the stability and operability of the grabbing area are improved by optimizing the boundary symmetry and curvature fluctuation.

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 modeling optimization processing method and system based on a digital image, and relates to the technical field of three-dimensional modeling, and the method comprises the steps: using a lightweight Vision Transform to extract global features, using a Harris corner point to detect and recognize feature points, generating a multi-scale feature map through Laplace decomposition, generating a semantic segmentation map in combination with DeepLabv3 +, constructing a hybrid Poisson equation, and carrying out solution optimization. A smooth three-dimensional surface model is generated through a Marching-Cubes algorithm, an initial contour is adjusted, optimization is carried out in combination with a dynamic change vector, and a final optimization model is generated through completion, denoising, simplification, quantification and compression processing. According to the method, the feature extraction quality is enhanced through standardization processing and Vision Transform, the initial model precision is improved through multi-scale fusion and semantic segmentation, efficient and refined modeling is realized through mixing Poisson optimization and multi-scale adjustment, and a high-quality and low-complexity three-dimensional model is generated.

The present application relates to a geographic mosaicking method and apparatus for video images, and a computer device and a storage medium. The method comprises: acquiring real-time live streaming images of a real-world scene that are collected by a plurality of unmanned aerial vehicles, and GNSS information of the plurality of unmanned aerial vehicles; using a visual SLAM algorithm to perform input frame tracking on the real-time live streaming images, and performing pose estimation on successfully tracked input frames by combining visual trajectories and the GNSS information, so as to generate georeferenced camera poses; using the camera poses and a surface reconstruction algorithm to perform densification processing on the input frames, so as to generate a depth map of the real-world scene, and mapping the depth map into dense three-dimensional point clouds, so as to construct a three-dimensional surface model of the real-world scene; and using the camera pose and the three-dimensional surface model to perform orthorectification on the input frames, and integrating the orthorectified input frames into a global mosaicked map, so as to generate a global image of the real-world scene. By means of the embodiments of the present application, a real-time picture of a real-world scene can be quickly acquired, thereby providing robust support for a rapid emergency response in a real-world scene.

The invention belongs to the technical field of agricultural product automatic detection, and discloses an agricultural product quality identification method and system, and the method comprises the steps: synchronously obtaining a visible light image, a structured light reflection image, a hyperspectral image and other multi-mode images; constructing a three-dimensional surface model by using the structured light phase information, and mapping the multi-source data onto the three-dimensional surface model to form a unified data volume; self-adaptive fusion is carried out through a cross-modal attention network, and depth features are generated; secondly, augmenting features of rare defects in a feature space by adopting a conditional generative adversarial network to solve the problem of data scarcity; finally, a classifier is trained based on the augmented data set, high-precision recognition of the quality of the agricultural products is achieved, and the robustness and the recognition breadth of complex scenes are remarkably improved. According to the method, multi-source information is deeply integrated, the problem of data scarcity is innovatively solved, and the breadth and precision of agricultural product quality identification are remarkably improved.

The invention relates to the technical field of image data processing, in particular to a workshop filter defect detection method and system based on image processing, and the method comprises the steps: calculating the real-time rotating speed of a fan based on image analysis; dynamically adjusting the frame rate of the camera according to a rotating speed comparison result; reconstructing a three-dimensional surface model of the equipment; performing camera vibration compensation through the coding mark points; fusing the visible light image, the thermal imaging and the three-dimensional model data to generate a comprehensive diagnosis result; compared with the prior art that blade images are blurred and illumination and shadow interference cannot be accurately distinguished when a fixed frame rate camera fluctuates in rotating speed, the method has the advantages that the frame rate is dynamically adjusted by tracking the fan rotating speed deviation in real time, and multiple groups of images of each blade at the same rotating angle position are captured; the blade tip gradient vector is combined to analyze illumination shadow distribution characteristics, real defects and shadow artifacts are effectively separated, the microcrack recognition accuracy is remarkably improved, and meanwhile misjudgment caused by sudden change of environment illumination is avoided.

The application relates to the technical field of image processing, in particular to a camera-based progressive feature distribution sampling 6D pose estimation method and system, which comprises the following steps: obtaining dense pixel points by using a multilayer progressive feature sampling network of Unet++; analyzing three-dimensional model surface point features by using graph convolution to form a corresponding distribution relationship between image pixel points and surface points; calculating the probability distribution of the corresponding key of the image pixel coordinate point set features by querying the model to obtain the mask probability of the corresponding three-dimensional surface of the image pixel coordinates; calculating the best probability distribution value; scoring and voting each generated hypothesis; refining the obtained best voting score attitude hypothesis according to the surface distribution to obtain the target final attitude estimation pose. The application solves the problems that the stability and accuracy of pose estimation of a target object are not high when only RGB data is used, and the accuracy is not high when irregular features exist in three-dimensional model surface feature sampling.

The invention belongs to the technical field of bulk material scanning, and relates to a bulk material scanning method based on a laser radar sensor, which abandons a low-efficiency mode that all laser radar sensors continuously work at full power in the prior art, carries out global monitoring by setting a master scanning node, and sets a slave scanning node in a low-power-consumption mode. The method comprises the following steps: registering point cloud data collected by a main scanning node in adjacent scanning periods, generating a change heat map through voxelization differential calculation and spatial clustering, retrieving a high heat change region in the map, and aiming at the high heat change region, considering a geometrical relationship between a sensor and the region and multi-sensor visual angle complementarity. A sensor subset is selected from all slave scanning nodes to execute cooperative enhanced scanning, high-density local point clouds obtained through enhanced scanning and global point clouds of the master scanning nodes are fused, a bulk material pile three-dimensional surface model is generated, and dynamic on-demand scheduling and energy efficiency optimization of scanning resources are achieved.

The invention provides a space intelligent non-planar reflection generation method based on surface normal guidance. Extracting multi-layer visual representation from an original RGB image containing a non-planar reflection medium scene, and identifying a non-planar reflection area by combining brightness distribution of the original RGB image and a spatial symmetry relation; analyzing non-linear deformation features of textures in the non-planar reflection area, and predicting pixel-level three-dimensional surface normal vectors and confidence distribution in combination with multi-layer visual representation to construct a surface normal graph; determining a line-of-sight vector based on a camera imaging model, and executing geometrical optical mapping operation on the line-of-sight vector, the surface normal graph and the confidence distribution to construct a reflection offset field; extracting an intermediate semantic feature representation corresponding to the non-planar reflection region, and generating a pre-distortion reflection feature representation according to the reflection offset field and the intermediate semantic feature representation; and injecting the pre-distortion reflection feature representation, the multi-layer visual representation and the initial reflection area mask into a generative model for image reconstruction, and generating a non-planar reflection image.

The invention belongs to the technical field of forge piece warpage measurement, and particularly relates to a large long beam type die forging warpage measurement method and system based on three-dimensional contour scanning, and the method comprises the steps: S100, scanning a forge piece, and obtaining original point cloud data; step S200, processing the obtained original point cloud data so as to realize de-noising and main body target segmentation; step S300, accurately matching all the point cloud data on the scanning path, and splicing the point cloud data in sequence to obtain final point cloud data of the forge piece; step S400, performing surface reconstruction on the final point cloud data to generate a continuous and complete three-dimensional surface model; step S500, measuring and calculating the warping degree of the forge piece by using the three-dimensional surface model; the warping detection process can be improved from original sampling inspection to sample inspection, so that the defective rate is reduced; the forge piece scrapping risk caused by blind correction is avoided, and equipment and electric furnace energy consumption and labor cost caused by repeated correction are reduced; and the operation is automatic and convenient.

The invention belongs to the technical field of three-dimensional surface reconstruction, and particularly relates to a robust three-dimensional surface reconstruction method based on multi-scale fusion. Acquiring three-dimensional point cloud data, fitting local implicit curved surfaces of neighborhood points by adopting a weighted least square method, and generating a geometric reconstruction grid model; extracting geometric features in different receptive fields through multi-scale convolution, introducing channel attention, space attention and a gating mechanism to perform weighted fusion on the geometric features in different receptive fields, enhancing a global dependency relationship by adopting a feature mixing operation, and generating a deep learning grid model; performing double-branch fusion on the geometric reconstruction grid model and the deep learning grid model, highlighting a geometric sensitive area, and obtaining a fused enhanced feature; inputting the fused enhanced features into an implicit function decoder, and predicting a signed distance function value of each vertex; and based on the predicted signed distance function field, a contour surface extraction algorithm is adopted to generate a triangular mesh model, so that three-dimensional surface reconstruction is completed.

The invention relates to the technical field of image processing, in particular to a chip surface defect detection method based on multi-modal image fusion. The method comprises the following steps: firstly, acquiring three-dimensional surface geometric data and dynamic thermal response data of a chip; performing physical model correction on the dynamic thermal response data based on the three-dimensional surface geometric data to generate a thermal conductivity surface graph; then, guiding a scanning acoustic microscope to carry out sub-surface detection on the chip based on an abnormal region identified by the thermal conductivity surface graph so as to obtain acoustic image data, fusing the three-dimensional surface geometric data, the thermal conductivity surface graph and the acoustic image data in a three-dimensional coordinate system, and constructing a surface-sub-surface integrated three-dimensional model of the chip; then, analyzing the integrated three-dimensional model by adopting a physical information neural network embedded with physical constraints, and outputting a defect type identification result; the physical accuracy and the detection rate of chip surface defect detection can be improved.

The present invention relates to the field of PCB circuit board detection technology, and discloses a PCB circuit board defect detection method and system. The method comprises the following steps: an image acquisition module acquires multispectral imaging data to generate an original image dataset; a defect analysis server receives synchronized imaging data to construct a three-dimensional surface topology matrix; the original image dataset and real-time imaging data are combined to perform multi-scale decomposition of the three-dimensional surface topology matrix, extract texture features, locate defect areas, output spatial distribution features of defect types through a hierarchical recognition model, and update the original image dataset; and detection parameters are dynamically calibrated according to feature categories. The system includes an image acquisition module group, a data transmission module, a three-dimensional modeling module, a defect recognition module, and a parameter calibration module. This solution improves the accuracy, comprehensiveness, and efficiency of defect detection and is suitable for the detection needs of modern PCB production.

The invention discloses a burning metal particle three-dimensional surface panoramic microscopic imaging measurement method. The method comprises the following steps: collecting a dynamic panoramic microscopic image of burning metal particles; the collected panoramic microscopic image is preprocessed, the combustion metal particles are cut out independently from the panoramic microscopic image, and a binary image of the combustion metal particles and a binary image of surface oxides of the combustion metal particles are obtained; modeling combustion metal particles by using a long ellipsoid geometric model to obtain a mapping relation between the three-dimensional surface of the combustion metal particles and the two-dimensional information of the collected panoramic microscopic image; and calculating the three-dimensional surface areas of the combustion metal particles and the surface oxides thereof according to the binary image of the combustion metal particles, the binary image of the surface oxides thereof and the mapping relation. According to the measurement method, the three-dimensional surface reconstruction of the combustion metal particles can be realized, the areas of the combustion metal particles and oxides thereof can be quantitatively obtained, and the dynamic process of oxide fragments on the surfaces of the combustion metal particles can be visualized.

The invention relates to a missing information supplementary modeling method based on multi-modal fusion, and the method comprises the steps: carrying out the coding of an RGB image through color components, and obtaining a first feature; coding the point cloud image by using spatial position coding to obtain a second feature; aligning feature spaces of the first feature and the second feature, and performing feature fusion to obtain a joint feature; performing image reconstruction based on the joint features to obtain a reconstructed RGB image; frequency domain transformation is carried out on the point cloud image and the reconstructed RGB image, and color high-frequency features, color low-frequency features, point cloud high-frequency features and point cloud low-frequency features are decomposed; taking the color high-frequency feature and the point cloud high-frequency feature as a source domain, taking the color low-frequency feature and the point cloud low-frequency feature as a target domain, and aligning and fusing the source domain and the target domain to obtain a point cloud supplementary feature; and performing local geometric and color feature extraction and three-dimensional surface reconstruction on the point cloud supplementary features, and combining the extracted features and the reconstructed three-dimensional surface to obtain a three-dimensional model.

The invention provides a three-dimensional reconstruction method based on a structured Gaussian and neural kernel field, and the method comprises the steps: taking a multi-view image, internal and external parameters of a camera, and a point cloud as input, firstly constructing a structured Gaussian representation, optimizing the structured Gaussian representation, then constructing a neural kernel function field of a scene, taking a sparse voxel grid and an anchor point as a basic space structure of a kernel function, and carrying out the optimization of the neural kernel function field; and related features of the kernel function are predicted through neural network learning. According to the method, a mutual guidance mechanism between the structured Gaussian representation and the neural kernel function field is introduced to collaboratively optimize the two representations. And finally, performing efficient view rendering by using the structured Gaussian representation, and outputting a high-precision three-dimensional surface model. According to the method, high-quality rendering and reconstruction output can be realized by utilizing compactness and efficiency of structured Gaussian representation in processing a large-scale scene and combining scalability and robustness of a neural kernel function field in implicit surface learning, and the method is suitable for understanding and application of complex and large-scale three-dimensional scenes.

The invention discloses a document wrinkle repairing method based on deep learning, and relates to the technical field of document image repairing, and the method comprises the steps: obtaining a two-dimensional image and point cloud data of a to-be-processed document, and aligning each pixel point of the two-dimensional image with the point cloud data; calculating a curvature change rate of a neighborhood where each point in the point cloud data is located, and calculating to obtain a trace line according to the curvature change rate of the neighborhood where each point is located; dividing the point cloud data according to the trace line and generating a plurality of sub-surfaces, performing local affine transformation on each sub-surface to obtain a corresponding flattened sub-surface, and splicing each flattened sub-surface to obtain a three-dimensional surface model; extracting an image modal feature in the two-dimensional image, extracting a geometric modal feature of the three-dimensional surface model, and fusing the image modal feature and the geometric modal feature to obtain a fused feature; and based on the fusion features, carrying out cross-modal collaborative restoration on the two-dimensional image to obtain a target image. According to the method, the repairing effect is improved.

The invention provides a CF / PEEK milling surface roughness modeling method based on an L1-LSSVR algorithm. The method comprises the steps that S1, static milling process parameters serve as experimental variables, an experimental scheme is designed, dynamic signals in the milling process are collected in the experimental process, and after the experiment is finished, the CF / PEEK three-dimensional surface roughness is measured; s2, time domain features of the dynamic signals are extracted, and the time domain features are used for L1-LSSVR model training after being combined with static milling process parameters; s3, analyzing the correlation between the features through a Pearson's correlation coefficient, and eliminating redundant features with similar influence rules on the roughness of the three-dimensional surface; and performing feature selection by using L1 norm regularization, and screening out features having high correlation with the three-dimensional surface roughness. S4, establishing a CF / PEEK milling surface roughness model by taking the features screened in the step S3 as the input of an LSSVR algorithm and the CF / PEEK milling three-dimensional surface roughness as the output; according to the algorithm designed by the invention, modeling of the milling three-dimensional surface roughness of the CF / PEEK material can be effectively realized.

The invention discloses a rusted steel surface topography modeling method based on frequency spectrum generation, which comprises the following steps of: measuring the thickness of rusted steel by a ruler to obtain a rusted steel point position thickness data set, calculating a representative value of the measured thickness of the rusted steel, and calculating the surface topography of the rusted steel. Further correcting the manual measurement thickness representative value of the rusted steel according to the determined manual measurement error correction proportionality coefficient to obtain the corrected thickness of the rusted steel plate, and approximately evaluating the uniform corrosion degree; the method comprises the following steps: acquiring a material category of rusted steel, searching a pre-constructed non-uniform corrosion evaluation data set of various types of steel, and calculating a simulated surface curve corresponding to non-uniform corrosion of the rusted steel; and generating an artificially synthesized surface by combining the corrected thickness of the rusted steel plate and the simulated surface curve of non-uniform corrosion, and constructing a model capable of being used for finite element analysis of the tensile property of the rusted steel. According to the method, only one ruler thickness parameter is needed, the three-dimensional surface matched with actual steel corrosion can be rapidly generated, and the problem that modeling of the rusted steel surface is complicated is solved.

The invention discloses a path planning method and system of a tower crane and a storage medium, and belongs to the technical field of building construction.The method includes the steps that a modeling device is arranged on a luffing mechanism of the tower crane and used for collecting image data at a plurality of rotating positions and moving positions; according to the method, the camera internal parameters and the camera external parameters of the modeling device can be simply and efficiently determined, so that extraction matching, point cloud fusion and three-dimensional surface model conversion can be efficiently carried out on site features. The site features are identified, so that the construction path can be optimized, and the construction efficiency and the resource utilization efficiency are improved.

The invention provides a point cloud completion method and system based on a high-dimensional feature field and structured tensor decomposition, and belongs to the field of artificial intelligence. Comprising the following steps: encoding geometric information carried by sparse point clouds through a space geometric encoder, and distributing the geometric information to three mutually orthogonal two-dimensional feature planes and a three-dimensional feature grid to jointly form a high-dimensional feature field in which space local information is reserved; for any query point in the space, combining the space coordinates of the query point and the local features sampled in the high-dimensional feature field as input, driving a decoder based on structured tensor decomposition, and reconstructing the geometric attribute value of the query point; a continuous geometric field function implicitly defines a three-dimensional surface after completion, and point cloud completion is completed by sampling the function and extracting a zero contour surface of the function. The technical problems that when sparse and incomplete point cloud data are processed, due to information bottleneck and lack of geometric priori, the complementation quality is poor, and macrostructures and microscopic details are difficult to consider at the same time are solved.

The application provides a three-dimensional reconstruction method based on binocular stereo matching, and relates to the technical field of binocular stereo vision. The method combines binocular stereo matching algorithm, triangulation algorithm and surface texture mapping, and can realize surface reconstruction of scene objects in different environments. After stereo calibration is performed on a binocular camera, the internal and external parameters of the binocular camera are obtained, stereo correction is performed, then an image disparity map of the corresponding scene object is generated according to an optimized semi-global stereo matching algorithm, after the disparity information is obtained, the point cloud information converted by the object disparity is triangulated on the surface according to the triangulation algorithm, and finally three-dimensional surface reconstruction of the object is realized through the texture mapping technology. The application can reduce the influence of noise in the disparity map generation process, i.e. the semi-global stereo matching algorithm, improve the accuracy of the disparity information, and complete the three-dimensional surface reconstruction task of the object with little sacrifice of time performance.

The invention provides a passive three-dimensional reconstruction method and device based on single polarization information, and the method comprises the steps: obtaining the polarization degree and polarization normal information of each pixel point on the surface of a to-be-reconstructed target, and enabling the polarization normal information to comprise a polarization normal azimuth angle and a polarization normal zenith angle; determining prior gradient information corresponding to each pixel point according to the polarization normal zenith angle of each pixel point based on a linearization iterative algorithm; correcting polarization normal azimuth angles of the corresponding pixel points according to the prior gradient information corresponding to the pixel points; determining a real normal of the corresponding pixel point according to the polarization normal zenith angle corresponding to each pixel point and the corrected polarization normal azimuth angle; according to the real normal lines of all pixel points corresponding to the to-be-reconstructed target, the three-dimensional surface of the to-be-reconstructed target is reconstructed, the problem of polarization normal azimuth angle ambiguity is solved, and high-precision three-dimensional reconstruction based on single polarization information is realized.