59 results about "Geometric alignment" patented technology

The invention provides a camera external parameter calibration method and device based on image and point cloud matching, and the technical scheme of the invention is that a point cloud rendering view corresponding to an original point cloud is rotated, so that the point cloud rendering view and a camera image are superposed visually, and initial space association is established; and directly learning and solving accurate camera external parameters from the local relevance between the original point cloud intensity information and the image RGB information in an end-to-end manner by using a deep learning model. According to the scheme, the strict camera-radar orientation consistency requirement in a traditional method is not needed through rough matching, manual participation in the whole process of traditional calibration is replaced, transition from visual alignment to geometric alignment is achieved, and the calibration efficiency and scene adaptability are remarkably improved. Meanwhile, the scheme of the invention initiates a process of visual field cone cutting-perspective projection rasterization-intensity-RGB end-to-end matching, and compared with a traditional feature point method, the calculation complexity is greatly reduced, and the memory occupation is reduced by 80%.

The invention discloses an automatic texture color mapping method for large-scale point cloud data. The automatic texture color mapping method comprises the following steps: firstly, realizing geometric alignment of geographic texture data and point cloud data; then realizing release of intra-core buffer by means of a mass point cloud space segmentation and retrieval management technology, and segmenting the mass point cloud into a plurality of regional point cloud blocks; carrying out filtering processing on the regional point cloud blocks to filter out discrete flying spots; secondly, considering correction constraints of different texture source information to realize seamless generation of geographic texture data; secondly, efficient point cloud preliminary color assignment is achieved by means of the spatial relation between geometric mapping and local aggregation of regional point cloud blocks and geographic texture data; secondly, eliminating the texture information (color) of the wrong coloring point cloud by using the self-shielding judgment result of the point cloud; and finally, realizing spatial merging of regional point cloud block data sets by using an efficient out-of-core file merging technology. According to the method, the dependence on hardware equipment (such as a large memory and a high graphics card) is avoided, and the block processing of massive point clouds is efficiently realized.

A robot grabbing method based on a geometric topology constraint field and adaptive calibration comprises the following steps: constructing a manifold neighborhood enhancement field based on a target object prior point cloud; carrying out feature coding on an input real-time observation point cloud and a prior point cloud template, and embedding observation features into a manifold neighborhood enhancement field to realize geometric alignment; aiming at the RGB image and the real-time observation point cloud, establishing a preprocessing and symmetry sensing mechanism of a multi-modal feature; constructing a geometric feature re-calibration module, performing adaptive weighting on feature response through a channel attention and space attention mechanism, and performing adaptive calibration on deviation between observation features and prior manifold features; the 6D pose parameters of the target object are regressed based on the re-calibrated global features, and a corresponding geometric consistency measurement value is output; and according to the consistency measurement result, the pose result is subjected to iterative correction in the reasoning stage, and a robot grabbing instruction is output. According to the method, the space postures of different objects can be accurately modeled, and the grabbing precision of the robot in a complex environment is improved.

The invention relates to the technical field of multi-camera image processing and computational photography, and discloses an image processing method and system based on four-camera cross-focal-length continuous zooming fusion. The image processing method comprises the steps of system initialization, image acquisition, zoom routing, automatic ROI extraction and tracking, field-of-view cutting and geometric alignment, image fusion and binocular depth recognition. Through systematized multi-camera collaborative design, an innovative mechanism is introduced in key links such as zoom routing, geometric alignment, image fusion and depth recognition, smooth zoom, space consistency, detail fidelity, power consumption optimization and high-quality 3D perception are realized, the imaging quality is improved through the effects, the application scene is expanded, and the application prospect is wide. And a comprehensive solution is provided for mobile photography, AR and intelligent visual systems.

The invention relates to a multi-modal data fusion method, system and device and a medium. The method comprises the following steps: acquiring synchronous RGB (Red, Green and Blue) images and laser radar point cloud data; geometric alignment is carried out on the point cloud data, and a mapping relation between the point cloud data and image pixels is established; respectively extracting two-dimensional visual features of the image and three-dimensional geometric features of the point cloud based on the mapping relation, and re-projecting the three-dimensional features to a two-dimensional space aligned with the visual features; and finally, dynamic weighted fusion is carried out on the two types of features through an adaptive attention mechanism, and a multi-modal fusion feature map is generated. By adopting the method, the fusion weight can be automatically adjusted according to the environment change, and the robustness and accuracy of a sensing system in a complex scene are effectively improved.

The invention provides a Riemannian graph word segmentation device for structure knowledge migration, which belongs to the field of graph basic models, and comprises a geometric vocabulary sampling module used for sampling input graph data to obtain geometric vocabularies; the geometric vocabulary encoding module is used for respectively mapping geometric vocabularies into corresponding constant curvature Riemannian spaces for coordinate encoding according to different structural modes of the geometric vocabularies to obtain space coordinates corresponding to the structural modes of the geometric vocabularies, discretization is carried out by using the Riemannian quantization module to obtain quantization marks, and finally, the geometric alignment decoding module is used for decoding the geometric vocabularies. And fusing the quantitative marks in different Riemannian spaces to obtain a unified graph structure representation capable of supporting structure knowledge migration. The problems that in the prior art, due to the fact that a curvature selection mechanism of a local form of a graph structure is lacked, a word segmentation device cannot dynamically adapt to geometric characteristics of the structure, and structural representation aliasing exists, the graph structure coding accuracy is insufficient, and the cross-domain knowledge migration generalization ability is low are solved.

The invention discloses a welding seam welded area segmentation method and system based on a semantic segmentation network and depth geometric constraint, and relates to the technical field of welding seam welded area extraction, and the method comprises the steps: obtaining an RGB image and a depth image of a to-be-welded part; acquiring a rectangular detection frame and a preliminary mask for marking a welded area of the weld joint; constructing an energy model; performing global pixel-level optimization on the preliminary mask by adopting an energy model to determine a mask boundary, and obtaining an optimized mask; calculating to obtain a gradient field, a normal direction and a local curvature of the depth image in a boundary neighborhood, and interpolating the depth change curve along the normal direction to obtain an interpolation curve; and performing depth interpolation processing on the optimized mask by utilizing the interpolation curve to obtain three-dimensional boundary information of the welding seam. According to the method provided by the invention, three aspects of feature expression, geometric alignment and engineering availability are considered at the same time, and a complete boundary inference process from network coarse segmentation to deep geometric fine positioning is realized.

The invention discloses a short-focus projection image correction optimization system based on artificial intelligence, and the system comprises an image collection and structure modeling module which is used for generating a structure perception feature map; the structure guide matching module is used for constructing a stable matching subset and executing matching point screening; the dense matching and geometric correction module is used for outputting a coarse correction image; the structure residual error fine tuning module is used for constructing a structure residual error image and inputting the structure residual error image into the local fine tuning sub-network, guiding the model to carry out fine-grained correction on a structure error region, and generating a final correction image; the structure consistency evaluation module is used for evaluating key point deviation, edge direction similarity and geometric alignment degree; the correction convergence judgment module is used for tracking the correction score change; and the correction result output module is used for outputting a final correction image. The method has the advantages of high precision, strong robustness and iterative optimization, and is suitable for image geometric alignment and enhancement requirements in complex optical projection scenes such as smart classrooms, interactive display, immersive projection and the like.

The invention discloses a positioning method for generating a pseudo LiDAR descriptor for cross-modal matching based on visual depth estimation, and the method comprises the following stages: in an offline training stage, obtaining synchronous multi-modal data, inputting a camera image into a pre-trained monocular depth estimation network, generating a dense depth map, and carrying out the alignment; training a global descriptor generation network with weight sharing; a laser LiDAR global descriptor database is constructed; an on-line positioning stage: acquiring a surrounding environment image; inputting the image into a pre-trained monocular depth estimation network to generate dense depth map alignment; inputting a global descriptor generation network, and generating a visual global descriptor; and performing cross-modal descriptor retrieval and matching. Generating a geometrically aligned pseudo LiDAR depth map through monocular depth estimation, and realizing cross-modal matching with a real LiDAR map; the system hardware threshold is obviously reduced; introducing a triple supervision mechanism based on geometric overlapping degree and a yaw angle invariant descriptor network of weight sharing; and a modal gap between the vision and the LiDAR is bridged.

The invention relates to a three-dimensional visual synthesis system based on heterogeneous collaboration and screen alignment fusion, which is characterized in that a master control system generates complete viewpoint configuration information of a current scene acquired by an image acquisition device, and the complete viewpoint configuration information is used as a single data source and is simultaneously distributed to a graphic processing unit and a neural rendering coprocessor of the master control system; synchronous starting and parallel execution of the two heterogeneous rendering pipelines are achieved. And in combination with a subsequent screen alignment fusion mechanism, an end-to-end collaboration framework with low delay, low coupling and high coordination is constructed, and the key problem of real-time integration of high-fidelity contents is effectively solved. On the premise of not depending on a traditional deep buffer area or geometric alignment, high-fidelity visual content generated by special hardware acceleration and a dynamic environment of a main control system are efficiently fused, and complex effects such as lightweight asynchronous communication and real-time shadow are supported. Therefore, multiple bottlenecks in the aspects of real-time performance, compatibility and engineering deployment in the prior art are broken through.

The invention discloses an image restoration method, which comprises the following steps: acquiring a target image, a reference image set and a boundary intensity image, and extracting target features, a reference feature list and boundary features; based on the extracted features, performing similarity modeling by using a learnable spiral path to generate a similarity model result; based on a similarity model result, performing geometric alignment and double-domain fusion on the reference feature list to generate initial fusion features and alignment parameters; calculating an error mode pattern based on the initial fusion features; executing closed-loop feedback according to the error mode pattern, and iteratively updating to generate a final fusion feature; and decoding the final fusion feature to obtain a repaired image. Through spiral sampling of boundary perception, non-folding alignment and closed-loop feedback, the structure consistency and detail fidelity of the repaired image under non-rigid deformation are improved.

The invention discloses a dynamic shelter restoration method and system based on continuous streetscape panoramic images, and the method comprises the steps: firstly obtaining a to-be-restored target panoramic image A and a to-be-restored reference panoramic image B, and generating an original pixel-level shelter mask; secondly, extracting matching points among the panoramic images, realizing cross-view geometric alignment of the images, and obtaining a target perspective view C and a reference perspective view D through perspective re-projection; and then inputting the target perspective view C and the reference perspective view D into a three-dimensional reconstruction framework, and performing three-dimensional point cloud reimaging under the camera pose of the target perspective view C by using a depth inspection mechanism. And finally, carrying out image restoration on a three-dimensional point cloud re-imaging result, restoring to a panoramic coordinate system, splicing with an original panoramic image, and outputting a shielding-free panoramic image. According to the method, it is ensured that the repairing result conforms to the authenticity and geometric consistency of the geographic space, and the problems of overlapping conflicts and visual tearing during multi-view projection fusion are effectively solved.

This invention discloses a 3D defect detection method, system, and device based on typical geometric alignment and multi-view adaptive rendering, comprising the following steps: S1, acquiring the initial 3D point cloud of the object to be detected, and performing unsupervised feature analysis on the initial 3D point cloud through typical geometric alignment to obtain a standardized aligned point cloud of the object to be detected; S2, converting the standardized aligned point cloud of the object to be detected into a set of geometric feature maps from multiple camera views; S3, inputting the geometric feature maps of the object to be detected from the camera views into a general 2D image anomaly detection network, outputting point-level anomaly scores, and then back-projecting them onto the corresponding surface points of the standardized aligned point cloud to obtain object-level anomaly indicators for 3D defects. This invention does not rely on the geometric structure and observation posture of the object to be detected, and can achieve robust anomaly defect detection on unaligned object 3D point clouds, achieving high-precision point-level anomaly localization.

The invention belongs to the field of three-dimensional scene understanding, and provides a weak supervision three-dimensional target detection method based on multi-signal decoupling. According to the method, a multi-signal decoupling learning framework is constructed, and the position, the size and the orientation of a three-dimensional bounding box are respectively supervised by utilizing two-dimensional target frame constraint, category size priori generated by a language model and point cloud geometric information. The method specifically comprises a centrality-enhanced projection constraint module used for optimizing target positioning, a semantic prior anchoring module used for guiding size regression, a rotation consistency regularization module used for improving orientation discrimination capability, and an adversarial geometric alignment module used for dynamically adjusting a three-dimensional frame boundary. All the modules are trained cooperatively, a high-quality pseudo label can be generated for detector training without three-dimensional labeling, and finally the high-precision weak supervision three-dimensional target detection method is realized.

According to the multi-task processing model learning method and the multi-task processing execution method using a machine learning model learned based on the method provided by the embodiment of the present invention, multi-tasks for performing output based on a plurality of domains can be processed, and the multi-task model learning method and the multi-task processing execution method using the machine learning model learned based on the method can be provided. According to the multi-task processing model learning method, knowledge data scattered in a potential space according to each task are mutually migrated and learned through geometric alignment in the comprehensive potential space, and the multi-task processing execution method of a machine learning model learned based on the method is utilized.

The application relates to the technical field of image processing, and discloses a multispectral fusion imaging camera module and an image processing method, which comprises the following steps: performing spatial mapping on infrared imaging images and near-infrared imaging images based on the spatial arrangement order of corner points and the direction of continuous edge broken lines; determining a spectral corresponding structure according to the light and dark trend changes of the same scene in three imaging channels; performing regional division on a geometric alignment result based on the spectral corresponding structure; performing direction consistency correction on geometric edges in a visible light imaging image based on a dominant contour line; performing extensibility superposition on texture lines in the spatially mapped near-infrared imaging image based on the texture direction of a texture detail area; performing superposition on the brightness levels of the visible light imaging image based on the brightness extension direction of a background smooth area; and performing connectivity checking on the boundary direction, the texture direction and the brightness extension direction. The application ensures the reliability of multispectral image fusion.

This invention discloses a scale-redistribution-based top-view fisheye pedestrian detection method, belonging to the field of target detection technology. In the feature generation stage, this invention employs frequency-preserving downsampling, concatenating low-frequency contours and high-frequency textures after sub-pixel rearrangement and wavelet decomposition in the channel to achieve spatial depth swapping. In the feature fusion stage, it increases the participation ratio of high-resolution features through adaptive upsampling for geometric alignment. In the spatial computation stage, it divides features into high-response and low-response regions by learning a spatial weight map, performing focused convolution only on high-response regions. In the optimization stage, it constructs a joint regression mechanism, jointly modeling overlap consistency and distribution distance, adjusting sample gradient contributions based on detection difficulty, and controlling the angle supervision intensity according to the target aspect ratio, forming a scale redistribution that spans multiple stages. This invention achieves stable detection of small-scale and distorted targets in top-view fisheye images, improving the detection consistency between edge and dense regions.

This invention relates to the field of automated robot assembly technology, specifically a method and system for robot assembly tasks based on 3D vision. The method includes the following steps: Step 1: Workpiece surface scanning: Scan the workpiece surface, construct a 3D model of the workpiece surface, determine each measurement point n, and construct a point cloud based on the depth measurement values ​​of the measurement points; For the first time, the ICP algorithm is used to solve the robot assembly task by employing a low-cost sensor, that is, the cost function is iteratively minimized based on the estimation of the point correspondence between two point clouds, making the process robust to incorrect geometric alignment. In the subsequent search phase, the Lissajous function is used for surface sliding and admittance control, enabling the robot to complete the assembly task under the condition of random position errors.

This invention discloses a sparse-view 3D reconstruction method based on block-based geometric alignment and texture decoupling, belonging to the field of computer vision and 3D reconstruction technology. The method includes: acquiring RGB images and prior depth maps from a sparse viewpoint, and initializing a 3D Gaussian sputtering model; eliminating monocular depth local scale drift through block-based local alignment and constructing a second-order normal loss to constrain geometric consistency; achieving adaptive smoothing regularization based on texture-structure decoupling weights guided by normal gradients; generating artifact masks through depth residual analysis, applying soft penalties and performing hard pruning on anomalous Gaussian primitives; jointly optimizing color reprojection loss and geometric, smoothing, and penalty losses, iteratively updating Gaussian parameters, and achieving high-fidelity rendering of the new viewpoint. This invention employs the aforementioned sparse-view 3D reconstruction method based on block-based geometric alignment and texture decoupling, solving the problems of geometric instability, texture-structure coupling interference, and floating artifacts under sparse viewpoints.

This invention discloses an image restoration method, comprising: acquiring a target image, a set of reference images, and a boundary intensity map; extracting target features, a list of reference features, and boundary features; based on the extracted features, performing similarity modeling using a learnable spiral path to generate a similarity model result; based on the similarity model result, performing geometric alignment and dual-domain fusion on the list of reference features to generate initial fusion features and alignment parameters; calculating an error pattern map based on the initial fusion features; performing closed-loop feedback according to the error pattern map to iteratively update and generate final fusion features; and decoding the final fusion features to obtain the restored image. This invention improves the structural consistency and detail fidelity of the restored image under non-rigid deformation through boundary-aware spiral sampling, non-folded alignment, and closed-loop feedback.

The invention discloses a multi-station integrated video monitoring and personnel behavior intelligent analysis system, relates to the technical field of scene monitoring, solves the problem that common areas among probes are difficult to accurately identify due to dependence on manual calibration or simple geometric alignment, and aims at complex layout of different stations. By dynamically adjusting the focal length of the probe and screening the optimal frame combination, the system maximizes the excavation of effective information of the intersection area, even if the monitoring range of the probe has deviation, it can still be ensured that no monitoring blind area exists in the key area through common area identification; the image splicing processing end further improves the visual consistency of the spliced image based on a weight calibration mechanism of a pixel ratio, and provides a high-quality global visual basis for subsequent personnel behavior analysis.

The application discloses a kind of dynamic shelter repair method and system based on continuous street view panoramic image, the method is first obtained target panoramic image A and reference panoramic image B to be repaired, generate original pixel-level shelter mask.Secondly, the matching points between panoramic images are extracted, the geometric alignment of image across view angle is realized, and the target perspective C and reference perspective D are obtained by perspective re-projection.Then, the target perspective C and reference perspective D are input into the three-dimensional reconstruction framework, and the three-dimensional point cloud is re-imaged using the depth checking mechanism under the camera pose of the target perspective C.Finally, the image restoration result of three-dimensional point cloud re-imaging is restored to the panoramic coordinate system, and spliced with the original panoramic image, and the de-shelter panoramic image is output.The application ensures that the repair result conforms to the reality and geometric consistency of geographic space, effectively solves the overlapping conflict and visual tearing problem when multi-view projection fusion.

The application discloses a guiding auxiliary device and a control system thereof, and belongs to the technical field of feature registration. A target object surface model is constructed through multi-model access and three-dimensional point cloud reconstruction. The registration problem caused by the difference between global and local scales is solved by using curvature feature matching and potential area screening. A closed operation area is formed through edge point mapping, and precise spatial alignment of the design model and the actual scene is realized by combining centroid deviation adjustment. Key points of equipment positioning are planned based on a rigid transformation matrix, ensuring geometric alignment of the operation end and the functional feature points, and a collision-free path is generated through safety verification. With the aid of light source visualization guidance and real-time deviation monitoring, dynamic feedback of the operation process is realized. After operation, effect evaluation is performed through comparison of the geometric features of the functional points, and global reset is avoided through local correction. The device operation precision and safety in the precise medical implantation scene are effectively improved.

The present application relates to the technical field of image processing, and discloses a kind of self-adapting splicing method and system for scratch image.The method includes by comparing the matching score of overlapping area in different directions, automatically select the optimal splicing direction;Along the direction, overlapping area is divided into multiple calculation windows, and the vertical offset of each window is obtained using hybrid registration method, and the global offset is obtained by weighted fusion;In continuous splicing, the difference between current offset and historical value is monitored in real time, and if it exceeds the threshold, trigger the template matching recalculation and correction based on global area to block error accumulation;Finally, geometric alignment and gradual fusion are performed according to the corrected offset.The method realizes the adaptive determination of splicing direction and real-time error correction of splicing process, and improves the robustness and overall accuracy of scratch image splicing under complex conditions.

The high dynamic range imaging network method based on pyramid structure spatial feature transformation is based on three sub-networks: a deformable convolutional alignment network, a PSFT conditional network, and a fusion network of deformable convolutional residual dense blocks. The process is as follows: 1) The exposure of the input image is aligned using gamma correction, and then all images with and without exposure alignment are input into two convolutional layers of the deformable convolutional alignment network with shared offsets to obtain preliminary image features; 2) The exposure-aligned image features are geometrically aligned in the deformable convolutional alignment network with shared offsets, and the offset obtained in the geometric alignment process is also applied to the LDR image features without exposure alignment for geometric alignment; only gamma correction is used to align the exposure; 3) The geometrically aligned LDR image features without exposure alignment are input into the PSFT-based conditional network to obtain optimized features and obtain a high dynamic range image.

This application provides a method, device, equipment, and medium for 360° high-precision docking guidance of unmanned vehicles based on surround-view vision. The method includes: acquiring a multi-channel surround-view image sequence and a target docking reference space map; performing distortion correction processing according to the inverse perspective spatial transformation relationship set determined by camera intrinsic and extrinsic parameters, and projecting it into a three-dimensional world coordinate system to generate a panoramic surround-view bird's-eye view feature tensor; performing feature matching processing based on spatial geometric alignment conditions to extract a multi-dimensional spatial offset vector set; and converting the multi-dimensional spatial offset vector set into dynamic docking control commands based on kinematic constraint parameters to control the unmanned vehicle chassis execution unit to perform docking guidance operations. This application, by combining panoramic surround-view feature reconstruction with kinematic constraints, alleviates the defects of local perception blind spots, significantly improving the pose alignment accuracy and control smoothness of unmanned vehicle docking.

This invention discloses a multi-site integrated video surveillance and intelligent personnel behavior analysis system. This invention relates to the field of scene monitoring technology and solves the problem that relying on manual calibration or simple geometric alignment makes it difficult to accurately identify common areas between cameras. Addressing the complex layout of different sites, the system dynamically adjusts the camera focal length and selects the optimal frame combination to maximize the extraction of effective information from intersecting areas. Even when there are deviations in the camera's monitoring range, it can still ensure that there are no blind spots in key areas through common area identification. The image stitching processing end uses a pixel ratio-based weight calibration mechanism to further improve the visual consistency of the stitched images, providing a high-quality global visual foundation for subsequent personnel behavior analysis.

A large-range multi-resolution map construction method based on air-ground view cross matching mainly comprises the following steps: respectively transforming an air unmanned aerial vehicle image and an unmanned vehicle ground image to obtain an air polar coordinate image and a ground aerial view image; performing feature extraction on the aerial unmanned aerial vehicle image, the unmanned vehicle ground image, the aerial polar coordinate image and the ground aerial view image by adopting a deep learning-based multi-domain feature extraction method; geometric alignment is carried out on the extracted features, and a cross matching fusion result of the unmanned aerial vehicle aerial image information and the unmanned vehicle ground image information is obtained through optimization; on the basis of matching and fusion of aerial image information of the unmanned aerial vehicle and ground image information of the unmanned aerial vehicle, three-dimensional point cloud modeling information of a local area of the ground and operation load sensing information of the unmanned aerial vehicle are fused, and a large-range multi-resolution map is generated. According to the method, the sensing information of the ground unmanned vehicle and the air unmanned aerial vehicle can be matched and fused in a cross-domain manner, and a large-range multi-resolution map is constructed.

The invention discloses an RIS-SM system blind detection method and system based on structure mapping prototype clustering. Relates to the technical field of wireless communication. The method comprises the following steps: acquiring a signal sample set, system parameters and a constellation geometric mapping set of an RIS-SM system; establishing a structured constellation mapping library according to the constellation geometric mapping set, and defining a linear geometric mapping set; deriving an initial position of the center of each space prototype by using system parameters, statistical characteristics of an RIS channel and a linear geometric mapping set; transforming the space prototype center by using the linear geometric mapping set to generate a complete structured cluster center; and performing sample distribution and inverse geometric alignment updating on the structured cluster center to realize blind detection of the system. The method does not need priori channel information, reduces the calculation complexity, improves the detection precision and system performance, enhances the anti-noise capability, and is suitable for a complex communication environment.

The application discloses a medical device visual positioning and trajectory tracking method and system, specifically comprising: collecting UWB anchor point and device tag ranging and CIR data, combining a motion model to complete position prior prediction; constructing a channel quality factor based on a first path peak energy ratio and kurtosis to obtain an initial ranging weight; generating a multi-anchor subset solution candidate position and calculating geometric residuals, fusing position deviations and residuals to obtain a space-time geometric alignment score, and removing abnormal subsets and abnormal ranging according to the score; updating the data-driven filter to output the accurate position of the device; rendering the position sequence into a trajectory on a three-dimensional interface, and superimposing and displaying a covariance into a three-dimensional confidence ellipsoid to intuitively represent the positioning uncertainty, and realizing medical device positioning and visual supervision.