42 results about "Stereo reconstruction" patented technology

The invention relates to a three-dimensional reconstruction method and model, in particular to a dynamic self-adaptive multi-view three-dimensional reconstruction method and model based on double-domain information fusion, and aims to solve the problems that in the prior art, the space relation between pixels is explored by utilizing attention along a channel dimension, the self-adaptive long-range information association capability is lacked, and the space relation between pixels is not explored. According to the dynamic adaptive multi-view three-dimensional reconstruction method based on double-domain information fusion, spatial domain detail enhancement and frequency domain dynamic aggregation and enhancement are realized through a multi-scale spatial domain and frequency domain information fusion network, so that multi-scale enhancement characteristics of an input image are obtained; a multi-scale depth prediction network is constructed by adopting a coarse-to-fine framework, depth hypotheses of all scales are obtained, a cost body is generated through adaptive aggregation, finally regularization is carried out to obtain prediction depth, and the prediction depth is converted to obtain a three-dimensional point cloud file of a corresponding scene. Meanwhile, the invention provides a model capable of realizing the method.

The invention discloses a high-precision three-dimensional reconstruction method for a complex scene, and the method comprises the steps: obtaining the position and orientation of a camera from a multi-view image through the pose estimation of the polarization camera, and obtaining a normal vector of a scene surface through an MLP network; according to the method, a scene surface normal vector is used for correcting an ambiguous polarization normal vector calculated by using a polarization image, so that a prior normal vector is obtained, and the prior normal vector is input into a pre-trained improved NeuS network to provide additional constraints, so that the geometric blurring problem of a non-texture region and a mirror reflection region is relieved. And compared with depth priori, the network based on the priori normal vector can generate a smooth geometric shape, and can also avoid the problem of depth scale blur among multiple views, thereby obtaining more accurate three-dimensional information. According to the method, high-precision reconstruction can also be carried out on an object with shielding and an object with a thin structure, and better geometric reconstruction can also be carried out on a scene with sudden depth change.

The invention relates to an image three-dimensional reconstruction method, in particular to a multi-view three-dimensional reconstruction method based on frequency perception feature enhancement and cost aggregation. The objective of the invention is to overcome the defects of lack of frequency sensing capability and limited processing capability for problems of weak texture, noise, illumination variation, color distortion and the like in an existing learning-based multi-view three-dimensional reconstruction method. According to the method, multi-view three-dimensional reconstruction is realized through the steps of acquiring a multi-view image, calculating a multi-scale frequency sensing feature, calculating an initial cost body, embedding frequency information into the initial cost body, calculating depth estimation, performing back projection and the like in sequence; when multi-scale frequency sensing features are calculated, a double-branch frequency component enhancement module is arranged to process wavelet transform layer decomposition to obtain lossless approximate low-frequency components and high-frequency components of an input image, so that global consistency and local detail expression of depth estimation are improved.

The invention relates to the technical field of three-dimensional reconstruction, and provides a local feature adaptive fusion and high-fidelity new view angle synthesis method based on three-dimensional reconstruction. According to the method, a local feature module is introduced into a point cloud network, robust matching of local features is realized, a self-adaptive matching strategy is combined, high-resolution images can be efficiently processed, and the precision and stability of three-dimensional reconstruction are remarkably improved. The method is suitable for application scenes such as three-dimensional rendering, three-dimensional reconstruction and new view angle synthesis. The specific process comprises dense reconstruction and initial rendering; and carrying out missing region repairing and iterative optimization. Generating a high-precision point cloud based on an improved stereo matching algorithm, and rendering an initial image of a target view angle in combination with camera parameters; a conditional video generation model is adopted, and holes, distortion and artifacts in initial rendering are repaired; and carrying out progressive complementation on the occlusion region by using the view angle consistency constraint, and improving the reconstruction integrity by jointly optimizing the point cloud geometry and texture. According to the method, the final picture quality is remarkably improved through feature matching optimization and efficient processing capacity. Local feature branches and global context information are coordinated, and the problem of matching ambiguity in a complex scene is solved; the adaptive fusion strategy gives consideration to high-resolution image processing efficiency and detail retention; and the detail fidelity and the overall quality of the rendering result are effectively improved.

The invention discloses a photovoltaic module construction quality identification method based on computer vision and three-dimensional modeling. The method comprises the following steps: S1, collecting an original image sequence of a photovoltaic construction site and carrying out image preprocessing; s2, outputting a semantic tag graph and a two-dimensional space boundary graph through an improved BiSeNet network; s3, using a mask constraint SIFT algorithm to extract cross-view key point features, generating a dense three-dimensional point cloud according to multi-view stereo reconstruction, and outputting a structure three-dimensional geometric model; s4, mapping the semantic tag graph and the two-dimensional space boundary to the structure three-dimensional geometric model to obtain a semantic three-dimensional model; s5, performing spatial registration on the semantic three-dimensional model and the reference model, and calculating spatial error parameters of the photovoltaic module; s6, performing compliance discrimination on the spatial error parameters, and outputting a quality discrimination result; and S7, generating a construction quality evaluation report. According to the invention, automatic identification and accurate evaluation of the photovoltaic construction quality are realized, and the detection efficiency and the discrimination accuracy are improved.

The invention provides an unsupervised multi-view three-dimensional reconstruction method based on a bidirectional attention mechanism, and mainly solves the problem of low three-dimensional reconstruction quality in a weak texture region and a complex shielding scene in the prior art. The scheme comprises the following steps: 1) inputting a multi-view image and camera parameters; 2) preliminarily extracting multi-scale features of an input image through a bidirectional feature pyramid network Bi-FPN, taking the multi-scale features as input data, performing enhancement through an attention mechanism, and then calculating a fusion feature map; 3) performing multi-stage depth estimation from coarse to fine by using the fused feature map; 4) calculating an initial depth map of a data enhancement branch; 5) fusing multi-source information to optimize the initial depth map, and generating a final refined depth map; and 6) converting the refined depth map into a point cloud model to obtain a reconstruction result. According to the method, the multi-scale features of the image can be extracted and fused more efficiently, so that the three-dimensional reconstruction quality of a weak texture region and a complex shielding scene is remarkably improved.

The invention discloses an outdoor scene-oriented efficient multi-view three-dimensional reconstruction method and system, and belongs to the field of multi-view three-dimensional reconstruction, and the method specifically comprises the steps: preparing a multi-view three-dimensional reconstruction data set for model training; constructing a multi-view three-dimensional reconstruction network comprising an adaptive feature extraction converter module based on big kernel attention, a cost regularization module and a depth estimation module; performing training and fine tuning on the network model to obtain a final model; building a multi-view three-dimensional reconstruction system and equipment; according to the method, the problems of poor accuracy and low integrity of a reconstruction result when an existing multi-view three-dimensional reconstruction method faces an outdoor large-scale scene can be solved, and the training reasoning time and the calculation cost of multi-view three-dimensional reconstruction can be reduced; and an important foundation is laid for application of outdoor large-scale scene image data in the field of multi-view three-dimensional reconstruction and development of a three-dimensional reconstruction technology.

The invention discloses a flexible metal material three-dimensional length measurement system based on a rotary camera mechanism, which is used for solving the problems that the existing contact measurement is easy to cause material deformation, the non-contact measurement is poor in adaptability to high-reflection / low-texture materials and the like. Comprising a rotary camera module (an industrial camera collects images at multiple angles along a semicircular guide rail), a visual calibration module (an ArUco code and a photoelectric sensor realize pose calibration and triggering), a texture enhancement module (structured light / random noise projection optimization feature extraction), a three-dimensional reconstruction module (a multi-view three-dimensional reconstruction algorithm generates point cloud) and an error compensation module (an error compensation module). And a material perception weighted least square fitting and neural network dynamic correction error) and a control splicing module. The bending deformation error of the material is eliminated through non-contact three-dimensional measurement; in combination with texture enhancement and error compensation mechanisms, the measurement precision of high-reflection materials such as aluminum foils and stainless steel bands is improved; and a modular integrated design is adopted, and automatic production line deployment is adapted.

The invention discloses a cattle face recognition method based on dense point cloud three-dimensional reconstruction, which comprises the following steps: S1, receiving four cattle pictures from different visual angles to capture detailed characteristics of cattle, S2, converting the cattle pictures from different visual angles into 3D dense point cloud coordinates by using a 3D reconstruction technology to ensure high efficiency and accuracy of face recognition, S3, extracting constructed 3D dense point cloud characteristics, and S4, extracting the 3D dense point cloud characteristics from the 3D dense point cloud characteristics. And performing cattle face comparison. Based on the 3D reconstruction technology, a high-quality 3D cattle face model is generated through a limited number of 2D cattle face images, further the 3D features of the cattle face are deeply mined and analyzed, accurate face information comparison is achieved, and cattle identity recognition is completed. The method improves the recognition accuracy and robustness through 3D feature comparison, can be suitable for file establishment, transaction and traceability in the cattle feeding process, is mainly used for cattle identity uniqueness recognition and authentication, is also called as living asset uniqueness authentication, and is used in multiple fields such as insurance and loan in scenes. And the traditional industry is enabled by means of tools of science and technology and general finance.

The invention provides a feature pyramid network in multi-view three-dimensional reconstruction and a multi-view three-dimensional reconstruction method. Comprising an input image module, a camera parameter module, a shallow feature extraction module, a multi-scale feature extraction module, a global representation sub-network module, a position coding module, a feature fusion module, an FPN encoder module, an FPN decoder module, a matching cost body construction module, a cost body regularization module, a depth sampling range estimation module and a depth map generation module. A depth map optimization module, a point cloud generation module and a loss function module. The invention provides a feature pyramid network in multi-view three-dimensional reconstruction, which comprises the key technologies of multi-scale feature extraction, global context information extraction, feature fusion, feature pyramid construction, matching cost body construction, cost body regularization, depth sampling range estimation, depth map generation and optimization and the like. And high-efficiency and high-quality multi-view three-dimensional reconstruction is realized.

The invention discloses a multi-view three-dimensional reconstruction method based on multi-scale structure perception fusion and confidence weighted optimization, and the method specifically comprises the following steps: 1, obtaining a multi-view data set, and dividing the data set into a training set and a test set; 2, constructing a depth estimation network, wherein the depth estimation network sequentially comprises a multi-scale feature extraction module, a structure perception and attention guidance feature fusion module, a confidence coefficient weighted feature enhancement module and a multi-stage depth estimation and refinement module; step 3, training the depth estimation network by using the training set in the step 1 to obtain a trained depth estimation model; and step 4, inputting the test set constructed in the step 1 into the model trained in the step 3 to obtain a predicted depth map of each reference view. The method solves the problems that an existing reconstruction method is insufficient in detail reduction in a weak texture region and fuzzy in boundary in a shielding and illumination variation region.

A method includes capturing a first image of a surface illuminated from a first direction along an axis by a first light at a first elevation angle with other lights of the illumination system off, and capturing a second image of the surface illuminated from a second direction along the axis by a second light at a second elevation angle with the other lights off, the second direction being opposite the first direction, the second elevation angle being substantially identical to the first elevation angle. The images are processed by taking a derivative of a reflected light intensity at each of a plurality of pixel locations in the images, respectively. The respective derivatives are employed to identify a set of invalid pixels where a photometric stereo condition is not satisfied. A photometric stereo reconstruction is generated based on a set of images and the identified set of invalid pixels.

The embodiment of the invention provides a three-dimensional reconstruction method and device for a visual target, electronic equipment and a storage medium, and relates to the technical field of visual perception, and the method comprises the steps: obtaining a video sequence corresponding to an initial three-dimensional model; sampling the video sequence to obtain an original image corresponding to the initial stereoscopic model; obtaining a convolution processing network for the original image, wherein the convolution processing network at least comprises a convolution coding network and a plurality of convolution decoding networks; performing feature extraction on the original image according to the convolutional coding network to obtain coding features corresponding to the original image; each convolutional decoding network is adopted to carry out progressive decoding processing on the coding features, and attribute elements corresponding to each convolutional decoding network are obtained; according to the method, the element sequence corresponding to each attribute element is obtained, progressive three-dimensional reconstruction is performed on the initial three-dimensional model according to the element sequence, and the target three-dimensional model corresponding to the initial three-dimensional model is obtained, so that the implementation cost is reduced, and the reconstruction output effect is ensured.

The invention discloses a multi-view reconstruction method and system based on geometric perception and attention fusion. The reconstruction method comprises the following steps: designing a multi-scale feature enhancement network and a geometric perception feature fusion network; the multi-scale feature enhancement network extracts a multi-scale feature map and inputs the multi-scale feature map to a multi-stage depth estimation module to generate a preliminary depth map; inputting the initial depth map into a geometric perception feature fusion network to generate a geometric perception enhanced feature map; inputting the geometric perception enhanced feature map into a multi-stage depth estimation module, and performing iterative optimization to output a refined depth map; and training a model constructed by the network by adopting a joint loss function of a pixel-level cross entropy loss function and a depth distribution similarity loss function. According to the method, the expression ability of image features and the utilization efficiency of geometric information are effectively improved, the problem that depth estimation is difficult in a low-texture area and a structure repeated area in an existing multi-view three-dimensional reconstruction method is mainly solved, and the accuracy and integrity of three-dimensional reconstruction are remarkably improved.

Computer-implemented procedure comprising the following steps: Performing a stereo alignment on a pair of images; Straightening (204) the image pair so that epipolar lines become either horizontal or vertical; Applying (206) a stereo alignment to the straightened image pair to establish an initial correspondence between pixels from the image pair; Generating a homography matrix transformation; Defining photo consistency between a template window in a base image of the image pair and a warp window of a translated image of the image pair as an implicit function of the homography matrix transformation; Improve the photo consistency of the warp window; Generating a translated pixel from a basic pixel, wherein the generation comprises repeatedly applying (208) the homography matrix transformation to the basic pixel until the absolute value of an increment step is less than a predetermined value; Triangulating (210) correspondence points to create a three-dimensional scene; and Providing the three-dimensional scene for display.

The invention provides a multi-view three-dimensional reconstruction system for multi-stage depth estimation and propagation. Comprising an image acquisition module, a primary depth estimation module, a depth refinement module, a cross-view propagation module, a feature matching module, a noise filtering module, an abnormal value processing module, a visual angle selection module, a three-dimensional model generation module, a data preprocessing module, a visual feature enhancement module, a computing resource management module, a user interface module and a result evaluation module. The image acquisition module outputs image data to the data preprocessing module; the data preprocessing module receives the image data of the image acquisition module and outputs the preprocessed image data to the feature matching module and the primary depth estimation module. According to the multi-stage depth estimation and propagation mechanism provided by the invention, the precision and efficiency of multi-view three-dimensional reconstruction are remarkably improved, the calculation cost is reduced, and the method has a wide application prospect.

The application discloses a high-precision three-dimensional reconstruction method for a complex scene, which comprises the following steps: obtaining the position and orientation of a camera from multi-view images through polarization camera pose estimation; obtaining a scene surface normal vector through an MLP network; correcting an ambiguous polarization normal vector calculated by using a polarization image by using the scene surface normal vector, thereby obtaining a prior normal vector; inputting the prior normal vector into a pre-trained improved NeuS network to provide additional constraints, so as to alleviate the geometric blur problem of a textureless area and a mirror reflection area; and compared with depth prior, the network based on the prior normal vector can generate smooth geometry, can avoid the depth scale blur problem between multiple views, and can obtain more accurate three-dimensional information. The application can also perform high-precision reconstruction on an object with occlusion and an object with a thin structure, and can also perform better geometric reconstruction on a scene with sudden depth changes.

The application discloses an efficient multi-view stereo reconstruction method and system for an outdoor scene, and belongs to the field of multi-view three-dimensional reconstruction. The specific steps are as follows: preparing a multi-view stereo reconstruction data set for model training; constructing a multi-view stereo reconstruction network including an adaptive feature extraction converter module based on a large kernel attention, a cost regularization module and a depth estimation module; training and fine-tuning the network model to obtain a final model; and building a multi-view stereo reconstruction system and equipment. The application can solve the problems of poor reconstruction result accuracy and low completeness of existing multi-view stereo reconstruction methods when facing outdoor large-scale scenes, reduce the training and inference time and calculation cost of multi-view stereo reconstruction, and lay an important foundation for the application of outdoor large-scale scene image data in the field of multi-view three-dimensional reconstruction and the development of three-dimensional reconstruction technology.

The present invention relates to a three-dimensional reconstruction method and model, specifically to a dynamic adaptive multi-view stereo reconstruction method and model based on dual-domain information fusion. To address the deficiency in the prior art that the use of attention along the channel dimension to explore the spatial relationship between pixels lacks the ability of adaptive long-range information association, resulting in a reduction in image reconstruction performance, a dynamic adaptive multi-view stereo reconstruction method based on dual-domain information fusion in the present invention realizes the enhancement of spatial details and the dynamic aggregation and enhancement in the frequency domain through a multi-scale spatial and frequency domain information fusion network to obtain multi-scale enhanced features of the input image. Then, a multi-scale depth prediction network is constructed using a coarse-to-fine framework to obtain depth hypotheses at each scale. Subsequently, an adaptive aggregation is used to generate a cost volume, and finally, regularization is performed to obtain the predicted depth, which is then converted to obtain a three-dimensional point cloud file of the corresponding scene. Meanwhile, the present invention provides a model capable of implementing the above method.

The invention relates to the technical field of three-dimensional reconstruction, provides an image rendering technology research based on dense three-dimensional reconstruction and multi-view background fusion, integrates an image matting network structure, a depth estimation algorithm, a video diffusion model and an image splicing algorithm, and aims to realize high-quality new-view pictures. The method is particularly suitable for application in the fields of three-dimensional rendering, reconstruction, view angle synthesis and the like. According to the method, firstly, a foreground and a background in an original image group are separated by using a foreground segmentation algorithm and a depth threshold segmentation technology; secondly, converting the foreground image into a three-dimensional point set through point cloud reconstruction, and generating a rotating video sequence; meanwhile, feature matching and splicing are carried out on the background image, and a complete background image is constructed. And finally, inputting an expected camera pose, respectively extracting and fusing the foreground and the background from the video sequence and the spliced background by utilizing geometric transformation, and generating an image with a brand new view angle. The method provides a new thought for new view angle generation, and has an important value in the field of three-dimensional reconstruction.

The application discloses an artificial intelligence generated video authentication method based on multi-view consistency. The method comprises the following steps: constructing a large-scale real-world simulation video dataset, using the previous frame as a prompt to generate a future frame video with high consistency in semantics, color and physical laws, and improving data authenticity; designing a detection model based on multi-view consistency physical prior, extracting video features through real-time multi-view matching, combining a time sequence memory module to dynamically store and update inter-frame stereo reconstruction features, and using an attention mechanism to enhance long-time consistency analysis; using a multi-modal large model to generate unified prompts and label data, and improving the model generalization ability; scoring each frame feature through a scorer and globally averaging to output a video authenticity judgment result. The application uses multi-view consistency prior and memory enhancement mechanism to effectively capture the subtle defects in AI generated videos that violate the real physical laws, and significantly improves the detection robustness and accuracy of high-quality generated videos.

The present application relates to image stereo reconstruction method, specifically to multi-view stereo reconstruction method based on frequency perception feature enhancement and cost aggregation. In order to solve the problem that the existing learning-based multi-view stereo reconstruction method lacks frequency perception ability and has limited processing ability for weak texture, noise, illumination change and color distortion, the present application realizes multi-view stereo reconstruction through the steps of acquiring multi-view images, calculating multi-scale frequency perception features, calculating initial cost volume, embedding frequency information into the initial cost volume, calculating depth estimation and back projection in turn. When calculating multi-scale frequency perception features, a double-branch frequency component enhancement module is set to process the lossless approximate low-frequency component and high-frequency component of the input image decomposed by wavelet transform layer, so as to improve the global consistency and local detail expression of depth estimation.

The invention relates to the technical field of semiconductor detection, in particular to a photometric stereo method surface defect detection method, system and equipment. A corresponding partition control image acquisition device is configured to acquire a plurality of sub-images related to a to-be-detected chip, and the plurality of sub-images are subjected to stereo reconstruction to obtain a 2.5 D image related to the to-be-detected chip; and identifying the 2.5 D image through a target detection model with a coordinate attention mechanism, and identifying an object with a depth value.

This invention discloses an intelligent magnetic resonance holographic imaging method and system. The system includes a data acquisition device, an image reconstruction device, and an image display device. The data acquisition device acquires undersampled magnetic resonance data in real time through undersampled scanning and sends it to the image reconstruction device. The image reconstruction device inputs the undersampled magnetic resonance data into a deep learning model and outputs a high-dimensional stereoscopic reconstructed image. The deep learning model takes the undersampled magnetic resonance data of the sample as input and the corresponding fully sampled magnetic resonance data as output, and is obtained through self-supervised training. The image display device renders the stereoscopic reconstructed image in stereo and displays it in stereoscopic form through holographic projection. This invention enables rapid magnetic resonance imaging and real-time stereoscopic magnetic resonance image display, thereby better assisting doctors in surgical interventions and treatments.

The present invention relates to a multi-view stereo reconstruction method based on adaptive learning and aggregation, comprising the following steps: S1. Feature extraction, extracting image features from an input image and removing unimportant information from a large amount of information; S2. Matching cost construction, calculating the matching cost between each pixel in a reference camera and the corresponding matching pixel in its adjacent camera under each sampling depth assumption; S3. Matching cost regularization, denoising the cost volume in the matching cost; S4. Depth map estimation, weighting the result of the regularized matching cost using function regression to obtain an initial depth map; S5. Depth map optimization, reducing the effects of oversmoothing on the edges of the initial depth map. The present invention aims to provide a multi-view stereo reconstruction method based on adaptive learning and aggregation, which has higher reconstruction accuracy and completeness than MVSNet and significantly reduces time consumption and graphics card memory consumption.

The present invention relates to the field of image detection technology, and in particular to a method and system for detecting surface defects of a power adapter based on image processing. The method comprises the following steps: obtaining a set of surface images of a power adapter from multiple perspectives; reconstructing the surface image set of the power adapter from a stereoscopic perspective to generate a three-dimensional reconstructed image of the power adapter; hollowing out the internal point cloud of the three-dimensional reconstructed image of the power adapter to generate a three-dimensional image of the surface of the power adapter; extracting the surface circular contour of the three-dimensional image of the power adapter to obtain a surface circular contour area image of the power adapter; segmenting the surface bubble defect area of ​​the surface circular contour area image of the power adapter to generate a surface bubble area segmentation image. The present invention improves the comprehensiveness and accuracy of surface defect detection of the power adapter through multi-perspective three-dimensional reconstruction, comprehensive defect type segmentation, smoothness and structure analysis, and defect correlation analysis.

The application discloses a multi-view reconstruction method and system based on geometric perception and attention fusion, and the reconstruction method designs a multi-scale feature enhancement network and a geometric perception feature fusion network; the multi-scale feature enhancement network extracts a multi-scale feature map and inputs the multi-scale feature map into a multi-stage depth estimation module to generate an initial depth map; the initial depth map is input into the geometric perception feature fusion network to generate a geometric perception enhanced feature map; the geometric perception enhanced feature map is input into the multi-stage depth estimation module to perform iterative optimization and output a refined depth map; and a joint loss function of a pixel-level cross-entropy loss function and a depth distribution similarity loss function is used to train a model constructed by the above network. The application effectively improves the expression capability of image features and the utilization efficiency of geometric information, focuses on solving the problem that the existing multi-view stereo reconstruction method has difficulty in depth estimation in a low-texture area and a structure repeated area, and significantly improves the accuracy and integrity of three-dimensional reconstruction.

The invention provides a three-dimensional root system measurement method and system, computer equipment and a storage medium, and belongs to the field of plant growth science, and the method comprises the following steps: planting crops in different growth periods in transparent containers of different specifications, collecting images by using a root system scanning platform, generating dense point clouds through structured light projection and multi-view three-dimensional reconstruction technologies, and carrying out three-dimensional root system measurement on the dense point clouds; a three-dimensional grid model is constructed through a Poisson reconstruction algorithm, a Medial Axis Transform algorithm is adopted to extract a main root and branch paths to construct a topological structure, a time sequence corresponding relation of model topological nodes in different periods is established by means of an ICP algorithm, then configuration parameters are extracted, a trend curve is fitted, a shielding region interpolation mechanism is introduced, and a shielding region is constructed. According to the method, the early-stage model and the physiological growth function are combined for structure completion, dynamic analysis is achieved, the VTK and Matplotlib technologies are used for achieving root growth visualization, morphological structure changes of crops in different growth periods can be visually analyzed, the root growth rule can be reserved, and accurate guide parameters are provided for research.

The application discloses a kind of based on attention mechanism correction Kuroshio front stereo center line reconstruction method, including the following steps: (1) based on Soble algorithm obtains three-dimensional Kuroshio front area;(2) extract Kuroshio initial stereo center line;(3) using the characteristics of flow field and sea surface height anomaly field to remove outliers;(4) using attention mechanism to adjust correction field weight.The application first uses Soble algorithm to obtain the initial stereo center line of Kuroshio front, then uses the characteristics of flow field and sea surface height anomaly field to correct the stereo center line, and introduces attention mechanism to adjust the correction field weight, realizes the stereo reconstruction of Kuroshio front center line, and effectively describes the vertical swing characteristics of Kuroshio axis, provides an effective tool for the description of Kuroshio front three-dimensional characteristics.