325 results about "Point pair" patented technology

The invention provides a panoramic image intelligent splicing method based on context semantics. The method comprises the following steps: firstly, obtaining and preprocessing a sequence image; extracting a depth feature map of the image by using a pre-trained depth network; performing semantic-guided feature matching and image alignment based on the feature map; an optimal suture line is generated according to the semantic region boundary, and fusion is carried out by adopting a semantic weighted multi-band fusion algorithm; and finally, detecting and repairing the semantic inconsistent region to ensure the semantic coherence of the panoramic image. According to the method, matching is carried out by using the high-dimensional feature tensor rich in semantic information extracted by the deep neural network, the problem of feature matching ambiguity caused by repeated textures and weak texture regions such as sky or white walls is solved, the semantic information is used as a strong constraint, regions with similar appearances but different semantics can be effectively distinguished, and the accuracy of feature matching is improved. Therefore, correct matching point pairs with consistent semantics are screened out from massive candidates, and the matching accuracy is greatly improved.

The invention relates to the technical field of multi-view three-dimensional reconstruction, and discloses a multi-view three-dimensional reconstruction method, device and equipment and a storage medium, and the method comprises the steps: obtaining a multi-view image, generating a diffusion feature map for the multi-view image through a diffusion model, carrying out the feature matching according to the diffusion feature map, and generating a matching point pair; calculating the pose of a camera through a Colmap algorithm according to the matching point pairs, extracting the deep features of the multi-view image by using a convolutional neural network to obtain a deep feature map, projecting the deep feature map to a three-dimensional space according to the pose of the camera to generate a preliminary three-dimensional feature map, and extracting the preliminary three-dimensional feature map according to the preliminary three-dimensional feature map. Fusing the preliminary three-dimensional feature map by adopting a U-Net network model to obtain a fused three-dimensional feature map, generating a three-dimensional voxel grid according to the fused three-dimensional feature map, generating a three-dimensional model according to the three-dimensional voxel grid, and finally outputting a globally optimal solution to improve generalization ability and precision.

The invention discloses an underwater target identification and positioning method based on fusion of a physical model and deep learning, and belongs to the technical field of computer vision, and the method comprises the steps: carrying out the transmissivity estimation and physical restoration of a collected underwater monocular image according to an underwater light propagation model, and carrying out the image correction; key feature points are extracted, and high-quality matching point pairs are screened in combination with the joint similarity; further deriving a basic matrix through the high-quality matching point pairs meeting the epipolar geometric constraint relation, solving an essential matrix, and obtaining relative attitude parameters between the cameras in combination with weighted re-projection-LM optimization; and finally, refraction correction triangulation is carried out according to the Snell's law, pixel-level fusion is carried out after scale normalization and space alignment are carried out on the refraction correction triangulation and dense depth output by the MiDaS, three-dimensional space coordinates of the target are inverted, and high-precision recognition and positioning of the underwater target are achieved. The system is light in structure, efficient in calculation, suitable for being integrated on various underwater autonomous or remote control robot platforms and used for tasks such as target recognition, tracking and positioning.

The embodiment of the invention provides a three-dimensional space change detection method and device, equipment and a storage medium, and relates to the field of digital image processing. The method comprises the following steps: acquiring image sequences of the same area shot by the unmanned aerial vehicle in two periods; for each image sequence, performing point cloud reconstruction on the region based on the image sequence to obtain the group of point cloud data; point cloud registration is carried out according to the similarity between the feature descriptors of all the points in the two sets of point cloud data, a matching point pair set comprising a plurality of matching point pairs is obtained, and two points in each matching point pair belong to two pieces of point cloud data and are closest to each other; and for each matching point pair, geometric information of two points in the matching point pair in respective point cloud data is acquired, and if it is determined that the difference between the two pieces of geometric information meets a preset difference condition, it is determined that the corresponding position of the matching point pair in the region is changed. According to the embodiment of the invention, the problem of insufficient spatial change detection precision in a complex scene is solved.

The application is suitable for the field of visual measurement, and provides a target, an information detection method and device, a terminal and a storage medium, wherein the method comprises: controlling a camera to take a picture of the target to obtain a first imaging image; based on the first imaging image, extracting a first pixel point pair with the farthest relative distance on the inner circumference of a circular ring, a second pixel point pair with the farthest relative distance on the outer circumference of the circular ring, a center pixel point of three circles and a vertex pixel point of a first triangle; based on the first pixel point pair and the second pixel point pair, determining a first straight line where the long diameter of the circular ring in the first imaging image is located; based on the center pixel point and the vertex pixel point, determining a second straight line where the bottom side of the first triangle in the first imaging image is located; and determining the intersection of the first straight line and the second straight line as the imaging center point of the target in the first imaging image. The scheme can improve the image data processing efficiency and the practicability of the target.

The embodiment of the invention provides an underwater concrete scene three-dimensional reconstruction method, system and device and a storage medium, and relates to the technical field of underwater three-dimensional reconstruction, and the method comprises the steps: extracting feature points in each key frame image from a plurality of key frame images of video data, and obtaining feature matching point pairs; calculating relative poses of adjacent key frame images and three-dimensional coordinates of the matched feature points according to the pixel coordinates of the same matched feature point; calculating a pixel depth value of each pixel point according to the relative pose; and performing multi-view fusion according to the relative pose, the three-dimensional coordinates of the matched feature points and the pixel depth value of each pixel point, and generating a three-dimensional grid model of the underwater concrete scene. According to the method, the feature points in the enhanced video image are obtained, the moving distance and the translation vector of the shooting camera are calculated, and three-dimensional reconstruction is carried out in combination with the pixel depth value, so that the image acquisition quality is improved, point cloud data are saved, and the precision and efficiency of three-dimensional reconstruction of a large-range underwater scene are improved.

The invention provides a defect detection method and device, electronic equipment and a storage medium. The method comprises the following steps: acquiring a template image and a product image of a to-be-detected product; performing feature matching on the template image and the product image, and performing non-rigid alignment on the product image based on feature point pairs obtained by feature matching to obtain an alignment image of which the geometric view angle is consistent with that of the template image; and defect detection is carried out based on the aligned image and the template image to obtain a detection result, so that the problem of image deformation caused by the fact that the surface of the to-be-detected product is a curved surface and the shooting angle is not correct in a traditional scheme is effectively solved, and local and global deformation of the product image can be corrected, thereby greatly improving the alignment precision of defect detection, and improving the detection accuracy of the to-be-detected product. Therefore, the real difference of the product surface can be more focused during defect detection, non-defect geometric deformation interference is effectively shielded, and the accuracy and robustness of defect detection of complex surfaces of household electrical appliances and the like are greatly improved.

The invention provides a real-time feature point extraction and matching method based on star operation and GateMLP, and the method comprises the following steps: S1, carrying out the preprocessing of an input image, and sending the image into a lightweight convolutional network to extract an initial feature; s2, on the basis of the output feature map, deep feature modeling is carried out by fusing the structure of a convolution branch and a star operation module, and a dense descriptor and a feature point thermodynamic diagram are output; s3, non-maximum suppression is performed on the thermodynamic diagram, and key point coordinates are screened in combination with confidence score; s4, performing rough matching on the key points of the left view and the right view by adopting cosine similarity and a mutual nearest neighbor strategy to obtain an initial matching pair; and S5, inputting the paired descriptors obtained by rough matching into the gated multilayer perceptron for fine-grained matching, and outputting accurate matching point pairs. The method provided by the invention realizes end-to-end efficient reasoning while ensuring high extraction and matching precision, has good real-time performance and robustness, and is suitable for unmanned aerial vehicle navigation, SLAM (Simultaneous Localization and Mapping) and other actual scenes needing quick response.

The invention relates to the technical field of SLAM, in particular to a dynamic environment robust positioning method based on improved ORB-SLAM2, and the method comprises the steps: obtaining RGB-D scene data containing an indoor moving object, and extracting ORB feature points described by FAST angular points and BRIEF descriptors; extracting feature points with unreliable heights according to geometric features of ORB feature points of adjacent frames; pixel analysis is carried out on the RGB-D scene data based on a Fast-SAM instance segmentation model, and a mask matrix containing environmental object space boundary information is generated; performing point-prompt extraction on the height unreliable feature points and the mask matrix to obtain a dynamic object mask; removing ORB feature points existing in the dynamic object mask to obtain a reliable feature point set; and calculating matching point pairs in the reliable feature point set to obtain an essential matrix and a corresponding rotation matrix and translation vector, and refining the pose based on a nonlinear optimization method.

The invention discloses an image staged matching and positioning method, and relates to the technical field of image analysis. Comprising the following specific steps: constructing a model architecture, and inputting an unmanned aerial vehicle image u; candidate images {u, Si} are screened through global semantic feature matching; the method comprises the following steps: extracting image key points and description words, establishing an initial matching point pair set, introducing a mismatching screening mechanism, executing homography transformation on geotagging information in a satellite image, and determining ground target positioning under the view angle of an unmanned aerial vehicle. According to the method, through a mode of combining global feature optimization and local mismatching screening, accurate positioning of a ground target under the view angle of the unmanned aerial vehicle is realized, and an optimal matching candidate is rapidly screened out; in the local matching stage, mismatching point pairs are eliminated, and the geotagging information in the satellite image is accurately mapped to the unmanned aerial vehicle image coordinate system through homography transformation, so that the image matching problem under the large view angle change is effectively solved, and the accuracy and efficiency of image matching and positioning are improved.

The invention belongs to the technical field of computer vision and three-dimensional image processing, and particularly discloses a mismatching mark point correction method and system based on an improved RANSAC algorithm. According to the method, congruent triangle criteria are introduced into the RANSAC algorithm, and potential matching point pairs conforming to geometric consistency are screened. Only when the potential matching point pairs meet the geometric consistency, the transformation matrixes R and T are solved, that is, mismatching points existing in the potential matching point pairs are effectively eliminated, so that on the premise that the point cloud splicing precision and the success rate are not changed, wrong calculation is reduced, the point cloud registration efficiency and robustness are improved, and the point cloud registration accuracy is improved. The method is suitable for various application scenes such as industrial part measurement, medical modeling, cultural relic digital protection and virtual reality.

The invention provides a bullet train bottom part loss detection method and a bullet train bottom part loss detection device based on deep learning, which can solve the problems of low registration precision and false report and missing report caused by easy interference under complex working conditions in the prior art, and the method comprises the following steps: obtaining a to-be-detected image at the bottom of a bullet train and a preset template image, the template image comprises at least one template frame for identifying the position of the standard part; extracting depth features of the to-be-detected image and the template image, and performing feature matching to obtain an initial key point pair set; performing abnormal point elimination on the initial key point pair set to obtain an effective key point pair set; according to the effective key point pair set, mapping a template frame in the template image into the to-be-detected image to obtain a corrected template frame; based on a preset comprehensive cost function, matching a target detection frame in the to-be-detected image with the corrected template frame to obtain an optimal matching pair; and according to the result of the optimal matching pair, identifying whether there is part loss in the to-be-detected image.

The invention discloses a large-parallax image splicing method and device, equipment and a storage medium, and belongs to the technical field of image processing. The method comprises the following steps: performing feature point matching on a to-be-spliced feature point set and a reference feature point set to obtain a feature matching point pair set; according to the parallax value of each feature matching point pair in the feature matching point pair set, layering the parallax of an overlapping region intersected with the reference image in the to-be-spliced image to obtain a plurality of parallax layers and a matching feature point pair set corresponding to each parallax layer; according to the matching feature points to be spliced in the matching feature point pair set of each parallax layer, determining each geometric feature region by using a region growing method; and according to the global homography matrix and the local homography matrix of each geometric feature region, performing fusion processing on the to-be-spliced image and the reference image to obtain a target spliced image. By using the technical scheme provided by the invention, the accuracy of large parallax scene image splicing can be improved.

The invention discloses a deep learning point cloud registration method for low-overlapping-rate point clouds. The method comprises the following steps: (1) carrying out multi-scale down-sampling on an input point cloud, calculating a weighted geometric centroid and a normal vector based on an adaptive neighborhood, and constructing a stable local reference point; (2) proposing a centroid geometric structure code, and embedding an attention mechanism by taking an inter-centroid Euclidean distance, a normal included angle and a neighborhood radius ratio as geometric priori to realize feature matching of structure constraint; (3) in a training stage, optimizing overlapping region positioning and point pair matching consistency by adopting a joint loss function formed by circular ring feature loss, overlapping perception loss and matchability loss; and (4) rigid body transformation is estimated by combining a coarse-to-fine matching strategy with a random sampling consistency algorithm, and global high-precision alignment is realized. According to the method, the registration precision and robustness under the conditions of low overlapping rate, noise and shielding are effectively improved, and the method is superior to an existing method in the aspects of a standard data set and real measurement data.

The invention relates to the technical field of three-dimensional modeling, in particular to an augmented reality-oriented three-dimensional scene reconstruction method and system, and is used for solving the technical problem that an iterative nearest point algorithm cannot meet high real-time performance and high-precision reconstruction requirements required by augmented reality at the same time. The method comprises the following steps: acquiring a pose transformation relationship between multiple frames of point cloud data and adjacent frames of point cloud data; extracting a plurality of feature points in each frame of point cloud data, and determining a feature descriptor of each feature point; according to the feature descriptors of the feature points, calculating a matching weight between feature point pairs in two adjacent frames of point cloud data, and calculating a local matching error based on the matching weight and a pose transformation relationship; performing global matching optimization based on the local matching error of the plurality of feature point pairs and the distribution density of each feature point in the respective point cloud frame, and determining an optimal matching point pair set which minimizes the global matching error; and according to the optimal matching point pair set, fusing the multi-frame point cloud data, and reconstructing a three-dimensional scene model.

The invention discloses a three-dimensional point cloud processing method and device based on multi-layer feature fusion and a storage medium. The method comprises the steps that point cloud data is subjected to standardized preprocessing, and PointNet is improved; after an original shared MLP feature extraction process of Point Net, a relative feature optimization mechanism of point-to-distance perception is added, and depiction of local geometric association is enhanced; feature optimization is carried out through an arc tangent function, and for each point i, the adjusted relative features of all point pairs are aggregated and fused with the original features; features of each intermediate layer of PointNet are extracted and fused, and feature expression integrity is improved by using multi-scale information; constructing a global-local adaptive receptive field fusion module, and enabling global semantics to dynamically guide local feature weights by introducing an adaptive mechanism so as to strengthen semantic association; and outputting a point-by-point segmentation label through a Point Net native segmentation head. According to the method, the limitation of the prior art is broken through through collaborative innovation of arc tangent feature distribution adjustment, full-front-layer multi-scale fusion and global-local adaptive guidance.

The application discloses a device pose estimation method and system based on stereo images, and the method comprises the following steps: acquiring a synchronous stereo image pair and performing stereo correction; extracting feature information of left and right images and performing matching and screening to obtain effective matching point pairs; calculating spatial three-dimensional point information through triangulation, and removing invalid points to form an effective three-dimensional space point set; calculating a pose transformation matrix based on the effective three-dimensional space point set and the corresponding relationship of adjacent frame features; and updating the absolute pose based on a pose recursive model and outputting the result. The application also comprises robust processing steps such as pose quality evaluation, state classification management and adaptive update strategy selection. The application enhances the robustness of pose estimation in a complex environment, and improves the continuity and stability of pose estimation.

The application relates to a feature point mismatch processing method and device, electronic equipment and a readable medium. The method comprises the following steps: matching initial feature points of two pictures to obtain a matching result; the matching result comprises a plurality of matched first feature point pairs; the moving vectors of the first feature point pairs in the two pictures are calculated; the target feature point pair is determined according to the moving vectors of the first feature point pairs; and the target feature point pair is removed from the matching result. The scheme provided by the application can reduce the probability of feature point mismatch and improve the matching accuracy.

The application relates to the technical field of remote sensing, and provides a scale-optimized remote sensing image matching method, device, equipment and medium, which comprises the following steps: pairing each first scale in a reference remote sensing image with each second scale in a remote sensing image to be matched; matching each feature point of the first scale with each feature point of the second scale for each scale pair, and screening out a best scale pair and an auxiliary scale pair with high matching quality; screening out an optimized matching point pair from the matching point pairs of the auxiliary scale pair according to the scale level difference between the two, and integrating the optimized matching point pairs of the auxiliary scale pair and the matching point pairs of the best scale pair into a same-name point pair set, and enhancing the same-name point pairs. The remote sensing image matching method provided by the application gives priority to feature point screening and matching between scales with similar levels, improves the similarity of the same-name point feature descriptions, and ensures that the finally output same-name point pair set has higher matching accuracy and positioning accuracy.

The invention discloses a three-dimensional cultural relic 3D scanning ultra-high-definition enhancement method, device and equipment and a medium, and relates to the technical field of three-dimensional modeling, and the method comprises the steps: obtaining a light supplement enhancement image set, carrying out the dense matching strategy of the light supplement enhancement image set, and generating image pairs with consistent structures; minimum coverage rectangular region extraction is carried out on the image pairs with the consistent structures, and redundant complementation region pairs are generated; performing texture completion processing on the receptor region image in the redundant completion region pair to obtain a texture completion image set; and key image pairs are extracted based on the final texture completion image set, a matching point pair set is obtained through scale invariant feature transformation, and a matching point three-dimensional coordinate set is obtained through triangulation of each matching point pair. According to the invention, pixel-by-pixel brightness compensation and color equalization are carried out by combining the time-weighted exposure consistency index, so that illumination equalization adjustment of the region with uneven local exposure is realized.

This application discloses a fast and interpretable homography decomposition method, relating to the field of computer vision technology. The method includes: obtaining a first anchor point pair, a second anchor point pair, and two sets of auxiliary point pairs based on a source image and a target image; calculating a first similarity transformation matrix based on the first anchor point pair and a second similarity transformation matrix based on the second anchor point pair; the first similarity transformation maps the first anchor point pair to a standard anchor point, and the second similarity transformation maps the second anchor point pair to the standard anchor point; calculating the parameters of a kernel transformation matrix using the auxiliary point pairs on the source plane and the target plane; the kernel transformation matrix has the property of preserving the standard anchor points; and synthesizing a homography matrix from the source plane to the target plane based on the first similarity transformation matrix, the second similarity transformation matrix, and the kernel transformation matrix. This application aims to solve the problems of low computational efficiency, scene limitations, and lack of algebraic and geometric meaning in existing homography calculation methods.

The application relates to a kind of body image matching network assisted internal three-dimensional deformation field measurement method, belong to deformation measurement field.The application implementation method is: in reference body image, define calculation region and calculation voxel point;Using three-dimensional body image feature extraction network, key three-dimensional features are extracted simultaneously.Using three-dimensional feature matching network, key three-dimensional features are matched.From matched three-dimensional feature point pair, the feature point pair in the preset three-dimensional neighborhood range around each calculation voxel point is extracted, and the three-dimensional deformation initial value of the calculation voxel point is calculated when more than four groups of feature point pairs are judged;Less than 4 groups are directly assigned using the deformation initial value of other successfully calculated points with the spatial position of the calculation voxel point nearest to the interpolation or, as the three-dimensional deformation initial value of the point.Using three-dimensional reverse combination Gauss-Newton nonlinear iterative optimization method, high-precision internal three-dimensional deformation field measurement value is calculated, that is, internal three-dimensional deformation field measurement is realized.

The invention relates to the technical field of defect detection, in particular to a railway contact line surface defect detection method and system. The method comprises the steps of obtaining a sequence image; calculating a space-time matching weight based on the tracking duration and the normal distance of the feature point pair, and obtaining a global homography matrix through a weighted random sampling consensus algorithm for preliminary correction; calculating a local affine transformation matrix and a distortion factor for the image segment, and splicing to generate a two-dimensional expansion graph; and projecting the candidate defect area segmented on the expanded image back to the original image, calculating a gradient direction entropy ratio, and judging a real defect based on a self-adaptive threshold determined by a distortion factor. According to the scheme, non-rigid deformation of the contact line can be compensated, geometric distortion and real defects can be distinguished in a self-adaptive mode, and the detection accuracy is improved.

The application provides a three-dimensional point cloud registration method and system based on robust correspondence screening, which comprises the following steps: extracting original local features and superpoint sets of source point cloud and target point cloud respectively; selecting multiple guide points which are dispersed in space from the superpoint set, calculating geometric position relationship between each superpoint and guide point, fusing the geometric position relationship as position embedding information with the original local features to obtain enhanced features; directly screening a first matching pair set from a similarity matrix according to similarity; and screening dispersed matching pairs as a second matching pair set based on spatial position distance constraint of the selected matching pairs; and estimating and optimizing a transformation matrix between the source point cloud and the target point cloud based on at least the first matching pair set and the second matching pair set. The application can obtain high-confidence matching pairs which can completely express the overlapping area through distance constraint screening, and effectively avoid the collapse and failure of a large number of matching pairs caused by the aggregation of point pairs with high feature similarity.

This invention relates to the field of data verification technology, and particularly to a method, apparatus, device, and storage medium for rapid geometric verification of hyperspectral data. The method includes: acquiring a high-resolution visible light area array image and preprocessing it to obtain a visible light orthorectified image; acquiring a raw hyperspectral image and performing coarse geometric correction to obtain a coarsely geometrically corrected hyperspectral orthorectified image; performing feature extraction and matching processing on the two images obtained above, constructing a large number of pseudo-control points based on the matching results to obtain a set of point pairs including geographic coordinates; calculating the true scan vector of each scanned pixel in the coordinates of a digital land model, and calculating the corresponding geographic coordinates; constructing a cost function based on the set of point pairs and the calculated geographic coordinates; and iteratively solving the hyperspectral geometric verification parameters with the goal of minimizing the cost function to perform geometric correction on the raw hyperspectral image. The method disclosed in this application can significantly improve the efficiency of geometric verification and reduce the verification cost.

A computer-implemented method for detecting a morphological difference between three-dimensional digital models of a tooth includes: obtaining first and second three-dimensional digital models which are two three-dimensional digital models representing a same tooth; performing alignment between the first and second three-dimensional digital models; based on the first and second three-dimensional digital models after the alignment, determining vertices of the first three-dimensional digital model and corresponding points of the second three-dimensional digital model to constitute a first point pair set comprising multiple first point pairs; dividing the multiple first point pairs into groups based on distances of respective first point pairs; and detecting a morphological difference between the first and second three-dimensional digital models by determining a category of a morphological difference in a region corresponding to each group based on distances of first point pairs in the each group.

The invention provides a dispensing method, electronic equipment and a storage medium. The dispensing method comprises the following steps: performing feature matching on an image of a product to be dispensed and a template image to obtain a plurality of matching point pairs; based on the multiple matching point pairs, the pose deviation of the product to be subjected to glue dispensing relative to the standard pose is solved through a rigid body transformation model; constructing a transformation matrix by using the pose deviation, and performing coordinate transformation on each path point in the preset dispensing path by using the transformation matrix to generate a corrected dispensing path; and controlling a dispensing execution mechanism to perform dispensing operation according to the corrected dispensing path. According to the method, the dispensing precision and the process stability can be improved.

This invention relates to the field of computer vision technology, specifically disclosing a disparity detection method based on synchronous filtering. The method comprises the following steps: S1: feature extraction and cross-view point pair construction; S2: constructing a disjoint-set data structure with dynamic member tracking capabilities; S3: constructing a synchronous filtering sequence based on global ranking; S4: dynamic topological consistency analysis and disparity suspect point marking; S5: filtering to obtain highly robust final disparity feature points and outputting them. The technical solution of this invention can accurately identify and retain disparity feature points that have physical consistency but violate the global geometric model in scenarios with large disparity and complex depth, without pre-setting a global geometric model or relying on fixed parameters.

The invention discloses a camera calibration method and device and electronic equipment, and the method comprises the steps: obtaining the GIS layer data of a target region, and enabling the target region to correspond to the collection range of a target camera; obtaining image data of a target area collected by a target camera; registration processing is carried out based on the GIS layer data and the image data, registration data are obtained, and the registration data comprise a plurality of homonymy point pairs; a transformation matrix between pixel coordinates of the image data and geographic information coordinates is determined based on the registration data. According to the scheme, the direct mapping relation from the end-to-end pixel coordinates to the geographic coordinates is constructed, and efficient, robust and low-cost automatic calibration can be achieved; the problems that traditional camera calibration depends on manual arrangement of calibration plates, known world coordinate points need to be accurately known, and the process is tedious are solved.

This invention discloses a rapid point accuracy matching calculation method, comprising the following steps: ① Selecting a study area and conducting field measurements on the boundary lines of various land features within the study area to obtain phased surveying results and checkpoint data collected according to quality inspection requirements; ② Preprocessing the phased surveying results and checkpoints; ③ Based on the checkpoint data, setting the allowable range for accuracy tolerance and generating a point matching retrieval range; ④ Based on the retrieval range, performing point matching and generating an association record table; ⑤ Based on the association record table, automatically selecting matched point pairs to calculate point accuracy error; ⑥ Based on the point accuracy error calculation results, obtaining the accuracy calculation result file. This method, based on GIS spatial analysis technology, automates the cumbersome data processing and point matching process, reduces the impact of the skill level of calculation personnel on quality inspection projects, and improves quality inspection efficiency and accuracy.