343 results about "Transformation matrix" patented technology

The invention discloses a road and bridge settlement displacement monitoring system and method based on image detection, and the method comprises the steps: selecting a plurality of static background reference points in a stable background region of a monitoring scene, so as to construct a virtual and stable image internal reference system; furthermore, by accurately tracking image coordinate changes of the background reference points in the initial reference frame and the current frame, a transformation matrix capable of accurately describing disturbance of the camera from the initial pose to the current pose is reversely calculated, and the transformation matrix is applied to observation coordinates of a monitored target point; therefore, the virtual displacement component introduced by the camera disturbance is accurately stripped from the total displacement, and finally the real image displacement generated only by the motion of the structure is obtained. By means of the mode, the system can effectively resist interference of external factors such as environment vibration and temperature change, it is ensured that the height of the finally calculated physical displacement is close to the real settlement value of the structure, and therefore the accuracy and reliability of the monitoring result are greatly improved.

The invention discloses a pose estimation method and device based on point pair feature matching, a medium and a product. The method comprises the following steps: performing coarse registration on a fusion scene point cloud and each template point cloud, and calculating point pair features; according to the corresponding hash key values, retrieving from each hash table to establish registration point pairs; calculating a translation vector and a rotation quaternion matched with each registration point pair according to the reference point coordinate system and the template coordinate system, and performing clustering processing on each registration point pair to obtain each initial corresponding set; screening out a target pose candidate transformation matrix and a matched target template point cloud from the pose candidate transformation matrixes matched with the initial corresponding sets respectively; a target optimization function between the fusion scene point cloud and the target template point cloud is constructed, iterative optimization is performed on parameters in the target pose candidate transformation matrix, and the target pose of the fusion scene point cloud is obtained at the end of iteration, so that the precision, real-time performance and reliability of six-dimensional pose estimation are improved.

The invention relates to the technical field of image precision optimization, in particular to a surveying and mapping image mathematical precision evaluation method based on dense point cloud matching, which comprises the following steps: acquiring and preprocessing image data, synthesizing an image gradient modulus length and a maximum principal curvature, and introducing a surface normal vector partial derivative to obtain a surface normal vector partial derivative; calculating a curvature-driven depth discretization step length, and generating an initial point cloud data set; constructing a point cloud complex network structure diagram and calculating the topology durability of a point cloud topology structure to obtain an optimized point cloud data set; based on the optimized point cloud data set, in combination with the target image information, the adaptive curvature gradient weight and the error diffusion control value, constructing an adaptive matching cost function, and calculating a point cloud matching cost matrix; and calculating an optimal point cloud transformation matrix based on the point cloud matching cost matrix. According to the surveying and mapping image mathematical precision evaluation method based on dense point cloud matching, low-texture region point cloud matching error suppression and structure optimization of high-curvature region point cloud on a complex curved surface are realized.

The invention provides an object grabbing method and system based on point cloud deep learning, and the method comprises the steps: obtaining a target object point cloud of a target object, carrying out the point cloud matching of the target object point cloud through a point cloud registration model based on a deep learning network, obtaining an initial matching position, optimizing the matching position through a nearest iteration algorithm, and obtaining an optimal matching position; obtaining an accurate transformation matrix of the target object relative to the template under a camera coordinate system; based on a pre-obtained hand-eye transformation matrix, calculating a transformation relation of the point cloud attitude of the target object relative to a tool coordinate system through teaching; the hand-eye transformation matrix is used for indicating a transformation relation between a sensor coordinate system and a mechanical arm tail end coordinate system; and according to the conversion relation and the accurate transformation matrix, the grabbing position coordinates of the target object under the mechanical arm base coordinate system are solved. According to the invention, point cloud registration is carried out through the deep learning network, and the position of the target object can be determined more accurately.

The invention relates to the technical field of face verification, and discloses a face identity verification data processing method based on dynamic feature extraction, which comprises the following steps: acquiring a continuous face video frame sequence and calculating a full-pixel instantaneous velocity vector to generate an original dense optical flow field, selecting rigid region anchor points to calculate a rigid affine transformation matrix and construct a theoretical rigid motion field, performing differential stripping on the theoretical rigid motion field from the original dense optical flow field, and extracting a non-rigid micro-motion residual field; mapping the non-rigid micro-motion residual field to a facial muscle topological grid to generate a time sequence feature tensor, and calculating a geodesic line distance between a covariance matrix of the time sequence feature tensor and a reference dynamic feature in a Riemannian manifold space; when the geodesic distance is smaller than a threshold value, verification is passed, through a rigid-non-rigid orthogonal decomposition mechanism, the special viscoelastic micro-motion and cooperation law of biological soft tissue is captured by utilizing a residual field, and the high-simulation mask is effectively defended.

The invention relates to the technical field of industrial vision and intelligent monitoring, and discloses a loom control system operation state monitoring and fault diagnosis method based on machine vision, which comprises the following steps: acquiring a video stream in a loom shed area and constructing a two-dimensional space-time slice tensor; performing global motion compensation processing on the space-time slice tensor by using a homography transformation matrix, mapping a compensated dynamic texture feature sequence to a three-dimensional phase space by using a time delay embedding algorithm, and reconstructing a closed phase space trajectory representing periodic operation logic of the loom; the discrete Frechet distance between the phase space trajectory of the current operation cycle and the preset reference trajectory is calculated, and a control instruction is generated. The health degree of the sequential logic of the system is directly quantified on the premise that specific components are not recognized by using the invariant characteristic of the phase space manifold topology; the technical problems that small phase lag is difficult to perceive and nonlinear faults cannot be early warned in a strong noise environment are solved.

The invention belongs to the technical field of intelligent early warning, and particularly relates to an intelligent early warning method for a DMF waste liquid purification and recovery control platform, and the method comprises the steps: carrying out the principal component analysis of long-period historical data, and constructing a principal component transformation matrix of a static reference model; for a moment to be diagnosed, calculating a reconstruction value by using the static reference model to obtain a residual vector, carrying out eigenvalue decomposition on a covariance matrix of a residual matrix of a sliding time window, calculating a drift coherence index according to the distribution of drift eigenvalues, modulating a drift principal component vector of the sliding time window by combining the residual vector, and carrying out diagnosis on the moment to be diagnosed; and obtaining a drift compensation vector, superposing the drift compensation vector with a reconstruction value of a real-time data vector at a to-be-diagnosed moment to obtain an adaptive reconstruction value at the to-be-diagnosed moment, calculating a reconstruction error, comparing the reconstruction error with a fault alarm threshold, judging whether a fault exists at the to-be-diagnosed moment, and performing early warning. According to the invention, the early warning accuracy and robustness are improved.

The invention discloses a multi-scale linear array camera splicing method and system based on point cloud. The method comprises the following steps: completing acquisition and preprocessing of point cloud data and image data of a target area; determining an overlapping region range between adjacent images; extracting spatial structure characteristics in the point cloud data, and performing multi-scale hierarchical decomposition on the point cloud through a multi-scale segmentation method; meanwhile, multi-scale image feature extraction is carried out on the images of the linear array camera; solving gradients in X and Y directions by adopting an optical flow method aiming at any pixel in the overlapping region, and calculating a motion vector between the pixels; fusing the optical flow information obtained under each scale, and constructing a globally consistent optical flow vector field; according to the fused optical flow vector, calculating to obtain a geometric transformation matrix of the whole overlapping region; and after image transformation and alignment are completed through the transformation matrix, fusion processing is carried out on overlapped areas. And the unification of the visual effect and the spatial integrity of the spliced image is ensured.

The invention discloses a five-axis machining cutter location linear interpolation optimization control method, which relates to the technical field of numerical control machining, and comprises the following steps: fitting a machining contour to generate a curve expression, determining an operation point sequence based on curvature distribution, and converting the operation point sequence into an actuator pose sequence; based on the model of a five-axis machining machine tool, a total transformation matrix from a base coordinate system to an actuator coordinate system is established through a D-H method, motion parameter changes caused by five-axis elastic deformation and inertia force are established based on the nonlinear beam theory and the Alembert principle, and a nonlinear axis body coupling motion equation is established; a correction expression is generated in combination with the Jacobian matrix, optimal unknown parameters in the correction expression are determined by introducing a quantum fluctuation simulated annealing algorithm, and a correction Jacobian matrix is generated; according to the actuator pose change of the adjacent operation points in the actuator pose sequence, the corrected Jacobian matrix is combined, the inverse kinematics algorithm is adopted to derive the motion parameter vector of the previous operation point, and higher-precision machining control is achieved.

The invention discloses a sparse polarization MIMO radar multi-parameter estimation method based on tensor decomposition, and the method comprises the steps: firstly constructing a dual-station MIMO radar system based on a polarized antenna, and calculating a covariance matrix of a receiving signal of a matched filter of a receiving end; then converting the covariance matrix into a tensor form, performing dimension transformation on the tensor, and obtaining a virtual covariance tensor of continuous virtual array elements in the difference joint array by using two transformation matrixes; dividing the virtual covariance tensor into a plurality of sub-tensors and connecting the sub-tensors along a fifth dimension and a sixth dimension; and finally, tensor decomposition is performed on the tensor obtained after connection, and multi-dimensional parameter estimation of the far-field target can be realized by using a matrix obtained through decomposition. The method has the advantages that joint estimation of the direction of arrival, the direction of departure and the polarization parameters is achieved under the underdetermined condition, and the estimation precision is high.

The invention provides a text retrieval-oriented adaptive length embedding method and system, and the method comprises the steps: encoding an original document into a high-dimensional embedding vector by using a trained embedding model, and obtaining an original document embedding matrix X belonging to Rn * d; carrying out matrix learning transformation on the embedded vector through a transformation matrix fitting module to obtain a transformed embedded vector; inputting the converted embedded vector into a hybrid coding module for hybrid coding, dividing the converted embedded vector of each document into a fixed-length dense part and a variable-length sparse part, dynamically adjusting the length of the sparse part according to the semantic complexity of the document, and then performing similarity calculation by combining the dense part and the sparse part to obtain a similarity value; and thus, self-adaptive text retrieval is realized. According to the method, the resource utilization efficiency of the system is remarkably improved, and the retrieval accuracy and robustness are also ensured. And the method is particularly suitable for a large-scale retrieval system and an application environment with strict requirements on storage and computing resources.

The invention relates to the technical field of image recognition, and particularly provides a multi-view point cloud registration method, which comprises the following steps of: performing coarse registration on a source point cloud and a target point cloud based on multi-dimensional features by using an RANSAC (Random Sample Consensus) algorithm in a coarse registration stage to obtain a coarse registration transformation matrix; in the fine registration stage, the coarse registration transformation matrix is used as an initial value, point cloud registration is carried out in at least two resolution spaces, segmented iterative optimization is carried out by using an error loss function set in each resolution space, rapid convergence is carried out in a low-resolution space through an iterative nearest point algorithm, and the point cloud registration is realized. And local geometric alignment optimization is carried out in other resolution spaces through a generalized iterative nearest point algorithm, and fine registration of the source point cloud and the target point cloud is completed after multi-resolution space progressive optimization registration. According to the method, the robustness of feature matching of the low-overlap region is remarkably improved, the registration speed and precision are balanced, and the limitation of a traditional point cloud registration method under the low-overlap and non-ideal point cloud condition is solved.

The embodiment of the invention discloses a point cloud registration method and system and electronic equipment. The method comprises the following steps: performing height layering on a source point cloud and a target point cloud in a preset height range to obtain a plurality of point cloud layers; performing feature matching based on each point cloud layer of the source point cloud and each point cloud layer of the target point cloud, and determining a plurality of reference transformation matrixes; performing consistency voting screening on the rotation components of the plurality of reference transformation matrixes to obtain at least one candidate transformation matrix; performing block verification on the source point cloud and the target point cloud according to the at least one candidate transformation matrix to determine an optimal transformation matrix; and performing fine registration based on the optimal transformation matrix, and determining a final transformation matrix of the source point cloud and the target point cloud according to a fine registration result. According to the technical scheme, point cloud map registration can be rapidly and accurately completed without an initial pose, and the problems of low speed, low precision and poor robustness of large-scale point cloud registration in the prior art are solved.

The invention discloses an AI-based historical defective fresco digital restoration system, and relates to the technical field of AI restoration, a two-channel filter and a U-Net network are adopted to extract a fresco damage boundary, and edge detection is enhanced in combination with manual labeling; calculating a multi-scale Hurst index, and constructing a matrix quantization fractal feature change rule; dynamically generating a probabilistic grammar rule set based on the fractal incidence matrix, and defining a state transition probability function of texture growth; recursively executing texture generation driven by grammar rules from boundary points, and introducing random disturbance attenuating along with depth to simulate a stroke natural form; textures are generated through Laplacian pyramid layered correction, and the phase of a reference image is aligned in a Fourier domain to reserve structural consistency; and solving an affine transformation matrix with unchanged illumination in the transition zone, and globally optimizing color connection between the restoration area and the original wall painting. According to the method, the dynamic grammar rule base is established by analyzing the fractal features of the residual strokes, so that the generated texture follows creation logic, and the visual coherence of a recovery area is improved.

The invention belongs to the technical field of data processing, and particularly relates to a multi-view cloud high-precision fusion splicing method based on semantic geometry collaborative optimization, which comprises the following steps: extracting and quantifying features of multi-view cloud, endowing each point cloud with a semantic label, calculating the confidence coefficient of classification of each point cloud, and simultaneously calculating the normal vector and curvature geometric features of each point cloud. And performing robust initial registration based on semantic constraint, and outputting a robust initial transformation matrix. And constructing a collaborative energy function, carrying out dynamic quantitative coupling on semantic confidence and geometric stability, and carrying out iterative registration. And realizing anisotropic point cloud fusion based on semantic guidance, and outputting a fused and spliced three-dimensional point cloud model. And finally, verifying the performance of the method, and measuring fusion splicing errors. According to the method, the semantic features of the point cloud data are effectively extracted and utilized, the precision and robustness of point cloud fusion splicing are improved, and geometric details of a final model are reserved to the maximum extent.

The invention provides an automatic image cutting method and system based on face key points, which are applied to the technical field of image processing, and the method comprises the steps: inputting a portrait image into a portrait segmentation model for foreground extraction, and obtaining a figure region mask; performing top positioning estimation based on the figure area mask to obtain a top position of the portrait image; inputting the portrait image into a face key point detection model to obtain a plurality of key points; determining a face midpoint of the portrait image based on the position relationship of the plurality of key points; performing affine transformation estimation based on the source point set and a preset target point set to obtain an affine transformation matrix; adjusting the vertical offset of the affine transformation matrix to obtain a longitudinal correction matrix; performing edge correction on the longitudinal correction matrix to obtain a cutting transformation matrix; and performing image transformation on the portrait image based on the cutting transformation matrix to obtain a standard composition image. According to the invention, the cut image with uniform size, standard composition and good detail retention can be generated.

The invention discloses a large language model machine forgetting algorithm based on representation spatial offset. The algorithm comprises the following steps: constructing a forgetting set and a retention set, executing causal tracking on a large model by using a knowledge exploration data set, and identifying a feedforward neural network having a significant contribution to correct prediction of the model to determine a forgetting layer; estimating an input feature space by using the reserved set approximation, and constructing a null-space projection matrix based on the feature space; inputting a forgetting set and a retention set, obtaining representation output of the training model and the original model in the last forgetting layer, and exporting a parameter updating gradient through a loss function; and carrying out null-space projection transformation on the updated gradient and then updating the last linear transformation matrix of the feedforward neural network in all forgetting layers. According to the method, the knowledge storage characteristics of the feedforward neural network in the large model are utilized, knowledge removal is guided in the representation space, interference on non-target knowledge is effectively suppressed while accurate forgetting is achieved, and the effectiveness and controllability of the forgetting process of a large model machine are remarkably improved.

The invention relates to the technical field of optical measurement, and discloses a biaxial galvanometer error calibration method and system, electronic equipment and a storage medium, and the method comprises the steps: obtaining a camera internal reference containing an internal reference matrix and a distortion coefficient through a calibration board; establishing an ideal geometric transformation model of the biaxial galvanometer, and defining a first galvanometer transformation matrix determined by the rotation angle of the first galvanometer and the first distance and a second galvanometer transformation matrix determined by the rotation angle of the second galvanometer and the second distance; installing error parameters are introduced, and a complete coordinate transformation model used for describing a complete light path transformation relation from an actual camera coordinate system to a final imaging coordinate system is formed; calibration data are collected, galvanometer system parameters and installation error parameters are optimized based on re-projection errors, re-projection residual errors are constructed, the re-projection residual errors are minimized through a nonlinear optimization algorithm, and optimal parameters are obtained. The installation error can be accurately calibrated and effectively compensated.

The invention provides a video point cloud data fusion and curved surface reconstruction method based on a digital twin scene, and belongs to the technical field of video processing. Then, inertial data is mapped to a Lie group manifold space, and a continuous trajectory model is constructed by using B spline fitting; based on the model, accurately calculating a first pose transformation matrix at the exposure moment of each row of pixels and a second pose transformation matrix of each laser point; then, projecting the point cloud to a global coordinate system by using the second matrix, and generating multi-dimensional feature mapping data of space-time registration by combining the first matrix, the internal reference and a perspective principle; and finally, inputting the data into a voxelization truncated symbol distance field, synchronously updating a distance value and a color weight, and generating a high-fidelity digital twin three-dimensional curved surface by extracting a zero level set contour surface.

The invention provides a non-contact measurement-oriented three-dimensional point AI registration and tolerance analysis method and system, and relates to the field of three-dimensional point AI registration and tolerance analysis, and the method comprises the steps: synchronously collecting point cloud data through multiple sensors, and carrying out the adaptive preprocessing, and obtaining a preprocessed point cloud pair; constructing a multi-scale geometric descriptor by using a local curvature and a normal vector, and performing multi-scale hierarchical feature extraction to obtain an enhanced point feature set; calculating a bidirectional matching probability matrix by using an optimal transmission theory, screening high-confidence point pairs through spatial compatibility constraint to obtain a high-confidence matching point pair set and a matching score, performing transformation matrix estimation and quantification uncertainty, and outputting optimal rigid body transformation and a covariance matrix thereof; self-adaptive optimization is executed in a layered mode, the search range is dynamically adjusted, transformation parameters are verified in a cross-scale mode, and a convergent accurate registration result is output. The method is used for overcoming the defect that in the prior art, low-overlapping-rate registration precision is low.

The invention relates to a geometric error measuring method for a machine tool rotating shaft, standard balls are mounted on a rotating shaft workbench of a machine tool, a probe is mounted on a machine tool spindle, and the method comprises the following steps: rotating the rotating shaft workbench to obtain sphere center coordinates of a plurality of groups of standard balls; selecting the center coordinates of one group of standard balls, and calculating an optimal fitting plane; according to the optimal fitting plane and the center coordinates of the other groups of standard balls, position-independent errors are obtained; constructing a transformation matrix, and obtaining a coordinate system after the position-independent error is eliminated; obtaining a matrix equation by combining the center coordinates of the plurality of groups of standard balls, the transformation matrix and the coordinate system; and solving the matrix equation to obtain a position correlation error. The measuring method is based on a numerical control machine tool rotating shaft geometric error model, influences of position-independent errors on the center position of a standard ball at the initial position are considered, four position-independent errors and six position-related errors of the rotating shaft are obtained through step-by-step calculation, and comprehensive measurement of ten errors of the rotating shaft is achieved.

This invention provides a rotation-aware enhanced variable window attention super-resolution method, belonging to the field of image processing technology. It includes acquiring and preprocessing the original remote sensing image of the target object, segmenting it into several basic windows, extracting feature vectors, using quadrilaterals to predict the basic parameters of the projective transformation matrix based on the extracted feature vectors to obtain the reconstructed projective transformation matrix, extracting the rotation angle difference between any two quadrilaterals as a rotation-aware term, establishing a variable window attention mechanism, embedding the variable window attention mechanism into a SwinIR network to build a high-precision super-resolution model architecture, training the high-precision super-resolution model, inputting real-time remote sensing images into the high-precision super-resolution model, and outputting a high-resolution image containing the target sampling area. By combining the variable window attention mechanism with the rotation-aware mechanism, the super-resolution reconstruction accuracy and geometric consistency of rotated objects are significantly improved while maintaining computational efficiency.

This invention relates to the field of image recognition technology, specifically providing a multi-view point cloud registration method. In the coarse registration stage, the RANSAC algorithm is used to perform coarse registration of the source and target point clouds based on multi-dimensional features, obtaining a coarse registration transformation matrix. In the fine registration stage, the coarse registration transformation matrix is ​​used as the initial value, and point cloud registration is performed in at least two resolution spaces. Segmented iterative optimization is performed using an error loss function set for each resolution space. In the low-resolution space, an iterative nearest-point algorithm is used for rapid convergence, while in the remaining resolution spaces, a generalized iterative nearest-point algorithm is used for local geometric alignment optimization. After progressive optimization registration in multiple resolution spaces, the fine registration of the source and target point clouds is completed. This invention significantly improves the robustness of feature matching in low-overlap regions, balances registration speed and accuracy, and overcomes the limitations of traditional point cloud registration methods under low-overlap and non-ideal point cloud conditions.

The application discloses a similarity optimal on-board image registration method based on inertial navigation data fast convergence, and comprises the following steps: constructing a projection transformation model between an image transformation matrix, a to-be-registered image and a reference image; obtaining the projection transformation matrix according to the field of view optical axis position and three-angle offset of the satellite at the imaging moment and coarse registration; performing global coarse matching by using the projection transformation model and the projection transformation matrix to obtain a coarse matching image; decoupling the image transformation matrix according to a mathematical model to obtain a to-be-solved transformation parameter, substituting the to-be-solved transformation parameter into the image transformation matrix to obtain an optimal transformation matrix, and applying the optimal transformation matrix to the to-be-registered image to obtain a fine registration image; and calculating the similarity of the fine registration image and the reference image according to a local normalized image similarity measurement algorithm to obtain an optimal matching result. The application has the advantages of meeting the on-board calculation capacity requirement, meeting the image processing precision requirement and meeting the on-board storage resource requirement.

The invention discloses a feature selection method and a data reconstruction method for shoreline image feature selection. The method comprises the following steps: S1, integrating original data into a sample data set matrix X; s2, initializing a reconstruction transformation matrix W, a transformation matrix Q, an eigenvalue regression coefficient matrix A, a coordinate basis matrix B, a coding matrix E, an auxiliary matrix F and an auxiliary matrix D as unit matrixes, initializing a weight vector p and a mean vector v of a data set as unit vectors, and initializing a weight matrix P = diag (p); and S3, updating the coding matrix E, the auxiliary matrix F and the like based on the data set matrix X, the mean vector v of the data set, the transformation matrix Q, the eigenvalue regression coefficient matrix A, the coordinate basis matrix B and the weight matrix P. According to the invention, the accuracy of data reconstruction and the validity of feature selection can be improved.

The invention discloses a novel symmetric iteration nearest point cloud registration calculation method for quality detection, and the calculation method comprises the steps: setting a point set and a point set in a three-dimensional space, carrying out the registration of a point set P and a point set Q, so as to solve a six-degree-of-freedom rigid body transformation matrix, and carrying out the registration of the point set P and the point set Q; the six-degree-of-freedom rigid body transformation matrix comprises a three-degree-of-freedom rotation matrix R and a three-degree-of-freedom translation vector t; a weighted point-surface model is constructed, and a symmetric iteration nearest point objective function based on weighted point-surface measurement and an adaptive robust loss function is constructed based on the weighted point-surface model; the calculation of the symmetric iteration nearest point objective function based on the weighted point-surface measurement and the adaptive robust loss function comprises the step of alternately executing a corresponding point updating step and a registration step until a convergence condition is met.

The invention discloses an apple recognition method, system and device and a storage medium, and relates to the technical field of apple recognition and detection, and the method comprises the following steps: calculating feature points on each edge contour in an original infrared thermal image and a visible light image based on the curvature of the edge contour; determining the main direction of the feature point based on the position information of each feature point on the edge contour; performing parametric coding on the position and the main direction of each feature point to generate a feature vector of each feature point; matching based on the Euclidean distance of the feature vector of each feature point on the two images to obtain a plurality of matching pairs; determining a transformation matrix through the coordinates of the plurality of matching pairs; performing image registration on the original infrared thermal image and the visible light image based on the transformation matrix to obtain a registered image; and carrying out image fusion and target identification on the registered image and the visible light image to obtain the position, the category and the confidence of the apple. According to the invention, the accuracy of transformation matrix estimation and the precision of registration are improved.

The invention discloses a three-dimensional scene reconstruction method and system for an energy storage battery cabin. The method comprises the following steps: carrying out geometric reconstruction on acquired multi-view image data by using camera calibration parameters; projecting the initial three-dimensional point cloud to an imaging plane of a reference visual angle to obtain a priori depth map DCi; performing depth filling processing on the priori depth image DCi and the reference view ICi by using an image-guided depth filling algorithm; plane constraint depth optimization processing is carried out on the depth map DCi '; converting the depth map DCi ''into point cloud data, and performing feature extraction on the point cloud data by using an FPFH algorithm; performing first registration on the point cloud data and the point cloud data of other visual angles through an RANSAC algorithm to obtain a registered initial transformation matrix; carrying out the second registration of the point cloud data after the initial transformation matrix through an ICP algorithm, and obtaining a final transformation matrix; according to the invention, the multi-view point cloud fusion precision is 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 discloses an iterative registration optimization algorithm based on angle clustering, and the algorithm comprises the following steps: inputting an initial scene point cloud and an initial model point cloud, simplifying the initial scene point cloud and the initial model point cloud, and obtaining an initial corresponding point pair set; constructing a compatibility constraint, calculating a compatibility score of each pair of corresponding point pairs, and sequencing to construct a compatibility matrix; performing outer layer circulation: sequentially selecting the foremost point pair from the compatibility matrix as a first corresponding point pair; inner layer circulation: selecting a second corresponding point pair; clustering the corresponding point pairs based on the six rotational degrees of freedom, and selecting all significant clusters to generate a conversion hypothesis; and verifying and selecting an optimal conversion hypothesis as an output conversion matrix. The compatibility among all the corresponding point pairs is evaluated by constructing a compatibility matrix; a simple and effective clustering strategy is adopted, and all significant clusters are considered to generate a conversion hypothesis; the simplified point clouds and the key points are effectively combined through a hypothesis verification strategy, and the accuracy of alignment of the low-overlapping point clouds is improved.