284 results about "Characteristic point" patented technology

The invention relates to a defect positioning method based on fusion of weld defect features and trajectory tracking data, and belongs to the technical field of weld defect detection and positioning. The method comprises the following steps: capturing welding seam track dynamic data and defect feature data, constructing a dynamic coordinate system based on a welding seam initial feature point, and establishing double-data-set reference mapping; performing multi-physics field interference decoupling correction on the trajectory data, and performing cross-modal feature purification and core feature consistency verification on the defect data; converting the preprocessed data into a feature form adaptive to fusion, and constructing a welding process-defect formation mechanism association network to regulate and control fusion weight; and finally, reconstructing a three-dimensional dynamic contour of the welding seam, calling dynamic positioning logic to position the defect, and outputting a result carrying the process-defect causal confidence coefficient. The positioning precision is improved through multi-dimensional data fusion and mechanism association, and technical support is provided for welding quality management and control.

The invention discloses a flow field key structure detection and feature point extraction method based on an optimized YOLO model, and the method comprises the steps: S1, obtaining flow field time sequence schlieren images of an air-breathing aircraft under different working conditions through a high-speed schlieren collection system in a wind tunnel test; s2, preprocessing the flow field time sequence schlieren image to obtain a data set corresponding to the strong shock wave of the isolation section of the air-breathing aircraft, and dividing the data set into a training set, a verification set and a test set in proportion; s3, constructing an optimized YOLO target detection model, and obtaining an optimal weight model after iterative training and verification; and S4, adopting the optimal weight model to complete target detection and feature point extraction of the test set or the schlieren image to be detected. According to the method, the target area is focused through area cutting, meanwhile, a targeted data enhancement strategy of Gaussian noise, brightness adjustment, contrast ratio adjustment and saturation is designed, a high-quality annotation data set is constructed, and the model detection precision is effectively improved.

The invention discloses a calibration method and system based on multi-scale feature fusion and an adaptive optimization algorithm, and belongs to the technical field of computer vision. The method comprises the following steps: collecting a plurality of images containing calibration modes; detecting and positioning feature points by adopting a multi-scale feature fusion technology, and distributing a confidence score for each feature point; carrying out feature point matching and removing abnormal values by using an improved RANSAC algorithm based on the prior geometrical relationship and confidence score; preliminarily calculating camera parameters; and a nonlinear optimization model fusing confidence weighting and adaptive regularization is constructed, joint optimization is performed on camera parameters, and a high-precision calibration result is output. According to the method, through an optimization mechanism of multi-scale feature fusion and confidence guidance, the problems of unstable feature point detection, abnormal value sensitivity and insufficient precision of a traditional method in a complex environment are effectively solved, and the accuracy, robustness and automation degree of image calibration are remarkably improved.

The invention discloses a pipeline defect positioning method, device and equipment, a medium and a product. An SPPF-LSKA optimization instance segmentation model is combined with an ORB-SLAN3 framework, and dynamic feature points are eliminated by using information of an RGB-D depth map and an instance segmentation mask; secondly, a sliding window confidence evaluation mechanism is introduced, the credibility of feature points can be dynamically evaluated, wrong feature points generated due to instantaneous false detection are removed in time, and high-precision dynamic point removal is achieved; and finally, in a back-end optimization stage, a double-layer map strategy is adopted, a static mapping layer participates in BA optimization to reduce a positioning error, and a dynamic observation layer independently records an optical flow speed and a segmentation result of a dynamic region for dynamic interference analysis and does not participate in global optimization, so that interference of dynamic factors on positioning precision is avoided.

A model and its establishment method and system, a compensation method, equipment and a storage medium, the establishment method comprises the following steps: providing a test layout, including a test pattern, including a plurality of test points; obtaining the initial pattern density of each test point according to the test pattern; taking any test point as a feature point, obtaining the effective pattern density of the feature point according to the initial pattern density of each feature point and the influence of the initial pattern density of the plurality of test points located around the feature point on the feature point, wherein the influence degree of the initial pattern density of the plurality of test points around the feature point on the feature point is set anisotropically; obtaining the actual size deviation corresponding to the feature point; obtaining the estimated size deviation of the feature point; judging whether the estimated size deviation meets the preset condition according to the estimated size deviation and the actual size deviation; when the estimated size deviation meets the preset condition, the establishment of the etching deviation compensation model is completed. The etching deviation compensation model is more accurately established.

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 provides a face recognition access control management method and system for a smart community. The method comprises the following steps: constructing a face illumination quality evaluation model to recognize an abnormal area, performing repair by adopting a multi-stage compensation mechanism, and constructing a face feature density field to recognize a sensitive area; dynamically calculating the grid size of each area of the face; extracting texture, shape and semantic features of various scales in the re-divided grids, and calculating a feature contribution value of each grid unit; and constructing a multi-modal input tensor, inputting the multi-modal input tensor to the multi-branch convolutional neural network for training, establishing a closed-loop updating mechanism based on data generated during operation of the access control system, and continuously optimizing the convolutional neural network and parameters of the previous steps. According to the method, the output quality is ensured through multi-dimensional verification, including feature point spatial distribution rationality inspection, density field continuity verification and sensitive area and facial anatomical structure consistency inspection, and the accuracy and reliability of an identification result are effectively guaranteed.

The invention discloses a self-adaptive compression and lossless fast transmission method and system for high-frequency test data. The method comprises the following steps: extracting multi-dimensional dynamic feature vectors from high-frequency test data in real time and marking feature points; based on the features, the network bandwidth and the target compression ratio, dynamically selecting a compression algorithm combination and parameters through a pre-training decision model; performing data framing and typed preprocessing according to the algorithm combination; compression is executed through a multi-stage assembly line, and a correction link based on statistical characteristics is inserted between stages; constructing a three-dimensional transmission state matrix fusing priorities, links and quality scores, dynamically allocating links and adaptively adding forward error correction codes; and after error correction and decompression of a receiving end, realizing lossless verification through multi-dimensional comparison of feature point sequences of the reconstructed data and the original data. According to the method, the whole-course self-adaption of the compression strategy and the transmission scheduling is realized, and the lossless and reliable recovery of the data is ensured while the compression and transmission efficiency is improved.

The invention belongs to the technical field of three-dimensional geometric measurement, and particularly relates to a three-coordinate detection data processing method and system for automobile parts, and the method comprises the steps: obtaining an actual point cloud and a standard point cloud; by calculating the maximum angle gap of the data points, screening to obtain actual and standard feature point sets; anti-noise geometric descriptors based on tangential weights are constructed for the feature points in the two sets of feature point sets respectively; matching the two groups of feature points based on the descriptors and generating an initial matching set; solving global optimal transformation from the initial matching set by adopting a random sampling consistency algorithm; and the transformation is applied to the actual point cloud, fine registration is carried out by using an iterative nearest point algorithm, and finally deviation analysis is completed. The technical problem that traditional registration depends on initial alignment and is prone to falling into local optimum is solved, and the robustness and accuracy of registration are improved.

The invention provides a pipeline defect detection method, electronic equipment and a storage medium, and relates to the technical field of computer vision. The method comprises the following steps: performing coordinate conversion on an initial fisheye video stream to obtain a multi-frame plane image; for any frame of plane image, determining a binarization mask of the frame of plane image based on a semantic segmentation network, and extracting feature points of a pipe wall region in the frame of plane image by taking the binarization mask as a constraint to form a feature point set; determining an optimal suture line between any two adjacent frames of feature point sets by adopting a graph cut algorithm based on energy minimization, and carrying out splicing to obtain a pipeline panorama; and identifying the pipeline panorama by using a deep neural network model to perform defect identification to obtain a target defect. According to the method, the binarization mask is used as a constraint, and non-pipe wall area interference is eliminated; an optimal suture line is determined and splicing is completed based on an energy minimization graph cut algorithm, so that inter-frame splicing gaps and artifacts are effectively eliminated, and rapid and accurate detection of target defects can be realized.

The application discloses a model quantization method, and relates to the field of artificial intelligence, which comprises the following steps: acquiring a first feature map of a first intermediate layer output of a neural network; determining a first truncation interval satisfying a preset condition according to the numerical distribution of a plurality of first feature points in the first feature map; the first truncation interval comprises a first upper boundary threshold and a first lower boundary threshold, and the preset condition comprises that the numerical distribution density of the feature points in the numerical truncation interval is greater than the numerical distribution density of the feature points outside the numerical truncation interval. The application uses an upper end truncation threshold and a lower end truncation threshold to represent the parameter setting of quantization, instead of the zero position and the range size commonly used in previous schemes, uses double-end cutting based on density on the floating-point model first, removes outliers in the long-tail distribution, can adapt to the asymmetric distribution trend, and further improves the precision of the quantized model.

The invention provides a shape-based template matching method, and belongs to the technical field of machine vision, and the method comprises the steps: obtaining a plurality of template images with different scales and angles; respectively calculating the gradient, the feature vector and the feature point at each pixel point on each template image; for a test image, calculating feature vectors and feature points of the test image, performing gradient direction expansion on each feature point to obtain a gradient direction quantized value, and enabling the gradient direction quantized value to correspond to a unique binary character string; a plurality of lookup tables are created, the index number of each lookup table corresponds to each binary character string, and the index value corresponds to the cosine value of the feature point and the feature vector corresponding to the binary character string; extracting template images and feature information of the template images, performing sliding window matching on the template images on the test images in the horizontal and vertical directions, acquiring overall similarity, and judging whether the test images are successfully matched with the corresponding template images or not; according to the invention, the matching speed and accuracy can be effectively improved.

The invention provides a point cloud normal estimation method and device based on local subspace clustering, electronic equipment and a storage medium, and the method comprises the steps: introducing a feature point weight evaluation mechanism based on covariance features and geometric structure indexes into a local neighborhood, and screening out feature points with high confidence; carrying out multi-subclass division and plane fitting on the local point cloud by adopting low-rank subspace clustering with priori weight constraint in the neighborhood of the feature points, so as to re-estimate the normal direction of the feature points on the subclass level; meanwhile, noise point filtering and a normal diffusion strategy based on adjacent non-noise points are combined, so that the method can more accurately describe a local geometric structure in a complex scene with noise, multi-structure aliasing and non-uniform sampling, the precision and stability of point cloud normal estimation are remarkably improved, and the method is suitable for popularization and application. And sharp features and edge details of the model can be better maintained.

The invention discloses an intelligent substation equipment fault diagnosis method, device and equipment, and relates to the technical field of intelligent substation fault diagnosis, and the method comprises the following steps: carrying out the space alignment of a power sampling signal based on an equipment connection relation, and obtaining a structured graph signal; performing feature transformation on the structured graph signal to obtain a statistical incidence matrix and mapping the statistical incidence matrix into real-time state feature points in a Riemannian manifold space; according to curvature characteristics of the Riemannian manifold space, obtaining geometric deviation between the real-time state feature point and a preset ideal state point by adopting a logarithm mapping operator; and calculating the fault contribution degree of each device by using the geometric deviation, determining a fault device, and outputting a device diagnosis result. The method is used for solving the problems that weak fault sensing sensitivity is insufficient and faults are difficult to trace accurately under complex working conditions in the prior art.

The invention relates to the field of point cloud registration, and discloses an ICP registration method fusing KNN vector features and quaternion optimization, and the method comprises the steps: obtaining a local plane normal vector of a source point cloud, calculating a covariance matrix based on the source point cloud, carrying out the feature decomposition of the covariance matrix, obtaining a feature vector, and calculating the minimum feature vector of the source point cloud; calculating a feature descriptor of the point cloud corresponding to the minimum feature vector in the source point cloud based on the local plane normal vector of the source point cloud, and selecting the point cloud of which the feature descriptor is greater than a preset threshold as a feature point; constructing a neighborhood for the feature points and carrying out feature point matching to obtain matched feature points; obtaining a first precise point pair based on the matched feature points, and performing weight configuration on the first precise point pair to complete preliminary registration; and performing iterative registration on the first precise point pair after weight configuration based on quaternion optimization. According to the method, registration is optimized by fusing KNN vector features and quaternion, and accurate registration of point clouds can be realized.

The present application relates to the field of target identification, and more particularly to a multi-modal target detection and identification method based on image processing, comprising: determining and extracting strategy based on perspective richness and feature stability; when performing uniform extraction, determining whether to adjust the extraction quantity based on perspective satisfaction; when performing compensation extraction, determining the compensation paragraph based on the regional parameter representation value; for the reference image obtained by extraction, determining whether to perform feature point optimization based on the feature anchor representation value and the spatial coverage; in the feature point optimization process, determining the point optimization strategy based on the evaluation balance; determining the sub-region category based on the regional prominence, and determining whether to adjust the feature region area based on the one-class quantity and the one-class aggregation degree. The present application effectively improves the multi-modal re-identification effect.

The application discloses a landmark-based positioning success judgment method, a chip and a robot, and the positioning success judgment method comprises the following steps: 1, when the robot judges that the same landmark is consistent for multiple times, it is judged whether the concentration of the feature point corresponding to the landmark is in a preset concentration threshold range; if yes, step 2 is executed, otherwise, it is determined that the landmark positioning is successful; 2, when the robot judges that at least one reference landmark is consistent with the landmark in step 1, it is determined that the landmark in step 1 is successfully positioned; the reference landmark is different from the landmark in step 1, and the reference landmark is a landmark with uneven feature point distribution.

To provide a self-localization device that can reliably estimate its own position while keeping manufacturing costs down. [Solution] The self-position estimation devices 100, 100a, and 100b for estimating the self-position of a moving object V1 include: a first distance estimation unit 11 that extracts feature points p1 to p11 and feature quantities of the feature points from each of a plurality of captured images F1 and F2 obtained in time series by an imaging device, and estimates a first distance, which is the distance to each feature point, using the feature points and feature quantities extracted from the plurality of captured images; a second distance estimation unit 12 that recognizes a specific object contained in each of the plurality of captured images, identifies changes in the size of the same specific object in the plurality of captured images, and estimates a second distance, which is the distance to the specific object, based on the changes in size; and a feature point selection unit 13 that selects feature points p3 to p9, which are feature points used for self-position estimation, from among the feature points extracted by the first distance estimation unit, based on the first distance and the second distance.

The invention relates to the technical field of computer vision and electric digital data processing, and discloses a visual pose sequence optimization processing method based on data driving. In order to solve the problems of instruction oscillation and slow convergence caused by the fact that visual observation data of a controlled object is easily interfered by high-frequency noise under a complex working condition, the method comprises the following steps: firstly, extracting a noisy original target feature point coordinate; fusing system prior prediction and a Kalman dynamic gain matrix to carry out denoising so as to output a net visual error; according to the net visual error and the change rate thereof, adaptively adjusting the length of a historical data interception window and constructing a local data subset; constructing a least square model with numerical regularization constraint based on the data subset, and solving a current optimal pose adjustment instruction; and finally, converting the instruction into a digital driving parameter to guide the closed-loop action of the terminal. The method effectively filters observation noise, gets rid of dependence on an accurate system mapping model, and improves convergence stability in a complex environment.

The invention belongs to the technical field of educational competition, and particularly relates to an improved educational competition optimization algorithm based on a distribution estimation algorithm, which adopts a contour comparison method to extract graphic key feature points, establishes feature point matching pairs through a K-nearest neighbor matching algorithm, and optimizes the educational competition. The contour similarity is calculated in combination with the number of matched pairs, the average distance of standard graphic feature points and the coordinate deviation value, accurate quantization of graphic similarity is achieved, modeling data abnormal value detection is conducted on modeling data through a box graph method, reasonable discrete data and unreasonable data are effectively distinguished in combination with the quartile distance characteristic of the data, and the accuracy of the data is improved. Different feature point extraction modes and different data models are adopted for comparison of different types of data, it can be guaranteed that the data models are more practical, and the extracted data have higher reliability.

The invention provides an identification position-based packet change prevention identification method, device and equipment and a storage medium, and the method comprises the steps: obtaining a first image after a two-dimensional code label is put into a concrete sample, and obtaining a second image after the two-dimensional code label is pressed according to a randomly determined pressing region, any target feature point is selected from the multiple first texture feature points, a target coordinate system is constructed, and a first deviation angle of each positioning mark is determined; and when a third image sent by the inspection terminal is obtained, constructing a target coordinate system based on the target feature points and determining a second deviation angle of each positioning mark, and determining an anti-packet-adjustment identification result based on the corresponding multiple groups of first deviation angles and second deviation angles. According to the technical scheme of the embodiment of the invention, the two-dimensional code label can generate longitudinal offset through the randomly determined pressing area, the coordinate system is constructed based on the randomly selected target feature points to obtain the offset angle as the identification information, and the reliability of packet change prevention identification is improved.

The present disclosure provides a feature point matching method and device for images and a storage medium. The method comprises determining a plurality of candidate matching point pairs of feature points in two images, mapping the plurality of candidate matching point pairs into a network graph, establishing a first energy function representing the energy required for marking the plurality of candidate matching points in the network graph as correct matching point pairs or incorrect matching point pairs based on image disparity information, solving the minimum cut of the network graph as the optimal value of the first energy function by using a maximum flow / minimum cut algorithm, and determining the correct matching point pairs and the incorrect matching point pairs according to the minimum cut. The correct matching point pairs in the images can be determined by using the graph cut principle. This process does not need to rely on the setting of a threshold, thereby improving the accuracy of feature point matching. In addition, the present scheme does not need to perform model training and has strong scene generalization ability. Thus, the effect of feature point matching is improved.

The application discloses an edge feature extraction method based on deep learning target identification, acquires a picture to be detected, determines a detection range of the picture to be detected, determines a gray scale change inflection point of the detection range, determines a training range according to the detection range, outputs boundary feature points through a CNN model, determines the gray scale change inflection points in a threshold radius range of the boundary feature points as feature points, and fits the boundary feature points and the feature points to form an edge line. The application combines the innovative deep learning detection and the screening function of the boundary feature points in the boundary algorithm, preliminarily positions through the CNN model, improves the stability in a noise environment, determines the approximate range of the boundary, and positions the accurate position in the approximate range through the boundary algorithm. The application still has the ability to maintain stable detection performance when abnormal data, noise or environmental changes occur, has the precision advantage of boundary extraction, can optimize the boundary position, the precision can reach the sub-pixel level, and the robustness and stability of detection are greatly improved.

The invention provides a relocation method and device and a storage medium, and the method comprises the steps: obtaining a current frame, and extracting the feature information of the current frame through a target feature extraction network, the feature information comprising key point coordinates, a first global feature point descriptor and a first key feature point descriptor, and the target feature extraction network being obtained through training; based on the global feature point descriptor, screening out a first key frame matched with the current frame from a preset key frame database; performing common-view relationship clustering on the first key frame to obtain a second key frame; determining a feature point matching pair corresponding to the current frame and the second key frame based on the first key feature point descriptor; and according to the feature point matching pairs and the key point coordinates, determining the current pose of the current frame and performing repositioning. According to the method, the false detection probability caused by environment similarity is effectively reduced, so that the accurate current pose is obtained based on the second key frame, and the positioning accuracy is improved.

The present application relates to the field of artificial intelligence, and relates to a model trust region-based copyright protection method for smart grid deep learning models. The method comprises: acquiring a smart grid deep model set; for each model in the smart grid deep model set, searching an exclusive dataset for feature data samples having a model prediction value approximate to a model discrimination boundary, so as to obtain a trust region feature point set; performing dimensionality reduction on gradient vectors of the trust region feature point set on the model discrimination boundary according to a linear discriminant analysis method, so as to obtain perturbation vectors of the trust region feature point set; on the basis of predicted label changes of each model before and after the trust region feature point set is combined with the perturbation vectors, generating a model feature identifier set corresponding to the smart grid deep model set; and, on the basis of the model feature identifier set, training a copyright detection model to be trained, so as to obtain a pre-trained copyright detection model.

The invention relates to a dynamic SLAM method and system based on lightweight YOLOv8 and an adaptive key frame strategy, and belongs to the technical field of computer vision. According to the method, a dynamic region in an image frame is detected through an improved lightweight network, and extracted dynamic feature points are removed in combination with an epipolar geometry constrained motion consistency detection method; extracting line features from the static region based on an improved LSD algorithm, and compensating the loss of feature points; designing a key frame selection strategy of adaptive weight to delete redundant key frames, and constructing a joint optimization objective function by combining static point features and static line features to perform pose estimation and construction of a point cloud map; and finally, performing closed-loop matching by using visual and semantic features between the key frames, verifying a closed loop, establishing a pose constraint, and performing global graph optimization. According to the method, the influence of a dynamic object in a scene on the mapping quality of the SLAM algorithm can be effectively reduced, and the robustness of the algorithm in a dynamic environment is enhanced.

This invention relates to the field of AI-based data quality assessment technology, specifically to an AI-based method and system for assessing data quality throughout its entire lifecycle. The method includes: acquiring multi-stage data samples throughout the data lifecycle; decoupling data content and metadata structure; separating the core content field from the process trajectory field; automatically constructing a multi-dimensional quality feature space in the core content field; extracting native feature points and mapping them into the space; tracking data transfer events in the process trajectory field to generate dynamic transfer trajectories; injecting the process information carried by the trajectory back into the feature space; applying process constraint offsets to the native feature points to form quality state points; and finally generating a data quality assessment value. This solution achieves dynamic quality representation throughout the data lifecycle through field decoupling and process constraint perturbation, making the assessment results more closely reflect the actual data flow state.

The invention relates to the technical field of computer vision nondestructive testing, and discloses a concrete crack nondestructive testing quantitative analysis method based on a convolutional neural network. The method comprises the following steps: firstly, carrying out definition screening on a video stream, and generating a semantic mutual exclusion mask covering a crack region by utilizing semantic segmentation and morphological expansion; removing non-coplanar interference feature points in the crack region under the constraint of a mask, resolving a homography matrix only based on the feature points of the background region, and generating an orthoimage without perspective distortion; and finally, calculating the sub-pixel physical width of the crack in combination with the camera physical distance obtained by homography matrix decomposition and a direction adaptive Zernike moment positioning algorithm. According to the method, interference of crack textures on plane parameter calculation is eliminated through semantic constraint, geometric distortion caused by oblique shooting is corrected through virtual orthographic projection, and high-precision crack width quantitative detection can be achieved without external distance measuring equipment.

The invention discloses a necking detection method based on geometric feature self-adaption. The necking detection method comprises the following steps that S1, the longest main axis of a simulation graph is determined; s2, selecting N feature points on the longest main shaft; s3, obtaining the minimum key size of the simulation graph at each feature point; and S4, judging whether the simulation graph has a necking risk or not based on each minimum key size. By identifying the geometric centroid and the longest principal axis of a graph, adaptively selecting feature points and performing omnidirectional scanning, the measured minimum critical dimension is compared with a set safety threshold, and a risk point which is most likely to generate necking on the graph is accurately positioned, so that the automation and objectification of risk detection are realized, and the risk detection efficiency is improved. Subjectivity and experience dependence of manual marking are avoided, and consistency of detection results is guaranteed; meanwhile, the method has good adaptability to complex or deformed contours, missing detection and misjudgment are effectively avoided, and the yield and reliability of chip manufacturing are improved.

The application discloses a point cloud registration method based on a curved surface characteristic region constraint, comprising a point cloud downsampling module, a characteristic point extraction module, a characteristic point description and characteristic matching module, and a registration module based on a curved surface characteristic constraint region, and is used in a registration task with noise and unordered point cloud data. The point cloud downsampling module is subjected to denoising treatment by adopting a denoising algorithm based on KD-tree. The characteristic point extraction module adopts an extraction detection method based on a curved surface change index. The characteristic description and characteristic matching module uses a fast point feature histogram and a random sampling consistency matching algorithm to complete initial pose transformation. The registration module based on the curved surface characteristic constraint region adopts a method of adding a curved surface characteristic region constraint, accelerates the matching rate of icp characteristic point pairs by a KD-tree algorithm, completes point cloud registration, and improves the accuracy of point cloud registration. The application can be applied to model registration tasks in a complex part digitization detection process.