907 results about "Point cloud processing" patented technology

The invention discloses a point cloud semantic segmentation method and system based on local neighborhood attention, and belongs to the technical field of three-dimensional point cloud processing and deep learning, and the method comprises the following steps: S101, dividing three-dimensional point cloud data into training, verification and test sets, and carrying out the preprocessing; s102, constructing a deep learning network based on local neighborhood attention, training by using the training set, and obtaining an optimal model by using the verification set; and S103, inputting the test set into the optimal model to obtain a semantic segmentation result. According to the method, the semantic segmentation model is constructed by fusing local neighborhood attention of global position coding and local position coding, so that the problem of poor space geometric structure information extraction due to the fact that a conventional model only pays attention to the relative position of points in a neighborhood is effectively solved, and the feature extraction capability of an attention mechanism is enhanced; and the robustness, generalization and segmentation accuracy of the three-dimensional point cloud semantic segmentation model are improved.

This invention disclosed a full-space intelligent detection method and system for underground drainage networks, as well as storage media, including the following steps: image acquisition, intelligent image denoising, internal pipe defect segmentation, concealed defect detection around the pipe, 3D reconstruction with volume quantification, and pipeline life prediction. This invention introduced a bionic four-wheel-drive, all-terrain detection robot that can effectively navigate through mud and flowing water-challenges that hinder traditional detection devices. By leveraging deep learning algorithms as well as various techniques of computing vision, 3D reconstruction, and point cloud processing, the system thoroughly analyzed collected data to determine defect types and precise locations. Utilizing this analysis, precise location of different defect type and quantitative measurement of their dimensions can be realized. Based on the data analysis results, a deep-learning driven model was developed for predicting pipeline longevity to support maintenance staff with timely information on pipe defects and operational lifespan.

A three-dimensional point cloud segmentation method and apparatus based on a locally weighted curvature and a two-point method, which method and apparatus relate to the field of point cloud processing. The segmentation method comprises: calculating a normal vector of each sample point in a three-dimensional point cloud image (S101); calculating a locally weighted curvature of each sample point on the basis of the normal vector, and comparing the locally weighted curvature with a preset curvature threshold value, so as to obtain a comparison result (S102); when the comparison result indicates that the locally weighted curvature of the sample point is greater than or equal to the preset curvature threshold value, marking the sample point as a boundary feature point, so as to obtain a boundary feature point set (S103); fitting a point cloud planar model on the basis of the boundary feature point set, and performing iterative calculation on the point cloud planar model, so as to obtain a target inner point set (S104); and segmenting the three-dimensional point cloud image on the basis of the boundary feature point set and the target inner point set (S105).

The invention relates to the technical field of computer vision and industrial automation, in particular to a shoe sole three-dimensional gluing contour extraction system and method based on vision single-line laser, and aims to solve the problems that virtual calibration target spots cannot be accurately generated based on shoe sole geometry, the positions and sizes of the target spots are difficult to determine by combining curvature extreme values and principal component analysis in the prior art, and the production cost is low. The problem that a double-branch deep learning model cannot be adopted to fuse feature prediction transformation, and the re-projection error is increased is solved; a virtual calibration target spot is automatically generated based on sole geometry through a feature fusion calibration module, a grid is generated through point cloud processing and Poisson reconstruction, the position and size of the target spot are determined by combining a curvature extreme value and principal component analysis, a corresponding relation is established by utilizing two-dimensional and three-dimensional feature matching, initial alignment is realized through ICP and re-projection error optimization, and the target spot position and size are determined. A double-branch deep learning model is adopted to be fused with feature prediction transformation, iterative optimization is carried out through space consistency errors, and re-projection errors are reduced.

The invention relates to the field of welding, and discloses a non-standard intelligent customized welding system based on vision and surface gradient, which comprises a visual perception module used for iteratively approaching a workpiece from a preset initial height through a self-adaptive high angle shooting exploration mechanism, and combining layered grid division based on a camera view and a progressive multi-angle expansion scanning mode, collecting multi-view three-dimensional point cloud data of the workpiece; and the point cloud processing module is used for performing regional progressive registration on the multi-view three-dimensional point cloud data, implementing threshold constraint based on point cloud curvature characteristics by dynamically adjusting registration step length, and eliminating accumulative errors in combination with pose map optimization. Through self-adaptive high-angle shooting and layered grid scanning, multi-view point cloud can be automatically collected without manually presetting a workpiece model, regional registration and curvature constraint are combined, the workpiece model is constructed, a weld joint structure is automatically extracted based on surface gradient features, traditional manual labeling is replaced, a welding track is generated according to weld joint topology, and the precision is dynamically corrected.

The invention provides a high-precision intelligent welding system adapting to complex working conditions, and relates to the technical field of laser welding. The high-precision intelligent welding system adapting to the complex working conditions comprises the steps of S1, based on a high-resolution visual sensor and a laser scanner, S2, based on preprocessed point cloud data, S3, based on a workpiece three-dimensional model, S4, based on an optimized welding path and a material attribute database, S5, based on a dynamic welding parameter set, S6, based on a real-time welding track, and S7, based on multi-sensor monitoring data. S8, based on a defect detection report; and S9, based on a repaired welding result and historical welding data. According to the invention, the geometric data of the workpiece are collected through the high-resolution visual sensor and the laser scanner, the filtering algorithm is applied to remove noise to generate high-quality point cloud data, and a subsequent processing foundation is laid; workpiece features are identified by using a point cloud processing algorithm and a machine learning method based on the point cloud data, and an accurate digital model is generated through a three-dimensional reconstruction algorithm.

The invention relates to the field of measurement, in particular to a subway tunnel secondary lining section limit measurement method based on SLAM mobile scanning, which comprises the following steps: step 1, tunnel point cloud acquisition and target clustering point cloud acquisition; step 2, CPIII control point coordinate extraction; step 3, point cloud coordinate conversion; 4, performing point cloud processing, and removing noisy points and non-secondary-lining target point cloud data in the tunnel secondary-lining point cloud; and step 5, performing three-dimensional limit deviation analysis, performing spatial comparative analysis on the point cloud under the construction coordinate system and the subway secondary lining tunnel design three-dimensional model by adopting a voxelization deviation mapping algorithm, extracting a continuous deviation region through voxel clustering, and generating a three-dimensional deviation thermodynamic diagram. According to the method, the two-lining tunnel point cloud obtained by SLAM mobile scanning is accurately converted into a construction coordinate system by combining CPIII high-precision control points in the tunnel, rapid, visual and three-dimensional contrastive analysis with design of a three-dimensional model can be achieved, and visual and reliable data support is provided for line and slope adjustment, track laying and defect improvement.

The invention discloses an efficient denoising power transmission line point cloud processing method and system, and relates to the technical field of three-dimensional point cloud data processing and intelligent analysis, and the method comprises the steps: carrying out the segmentation processing of original power transmission line point cloud data, dividing the point cloud into power equipment parts, carrying out the local density analysis of the segmented point cloud, and generating a density distribution diagram; based on the density distribution diagram and the local geometric features of the point clouds, multi-stage filtering is carried out, denoising model parameters are optimized, a statistical filtering method is combined to remove noise points, curvature features and normal vector consistency features of the point clouds are extracted, classification feature vectors are generated, and based on the classification feature vectors, fine classification is carried out on the point clouds. Through data preprocessing, multi-stage filtering, self-supervised learning, feature extraction and refined classification, the technical defects of insufficient multi-modal feature fusion efficiency and poor equipment feature adaptability in power transmission line point cloud denoising are effectively solved.

The invention discloses a shield muck volume estimation method and system based on image processing, and particularly relates to the technical field of tunnel engineering monitoring, and the method comprises the steps: obtaining muck RGB and depth images through an image collection device, generating high-precision three-dimensional point cloud data through the combination of laser scanning and a multispectral technology, and calculating the muck volume through a slicing method. And fusing the mass flow and water content data of the belt weigher, dynamically generating a calibration factor, and correcting a volume estimation result in real time. The system comprises an image acquisition module, a point cloud processing module, a volume calculation module and a dynamic calibration module, has the characteristics of automation, high precision, real-time feedback and the like, effectively solves the problems of large error and slow response of a traditional manual metering and single weighing mode, and is suitable for continuous and accurate monitoring of the volume of muck in the shield construction process.

The invention relates to a point cloud static denoising method based on geometric constraint and deep learning, belongs to the technical field of point cloud processing, and solves the problem of poor point cloud denoising effect caused by poor geometric fidelity and weak generalization ability for complex noise in the prior art. Comprising the following steps: after preprocessing a to-be-denoised point cloud, dividing the to-be-denoised point cloud into a plurality of point cloud blocks with overlapping regions; for each point cloud block, performing multi-round iterative optimization on the coordinates of each point cloud by using the trained denoising network, and optimizing the predicted coordinates of each point cloud output by the denoising network in each round of iteration by using normal guided filtering and incremental updating to obtain the coordinates of each point cloud for the next round of iteration; the denoising network sequentially comprises a dynamic graph convolutional network, a first multi-layer perceptron and a second multi-layer perceptron; and after the optimization results of the point cloud blocks are fused, carrying out post-processing filtering to obtain a denoised point cloud. The point cloud denoising precision and the detail retention capability are improved.

The invention discloses a laser welding seam online quality detection system and method based on multi-mode visual fusion, and particularly relates to the technical field of laser welding seam quality detection. Comprising a data acquisition and synchronous control unit, a 2D image processing and defect detection unit, a 3D point cloud processing and size measurement unit, a multi-modal information fusion and collaborative analysis unit and a comprehensive quality judgment and output unit. According to the laser welding seam online quality detection system and method based on multi-modal visual fusion, the 2D and 3D multi-modal visual fusion technology is adopted, an improved defect detection algorithm and an accurate size measurement method are combined, welding seam surface defects are accurately recognized through 2D images, key size data are obtained by means of 3D point cloud, and the detection accuracy is improved. And the limitation of'heavy defects and light sizes' or'heavy sizes and light defects' in single-modal detection is avoided, and comprehensive and accurate evaluation of the welding seam quality is realized.

The invention relates to the technical field of point cloud processing, and discloses a point cloud data processing method, system and device for industrial three-dimensional model construction. The method comprises the following steps: carrying out denoising and down-sampling processing on original point cloud data of an industrial part to obtain preprocessed point cloud data, calculating a local geometric descriptor, converting the local geometric descriptor into an industrial feature recurrent plot through a recurrent plot algorithm, inputting the recurrent plot into a convolutional neural network to extract a high-dimensional geometric feature vector, and mapping the feature vectors into B-spline curved surface control parameters through a deep belief network, and performing point cloud reconstruction quality evaluation according to the control parameters to obtain geometric accuracy error data and curved surface reconstruction integrity data. According to the method, the problems that in existing industrial three-dimensional model construction, a point cloud data processing method lacks intelligent feature recognition, the reconstruction precision is insufficient, and manufacturing constraints are not fully considered are solved, and the recognition accuracy of geometric features of complex industrial parts and the precision of three-dimensional model reconstruction are improved.

The invention relates to the technical field of line laser three-dimensional scanning, in particular to a reference coordinate system calibration method based on line laser three-dimensional scanning, which comprises the following steps of: calibrating camera parameters; calibrating a light plane equation of the laser; obtaining a calibration whiteboard image; establishing a reference coordinate system; and calculating a conversion matrix from the camera coordinate system to the reference coordinate system. The reference coordinate system of the line laser three-dimensional scanner can be determined only by additionally acquiring a white board image during calibration, and whether the current object is located in the measurement range or not can be determined according to the z coordinate value of the point cloud data by converting the acquired three-dimensional point cloud data of the measured object to the reference coordinate system. And the efficiency and the accuracy of subsequent point cloud processing and three-dimensional measurement are greatly improved. Therefore, the problem that an existing calibration method is low in measurement precision is solved.

The invention discloses a cultural heritage digital reconstruction system based on deep learning, and the system comprises a data collection module, a data preprocessing module, a feature description module, a modeling module, and a model optimization module. The feature description module is used for acquiring data processed by the data preprocessing module, feature extraction is performed on point cloud data through a point cloud processing network in deep learning, the data preprocessing module is used for data preprocessing, the data preprocessing comprises correction, denoising, color correction and cutting, and the modeling module is used for modeling the point cloud data according to the extracted features. Three-dimensional reconstruction of the cultural heritage is carried out, and a model optimization module is used for smoothing a generated cultural heritage model, filling the deficiency and eliminating noise; the cultural heritage digital reconstruction method has the advantages that the digital system has rich details and sense of reality, people can conveniently visit and learn, and the efficiency and precision of cultural heritage digital reconstruction are improved.

The invention discloses a hybrid teaching method and system and electronic equipment, and the method comprises the steps: collecting and preprocessing teaching environment point cloud data, generating a point cloud scene model through a decoupling sparse prior guide diffusion compression model, and improving the display quality through resistance diffusion compression. And deploying a teaching management system based on the micro-service architecture to realize multi-user real-time interaction, collecting interaction behavior data, and performing deep learning analysis to generate an evaluation report. Through the point cloud processing and image optimization method, the construction efficiency and image quality of the virtual teaching environment are greatly improved, distributed deployment and real-time interaction capability of the teaching platform are realized, and technical support is provided for hybrid teaching.

The invention relates to the field of livestock breeding, in particular to a cattle body size performance measurement system based on a binocular camera, which comprises a binocular camera acquisition module, a posture recognition and key point extraction module, a point cloud processing module, a posture correction module and a data processing and optimization module. According to the invention, the binocular camera is combined with an advanced algorithm, so that the measurement efficiency in the cattle breeding process is greatly improved; through cooperative work of multiple modules, reliable data support is provided for cattle breeding; manual measurement is not needed in the whole process, manual operation errors are avoided, the labor intensity of workers is reduced, meanwhile, stress reactions of cattle caused by manual fixing and measurement are reduced, and healthy growth of the cattle and scientization and humanization of breeding management are facilitated; the system can adapt to different light conditions, cattle body types and motion states, can stably and accurately complete body size performance measurement tasks no matter in an indoor breeding environment or in an outdoor pasture environment, and has a wide application prospect.

The invention discloses a space-ground-air integrated multi-source data acquisition and fusion method based on highway facilities, and relates to the field of data acquisition and fusion, and the method comprises the steps: obtaining the three-dimensional geographic information data of the highway facilities based on a diversified data acquisition technology, and carrying out the fusion processing of the three-dimensional geographic information data through employing a data fusion registration algorithm, outputting multi-source data information; verifying the fusion precision of the multi-source data information by using the elite centroid and a reverse learning strategy, and performing secondary fusion processing of the three-dimensional geographic information data according to a verification result to obtain a multi-source data fusion result; and performing triangulation network reconstruction processing on the multi-source data fusion result based on a triangulation algorithm, and performing visual display on the reconstruction result, the multi-source data fusion result and the three-dimensional geographic information data by using a three-dimensional digital display technology. According to the invention, multi-source point cloud processing software is used to carry out precision check and other operations on the fused point cloud, so that the integrity and smoothness of the point cloud data are ensured.

The invention discloses a laser scanning optimization system for high-precision ellipticity and diameter measurement, and relates to the technical field of cylindrical workpiece geometric dimension measurement. Comprising a laser contourgraph assembly, the laser contourgraph assembly comprises four laser contourgraphs, the four laser contourgraphs are arranged in the circumferential direction of the cross section of a to-be-measured workpiece at equal intervals, the included angle between every two adjacent laser contourgraphs is 90 degrees, and the laser contourgraphs are used for synchronously collecting contour point cloud data of the surface of the to-be-measured workpiece; and the data acquisition module is used for receiving and summarizing the contour point cloud data. The four laser contourgraphs are arranged at equal intervals in the circumferential direction of the cross section of the workpiece and collect synchronously, so that the problems that a traditional single laser module is incomplete in rotary scanning coverage and has timeliness deviation are solved; and the point cloud processing module improves the condition of insufficient point cloud processing refinement through splicing, direction correction, section extraction, adaptive region division and arc length interception processing.

The invention discloses a dynamic graph convolution-based target point cloud completion method for a battery replacement robot, and the method comprises the steps: 1, reconstructing a complete target fastener model from a multi-view image through SFM and MVS technologies, and obtaining complete point cloud data; 2, constructing an incomplete-complete point cloud pair as training data by using a geometric constraint-based adaptive cutting strategy; 3, multi-resolution point cloud processing is adopted, and feature extraction and fusion are carried out according to three-level resolution; 4, on the basis of DGCNN dynamic graph convolution, in combination with multi-stage Edge Conv dynamic edge convolution and a Transform coding module, local geometric feature capture and global relation perception are realized; 5, point cloud generation adopts a pyramid step-by-step refining method, geometric details are added to each layer based on a previous layer result, and the number of points is gradually expanded; and 6, the training process is guided through a multi-target joint loss function, and the point cloud quality is improved while the precision is ensured. The geometric structure integrity of the point cloud of the target fastener is improved, and the operation error of follow-up operation of the battery replacement robot is reduced.

The embodiment of the invention provides a cylinder detection method and device, electronic equipment and a storage medium, and relates to the technical field of point cloud processing. The method comprises the following steps of: segmenting a point cloud to be detected to obtain a plurality of segmented parts, selecting a projection surface for each segmented part, projecting the segmented parts to obtain a projection point cloud, simplifying the projection point cloud to obtain a simplified projection point cloud, clustering the simplified projection point cloud to obtain at least one clustering point cloud, and obtaining at least one clustering point cloud. And performing cylinder detection on each clustering point cloud to determine all cylinders in the segmentation part. According to the method, the to-be-detected point cloud is segmented, the segmented part is projected and simplified, clustering is carried out, and cylinder detection is carried out on each cluster, so that all short cylinders in the point cloud can be detected more accurately.

The invention discloses a multilevel feature fusion adaptive point cloud registration method, and relates to the field of point cloud processing technology and industrial visual inspection technology, and the method comprises the steps: carrying out the preprocessing of a point cloud pair; multi-scale feature extraction is carried out on the preprocessed point cloud pair, a feature pyramid is constructed through the extracted multi-scale features, and a top-down guide mechanism is adopted between layers of the feature pyramid; performing preliminary feature fusion on feature points of the point cloud pair based on the extracted multi-scale features; performing multi-level registration on the point cloud pair; the multiple layers comprise first-layer registration, second-layer registration and third-layer registration, a transformation matrix of each layer is obtained after each layer of registration is completed, and registration is carried out on the to-be-registered point cloud through the transformation matrix of each layer; according to the method, a multi-scale feature extraction strategy is adopted, three feature descriptors with different scales, namely ESF, SHOT and FPFH feature descriptors, are selected for the degradation problem in point cloud registration, and feature extraction and parameter adaptive adjustment from macroscopic to microscopic are achieved.

The invention provides a building instantiation segmentation method and device, a terminal and a storage medium, and relates to the technical field of three-dimensional point cloud processing, and the method comprises the steps: carrying out the plane segmentation of a building point cloud of a target building, and extracting a potential vertical plane point cluster from a building plane point cluster obtained through the segmentation; mapping all three-dimensional point clouds in the potential vertical plane point cluster to a two-dimensional feature space to obtain a two-dimensional point set, and processing the two-dimensional point set by using a preset dual-threshold judgment mechanism to extract a building vertical structure point cluster; generating a building distance map based on the building vertical structure point clusters, and generating instance anchor points on the distance map; extracting a multi-level contour in the distance map based on the instance anchor points, constructing a tree-shaped hierarchical relationship corresponding to the multi-level contour, and constructing a mark matrix based on the tree-shaped hierarchical relationship; and building two-dimensional instantiation segmentation is carried out on the distance map based on the mark matrix. Through the unsupervised hierarchical segmentation framework, the dependence on the annotated data can be eliminated, and the segmentation precision is improved.

The invention provides a side slope radar point cloud data slice abnormal value complementing method based on space-time fusion, which comprises the following steps: acquiring point cloud data of a plurality of time slices obtained by scanning of a side slope radar, and mapping the point cloud data into a three-dimensional point cloud; identifying a piece of abnormal regions of the three-dimensional point cloud through statistical filtering and a density-based clustering method; constructing a point cloud processing model based on PointNet + +, generating masked data and a mask index by adopting a mask strategy, and masking off pieces of point clouds; spatial features are extracted in different regions, static space weight matrixes generated by longitude and latitude and time difference global features are fused, deformation values of mask regions are predicted through a full-connection network, and model parameters are optimized; and inputting the trained model into latitude and longitude coordinates of an abnormal region of actual missing point cloud data, performing abnormal value completion, outputting a completed deformation value, and calculating a completion precision index. According to the method, the completion effect is good, all abnormal values of multiple time slices can be completed at a time, and the calculation efficiency is improved.

The invention relates to the technical field of computer vision and three-dimensional measurement, in particular to a three-dimensional point cloud semantic segmentation and measurement reference surface intelligent fitting system and a method thereof, which are mainly applied to scenes needing accurate three-dimensional space understanding, such as topographic survey, mining, urban planning, intelligent driving and industrial detection. The system comprises a data acquisition module, a preprocessing module, a feature extraction module, a reference surface acquisition module and a pixel feature conversion module. Three-dimensional point cloud data are collected through multi-sensor fusion, and the data quality and integrity are improved. A cascade filtering preprocessing process is innovatively designed, and the data quality is remarkably improved. The feature extraction module generates semantic tags, the reference surface acquisition module calculates a probability optimal fitting plane, and the pixel feature conversion module realizes multi-scale feature output. According to the method, the three-dimensional point cloud processing precision and efficiency are effectively improved, and powerful technical support is provided for the field of computer vision and three-dimensional measurement.

The invention relates to the technical field of large-scale structure measurement, and discloses a robot laser vision three-dimensional scanning measurement system for a large-scale structural member. The system comprises a robot motion platform, a laser vision sensing module, an ambient light sensing module, a point cloud processing module and a motion control module, wherein the laser vision sensing module is arranged at the tail end of the robot motion platform. The ambient light sensing module collects ambient light intensity distribution data in real time, and the motion control module combines the data with three-dimensional model data of the large-scale structural member to be measured to generate a multi-view dynamic scanning path instruction. The robot motion platform executes an instruction to drive the laser vision sensing module to move, the module projects laser stripes to the surface of a to-be-measured structural member and synchronously collects an image, the point cloud processing module performs adaptive filtering processing on the image and then generates three-dimensional point cloud data, and the requirement for efficient and accurate measurement of a large structural member in a complex industrial environment can be met.

The invention relates to the technical field of terrain monitoring, and discloses a terrain change monitoring system based on image analysis, and the system is characterized in that an image obtaining module is used for obtaining multi-source image data, carrying out the fusion, and generating a fused image; the preprocessing module is used for preprocessing the fused image to obtain a preprocessed to-be-analyzed image; the analysis module is used for constructing a dual-channel deep convolutional network, analyzing the preprocessed to-be-analyzed image and detecting a topographic change area; the generation module is used for processing the detected terrain change area by adopting an incremental point cloud processing technology, and generating a three-dimensional terrain difference model and a settlement area volume change thermodynamic diagram in real time by combining a Poisson surface reconstruction algorithm; the early warning module is used for analyzing the generated three-dimensional terrain difference model and the volume change thermodynamic diagram, calculating parameters of terrain change and performing early warning processing; according to the method, the topographic change is accurately evaluated, and the accuracy and reliability of early warning are improved.

The invention provides a slope deformation monitoring method based on image point cloud modeling, and the method comprises the steps: 1, obtaining an image; 2, carrying out the point cloud conversion, and generating point cloud data through a shot picture; and step 3, carrying out point cloud processing, wherein the point cloud processing comprises preprocessing, point cloud filtering and point cloud fine processing. Step 4, DEM generation: generating a DEM according to the obtained slope point cloud elevation data; and step 5, visualization processing: reading multi-stage DEM data and difference data, and carrying out visualization processing on the DEM data after difference. The portable device is used for shooting the image and processing the image to generate the high-precision point cloud data, so that efficient, real-time and low-cost slope deformation monitoring is realized, comprehensive deformation information and an intuitive visual chart are provided, meanwhile, an automatic early warning function is achieved, and the accuracy and timeliness of slope safety management are remarkably improved.

The invention discloses a multi-stage Mama point cloud completion method and device based on semantic and geometric guidance, and relates to the field of point cloud processing, and the method comprises the steps: constructing and training a multi-stage Mama point cloud completion model based on semantic and geometric guidance, and obtaining a trained multi-stage Mama point cloud completion model; the multi-sorting strategy Mama decoder unit comprises a plurality of stages of multi-sorting strategy Mama decoders which are connected in sequence; obtaining an incomplete point cloud to be complemented and inputting the incomplete point cloud to the trained multi-stage Mama point cloud complementation model, enabling the incomplete point cloud to pass through a Transform-Mama combined point cloud local feature coding unit to obtain coding features, and inputting the coding features into a sparse point cloud generation unit to obtain sparse point cloud; the sparse point clouds are input into the multi-sorting strategy Mama decoder unit to obtain decoding features, the decoding features pass through the point cloud up-sampling unit to obtain predicted complete point clouds, and the problems of quadratic complexity and local detail loss of an existing Transform encoder-decoder structure are solved.

The invention discloses a method for calculating the area of ​​paint based on three-dimensional scanning technology, which belongs to the technical field of paint area calculation. The method comprises: based on object surface point cloud data obtained by a three-dimensional laser scanner, a point cloud processing algorithm is used to construct a point cloud model of the object surface, surface shape characteristics and reflection intensity characteristics are analyzed, the point cloud boundary of the paint area is extracted, a point cloud model of the paint surface is established, a normal vector of a triangular network model is calculated by a differential geometry method, and a region is segmented to divide the surface into a plane region and a curved surface region. The area of ​​the plane region of the paint is calculated by accumulation, and the area of ​​the curved surface region is obtained by a numerical integration method. Small feature regions are extracted based on a convolutional network, and a local point cloud surface is reconstructed by an Alpha shape algorithm to calculate the small feature area. The total paint area of ​​the painted object is generated by comprehensively calculating the areas of the plane region, the curved surface region and the small feature region of the paint.

A novel method of classifying point cloud data by extending the Gray-level Co-occurrence Matrix (GLCM) technique from the 2D to the sparse 3D domain is described herein. The method is able to be applied to point clouds derived from a mesh collection / meshes (such as, the Real-World Textured Things (RWTT) mesh collection). Implementations designed for multiple purposes are described herein: sampling and quantization of RWTT meshes, generation of GLCMs and corresponding texture descriptors, and the selection of potential candidate point clouds based on these extracted descriptors.