The purpose of reverse engineering is to convert a large point cloud into a CAD model. In reverse engineering, the key issue is segmentation, i.e. studying how to subdivide the point cloud into smaller regions, where ...The purpose of reverse engineering is to convert a large point cloud into a CAD model. In reverse engineering, the key issue is segmentation, i.e. studying how to subdivide the point cloud into smaller regions, where each of them can be approximated by a single surface. Segmentation is relatively simple, if regions are bounded by sharp edges and small blends; problems arise when smoothly connected regions need to be separated. In this paper, a modified self-organizing feature map neural network （SOFM） is used to solve segmentation problem. Eight dimensional feature vectors （3-dimensional coordinates, 3-dimensional normal vectors, Gaussian curvature and mean curvature） are taken as input for SOFM. The weighted Euclidean distance measure is used to improve segmentation result. The method not only can deal with regions bounded by sharp edges, but also is very efficient to separating smoothly connected regions. The segmentation method using SOFM is robust to noise, and it operates directly on the point cloud. An examples is given to show the effect of SOFM algorithm.展开更多
3D laser scanning technology is widely used in underground openings for high-precision,rapid,and nondestructive structural evaluations.Segmenting large 3D point cloud datasets,particularly in coal mine roadways with m...3D laser scanning technology is widely used in underground openings for high-precision,rapid,and nondestructive structural evaluations.Segmenting large 3D point cloud datasets,particularly in coal mine roadways with multi-scale targets,remains challenging.This paper proposes an enhanced segmentation method integrating improved PointNet++with a coverage-voted strategy.The coverage-voted strategy reduces data while preserving multi-scale target topology.The segmentation is achieved using an enhanced PointNet++algorithm with a normalization preprocessing head,resulting in a 94%accuracy for common supporting components.Ablation experiments show that the preprocessing head and coverage strategies increase segmentation accuracy by 20%and 2%,respectively,and improve Intersection over Union(IoU)for bearing plate segmentation by 58%and 20%.The accuracy of the current pretraining segmentation model may be affected by variations in surface support components,but it can be readily enhanced through re-optimization with additional labeled point cloud data.This proposed method,combined with a previously developed machine learning model that links rock bolt load and the deformation field of its bearing plate,provides a robust technique for simultaneously measuring the load of multiple rock bolts in a single laser scan.展开更多
The point segmentation of power lines and towers aims to use unmanned aerial vehicles(UAVs)for the inspection of power facilities,risk detection and modelling.Because of the unclear spatial relationship between the po...The point segmentation of power lines and towers aims to use unmanned aerial vehicles(UAVs)for the inspection of power facilities,risk detection and modelling.Because of the unclear spatial relationship between the point clouds,the point segmentation of power lines and towers is challenging.In this paper,the power line and tower point datasets are constructed using Light Detection and Ranging(LiDAR)and a point segmentation method is proposed based on multiscale density features and a point-based deep learning network.First,the data are blocked and the neighbourhood is constructed.Second,the point clouds are downsampled to produce sparse point clouds.The point clouds before and after sampling are rotated,and their density is calculated.Next,a direct mapping method is selected to fuse the density information;a lightweight network is built to learn the features.Finally,the point clouds are segmented by concatenating the local features provided by PointCNN.The algorithm performs effectively on different types of power lines and towers.The mean interaction over union is 82.73%,and the overall accuracy can reach 91.76%.This approach can achieve the end-to-end integration of segmentation and provide theoretical support for the segmentation of large scenic point clouds.展开更多
Volume parameter is the basic content of a spatial body object morphology analysis.However,the challenge lies in the volume calculation of irregular objects.The point cloud slicing method proposed in this study effect...Volume parameter is the basic content of a spatial body object morphology analysis.However,the challenge lies in the volume calculation of irregular objects.The point cloud slicing method proposed in this study effectively works in calculating the volume of the point cloud of the spatial object obtained through three-dimensional laser scanning(3DLS).In this method,a uniformly spaced sequent slicing process is first conducted in a specific direction on the point cloud of the spatial object obtained through 3DLS.A series of discrete point cloud slices corresponding to the point cloud bodies are then obtained.Subsequently,the outline boundary polygon of the point cloud slicing is searched one by one in accordance with the slicing sequence and areas of the polygon.The point cloud slice is also calculated.Finally,the individual point cloud section volume is calculated through the slicing areas and the adjacent slicing gap.Thus,the total volume of the scanned spatial object can be calculated by summing up the individual volumes.According to the results and analysis of the calculated examples,the slice-based volume-calculating method for the point cloud of irregular objects obtained through 3DLS is correct,concise in process,reliable in results,efficient in calculation methods,and controllable on accuracy.This method comes as a good solution to the volume calculation of irregular objects.展开更多
As an important tropical cash crop,rubber trees play a key role in the rubber industry and ecosystem.However,a significant challenge in precision agriculture and refined management of rubber plantation lies in the lim...As an important tropical cash crop,rubber trees play a key role in the rubber industry and ecosystem.However,a significant challenge in precision agriculture and refined management of rubber plantation lies in the limitations of traditional point cloud segmentation methods,which struggle to accurately extract structural parameters and capture the spatial layout of individual rubber trees.Therefore,we propose an optimized dual-channel clustering method for the UAV LiDAR-based Rubber Tree Point Cloud Segmentation Network(RsegNet)for improved assessment of rubber tree architecture and traits.Firstly,we designed a cosine feature extraction network,termed CosineU-Net,to address the branch-and-leaf overlap problem by calculating the cosine similarity of the spatial and positional features of each point,leveraging deep learning approaches to improve feature representation.Secondly,we constructed a dual-channel clustering module reducing prediction error in rubber tree point cloud data,integrating multi-class association and background classification to tackle background interference.The cluster identification and separation accuracy in high-dimensional data processing is enhanced through a dy-namic clustering optimization algorithm.In our self-built dataset and across five regions of the FOR-instance forest dataset,RsegNet achieved the best performance compared to five state-of-the-art networks,reaching an F-score of 86.1%.This method calculated structural attributes including height,crown diameter,and volume for rubber trees in three areas under different environments in Danzhou City,Hainan Province,providing robust support for precise monitoring,plantation management,and health assessment.展开更多
This paper proposes a novel density-based real-time segmentation algorithm,to extract ground point cloud in real time from point cloud data collected by roadside LiDAR.The algorithm solves the problems such as the lar...This paper proposes a novel density-based real-time segmentation algorithm,to extract ground point cloud in real time from point cloud data collected by roadside LiDAR.The algorithm solves the problems such as the large amount of original point cloud data collected by LiDAR,which leads to heavy computational burden in ground point search.First,point cloud data is filtered by straight-through filtering method and rasterized to improve the real-time performance of the algorithm.Then,the density of the point cloud in horizontal plane is calculated,and the threshold of the density is selected to extract the low-density regional point cloud according to the density statistical histogram and 95%loci.Finally,the low-density regional point cloud is used as the initial ground seeds for iterative optimization of ground parameters,and the ground point cloud is extracted by the fitted ground model to realize road point cloud extraction.The experimental results on 1055 frames of continuous data collected on real scenes show that the average time consumption of the proposed method is 0.11 s,and the average segmentation precision is 92.48%.This shows that the density-based road segmentation algorithm can reduce the time of point cloud traversal in the process of ground parameter fitting and improve the real-time performance of the algorithm while maintaining the accuracy of ground extraction.展开更多
Non-destructive detection of wire bonding defects in integrated circuits(IC)is critical for ensuring product quality after packaging.Image-processing-based methods do not provide a detailed evaluation of the three-dim...Non-destructive detection of wire bonding defects in integrated circuits(IC)is critical for ensuring product quality after packaging.Image-processing-based methods do not provide a detailed evaluation of the three-dimensional defects of the bonding wire.Therefore,a method of 3D reconstruction and pattern recognition of wire defects based on stereo vision,which can achieve non-destructive detection of bonding wire defects is proposed.The contour features of bonding wires and other electronic components in the depth image is analysed to complete the 3D reconstruction of the bonding wires.Especially to filter the noisy point cloud and obtain an accurate point cloud of the bonding wire surface,a point cloud segmentation method based on spatial surface feature detection(SFD)was proposed.SFD can extract more distinct features from the bonding wire surface during the point cloud segmentation process.Furthermore,in the defect detection process,a directional discretisation descriptor with multiple local normal vectors is designed for defect pattern recognition of bonding wires.The descriptor combines local and global features of wire and can describe the spatial variation trends and structural features of wires.The experimental results show that the method can complete the 3D reconstruction and defect pattern recognition of bonding wires,and the average accuracy of defect recognition is 96.47%,which meets the production requirements of bonding wire defect detection.展开更多
Fast and accurate measurement of the volume of earthmoving materials is of great signifcance for the real-time evaluation of loader operation efciency and the realization of autonomous operation. Existing methods for ...Fast and accurate measurement of the volume of earthmoving materials is of great signifcance for the real-time evaluation of loader operation efciency and the realization of autonomous operation. Existing methods for volume measurement, such as total station-based methods, cannot measure the volume in real time, while the bucket-based method also has the disadvantage of poor universality. In this study, a fast estimation method for a loader’s shovel load volume by 3D reconstruction of material piles is proposed. First, a dense stereo matching method (QORB–MAPM) was proposed by integrating the improved quadtree ORB algorithm (QORB) and the maximum a posteriori probability model (MAPM), which achieves fast matching of feature points and dense 3D reconstruction of material piles. Second, the 3D point cloud model of the material piles before and after shoveling was registered and segmented to obtain the 3D point cloud model of the shoveling area, and the Alpha-shape algorithm of Delaunay triangulation was used to estimate the volume of the 3D point cloud model. Finally, a shovel loading volume measurement experiment was conducted under loose-soil working conditions. The results show that the shovel loading volume estimation method (QORB–MAPM VE) proposed in this study has higher estimation accuracy and less calculation time in volume estimation and bucket fll factor estimation, and it has signifcant theoretical research and engineering application value.展开更多
Point cloud segmentation is a fundamental problem. Due to the complexity of real-world scenes and the limitations of 3D scanners, interactive segmentation is currently the only way to cope with all kinds of point clou...Point cloud segmentation is a fundamental problem. Due to the complexity of real-world scenes and the limitations of 3D scanners, interactive segmentation is currently the only way to cope with all kinds of point clouds. However, interactively segmenting complex and large-scale scenes is very time-consuming.In this paper, we present a novel interactive system for segmenting point cloud scenes. Our system automatically suggests a series of camera views, in which users can conveniently specify segmentation guidance. In this way, users may focus on specifying segmentation hints instead of manually searching for desirable views of unsegmented objects, thus significantly reducing user effort. To achieve this, we introduce a novel view preference model, which is based on a set of dedicated view attributes, with weights learned from a user study. We also introduce support relations for both graph-cut-based segmentation and finding similar objects. Our experiments show that our segmentation technique helps users quickly segment various types of scenes, outperforming alternative methods.展开更多
基金Supported by the National Natural Science Foundation of China(60573177), the Aeronautical Science Foundation of China (04H53059) , the natural Science Foundation of Henan Province (200510078010) and Youth Science Foundation at North China Institute of Water Conservancy and Hydroelectric Power(HSQJ2004003)
文摘The purpose of reverse engineering is to convert a large point cloud into a CAD model. In reverse engineering, the key issue is segmentation, i.e. studying how to subdivide the point cloud into smaller regions, where each of them can be approximated by a single surface. Segmentation is relatively simple, if regions are bounded by sharp edges and small blends; problems arise when smoothly connected regions need to be separated. In this paper, a modified self-organizing feature map neural network （SOFM） is used to solve segmentation problem. Eight dimensional feature vectors （3-dimensional coordinates, 3-dimensional normal vectors, Gaussian curvature and mean curvature） are taken as input for SOFM. The weighted Euclidean distance measure is used to improve segmentation result. The method not only can deal with regions bounded by sharp edges, but also is very efficient to separating smoothly connected regions. The segmentation method using SOFM is robust to noise, and it operates directly on the point cloud. An examples is given to show the effect of SOFM algorithm.
基金supported by the National Natural Science Foundation of China(Grant Nos.52304139,52325403)the CCTEG Coal Mining Research Institute funding(Grant No.KCYJY-2024-MS-10).
文摘3D laser scanning technology is widely used in underground openings for high-precision,rapid,and nondestructive structural evaluations.Segmenting large 3D point cloud datasets,particularly in coal mine roadways with multi-scale targets,remains challenging.This paper proposes an enhanced segmentation method integrating improved PointNet++with a coverage-voted strategy.The coverage-voted strategy reduces data while preserving multi-scale target topology.The segmentation is achieved using an enhanced PointNet++algorithm with a normalization preprocessing head,resulting in a 94%accuracy for common supporting components.Ablation experiments show that the preprocessing head and coverage strategies increase segmentation accuracy by 20%and 2%,respectively,and improve Intersection over Union(IoU)for bearing plate segmentation by 58%and 20%.The accuracy of the current pretraining segmentation model may be affected by variations in surface support components,but it can be readily enhanced through re-optimization with additional labeled point cloud data.This proposed method,combined with a previously developed machine learning model that links rock bolt load and the deformation field of its bearing plate,provides a robust technique for simultaneously measuring the load of multiple rock bolts in a single laser scan.
基金Chengdu University of Technology Postgraduate Innovative Cultivation Program(CDUT2022BJCX015).
文摘The point segmentation of power lines and towers aims to use unmanned aerial vehicles(UAVs)for the inspection of power facilities,risk detection and modelling.Because of the unclear spatial relationship between the point clouds,the point segmentation of power lines and towers is challenging.In this paper,the power line and tower point datasets are constructed using Light Detection and Ranging(LiDAR)and a point segmentation method is proposed based on multiscale density features and a point-based deep learning network.First,the data are blocked and the neighbourhood is constructed.Second,the point clouds are downsampled to produce sparse point clouds.The point clouds before and after sampling are rotated,and their density is calculated.Next,a direct mapping method is selected to fuse the density information;a lightweight network is built to learn the features.Finally,the point clouds are segmented by concatenating the local features provided by PointCNN.The algorithm performs effectively on different types of power lines and towers.The mean interaction over union is 82.73%,and the overall accuracy can reach 91.76%.This approach can achieve the end-to-end integration of segmentation and provide theoretical support for the segmentation of large scenic point clouds.
文摘Volume parameter is the basic content of a spatial body object morphology analysis.However,the challenge lies in the volume calculation of irregular objects.The point cloud slicing method proposed in this study effectively works in calculating the volume of the point cloud of the spatial object obtained through three-dimensional laser scanning(3DLS).In this method,a uniformly spaced sequent slicing process is first conducted in a specific direction on the point cloud of the spatial object obtained through 3DLS.A series of discrete point cloud slices corresponding to the point cloud bodies are then obtained.Subsequently,the outline boundary polygon of the point cloud slicing is searched one by one in accordance with the slicing sequence and areas of the polygon.The point cloud slice is also calculated.Finally,the individual point cloud section volume is calculated through the slicing areas and the adjacent slicing gap.Thus,the total volume of the scanned spatial object can be calculated by summing up the individual volumes.According to the results and analysis of the calculated examples,the slice-based volume-calculating method for the point cloud of irregular objects obtained through 3DLS is correct,concise in process,reliable in results,efficient in calculation methods,and controllable on accuracy.This method comes as a good solution to the volume calculation of irregular objects.
基金This work was supported by the Hainan Province Science and Technology Special Fund(Grant No.ZDYF2025XDNY113)the Central Public-interest Scientific Institution Basal Research Fund(Grant No.1630032022007)+2 种基金the Special Fund for Hainan Excellent Team"Rubber Genetics and Breeding"(Grant No.20210203)the Hunan Provincial Natural Science Foundation Project(Grant No.2025JJ50385)in part by the National Natural Science Foundation of China(Grant No.62276276).
文摘As an important tropical cash crop,rubber trees play a key role in the rubber industry and ecosystem.However,a significant challenge in precision agriculture and refined management of rubber plantation lies in the limitations of traditional point cloud segmentation methods,which struggle to accurately extract structural parameters and capture the spatial layout of individual rubber trees.Therefore,we propose an optimized dual-channel clustering method for the UAV LiDAR-based Rubber Tree Point Cloud Segmentation Network(RsegNet)for improved assessment of rubber tree architecture and traits.Firstly,we designed a cosine feature extraction network,termed CosineU-Net,to address the branch-and-leaf overlap problem by calculating the cosine similarity of the spatial and positional features of each point,leveraging deep learning approaches to improve feature representation.Secondly,we constructed a dual-channel clustering module reducing prediction error in rubber tree point cloud data,integrating multi-class association and background classification to tackle background interference.The cluster identification and separation accuracy in high-dimensional data processing is enhanced through a dy-namic clustering optimization algorithm.In our self-built dataset and across five regions of the FOR-instance forest dataset,RsegNet achieved the best performance compared to five state-of-the-art networks,reaching an F-score of 86.1%.This method calculated structural attributes including height,crown diameter,and volume for rubber trees in three areas under different environments in Danzhou City,Hainan Province,providing robust support for precise monitoring,plantation management,and health assessment.
基金supported by the National Key R&D Program of China(2021YFB3202200)S&T Program of Hebei(Nos.21340801D and 20310801D).
文摘This paper proposes a novel density-based real-time segmentation algorithm,to extract ground point cloud in real time from point cloud data collected by roadside LiDAR.The algorithm solves the problems such as the large amount of original point cloud data collected by LiDAR,which leads to heavy computational burden in ground point search.First,point cloud data is filtered by straight-through filtering method and rasterized to improve the real-time performance of the algorithm.Then,the density of the point cloud in horizontal plane is calculated,and the threshold of the density is selected to extract the low-density regional point cloud according to the density statistical histogram and 95%loci.Finally,the low-density regional point cloud is used as the initial ground seeds for iterative optimization of ground parameters,and the ground point cloud is extracted by the fitted ground model to realize road point cloud extraction.The experimental results on 1055 frames of continuous data collected on real scenes show that the average time consumption of the proposed method is 0.11 s,and the average segmentation precision is 92.48%.This shows that the density-based road segmentation algorithm can reduce the time of point cloud traversal in the process of ground parameter fitting and improve the real-time performance of the algorithm while maintaining the accuracy of ground extraction.
基金Intelligent Manufacturing and Robot Technology Innovation Project of Beijing Municipal Commission of Science and Technology and Zhongguancun Science and Technology Park Management Committee,Grant/Award Number:Z221100000222016National Natural Science Foundation of China,Grant/Award Number:62076014Beijing Municipal Education Commission and Beijing Natural Science Foundation,Grant/Award Number:KZ202010005004。
文摘Non-destructive detection of wire bonding defects in integrated circuits(IC)is critical for ensuring product quality after packaging.Image-processing-based methods do not provide a detailed evaluation of the three-dimensional defects of the bonding wire.Therefore,a method of 3D reconstruction and pattern recognition of wire defects based on stereo vision,which can achieve non-destructive detection of bonding wire defects is proposed.The contour features of bonding wires and other electronic components in the depth image is analysed to complete the 3D reconstruction of the bonding wires.Especially to filter the noisy point cloud and obtain an accurate point cloud of the bonding wire surface,a point cloud segmentation method based on spatial surface feature detection(SFD)was proposed.SFD can extract more distinct features from the bonding wire surface during the point cloud segmentation process.Furthermore,in the defect detection process,a directional discretisation descriptor with multiple local normal vectors is designed for defect pattern recognition of bonding wires.The descriptor combines local and global features of wire and can describe the spatial variation trends and structural features of wires.The experimental results show that the method can complete the 3D reconstruction and defect pattern recognition of bonding wires,and the average accuracy of defect recognition is 96.47%,which meets the production requirements of bonding wire defect detection.
基金Supported by National Key R&D Program of China(Grant Nos.2020YFB1709901 and 2020YFB1709904)National Natural Science Foundation of China(Grant Nos.51975495 and 51905460)+1 种基金Guangdong Provincial Basic and Applied Basic Research Foundation(Grant No.2021A1515012286)Guiding Funds of Central Government for Supporting the Development of the Local Science and Technology(Grant No.2022L3049).
文摘Fast and accurate measurement of the volume of earthmoving materials is of great signifcance for the real-time evaluation of loader operation efciency and the realization of autonomous operation. Existing methods for volume measurement, such as total station-based methods, cannot measure the volume in real time, while the bucket-based method also has the disadvantage of poor universality. In this study, a fast estimation method for a loader’s shovel load volume by 3D reconstruction of material piles is proposed. First, a dense stereo matching method (QORB–MAPM) was proposed by integrating the improved quadtree ORB algorithm (QORB) and the maximum a posteriori probability model (MAPM), which achieves fast matching of feature points and dense 3D reconstruction of material piles. Second, the 3D point cloud model of the material piles before and after shoveling was registered and segmented to obtain the 3D point cloud model of the shoveling area, and the Alpha-shape algorithm of Delaunay triangulation was used to estimate the volume of the 3D point cloud model. Finally, a shovel loading volume measurement experiment was conducted under loose-soil working conditions. The results show that the shovel loading volume estimation method (QORB–MAPM VE) proposed in this study has higher estimation accuracy and less calculation time in volume estimation and bucket fll factor estimation, and it has signifcant theoretical research and engineering application value.
基金supported by the Joint NSFC–ISF Research Program (Project No. 61561146393)the National Natural Science Foundation of China (Project No. 61521002)+1 种基金the Research Grant of Beijing Higher Institution Engineering Research Center,and the Tsinghua–Tencent Joint Laboratory for Internet Innovation Technologysupported by grants from the Research Grants Council of the Hong Kong Special Administrative Region, China (Project Nos. City U113513 and City U11300615)
文摘Point cloud segmentation is a fundamental problem. Due to the complexity of real-world scenes and the limitations of 3D scanners, interactive segmentation is currently the only way to cope with all kinds of point clouds. However, interactively segmenting complex and large-scale scenes is very time-consuming.In this paper, we present a novel interactive system for segmenting point cloud scenes. Our system automatically suggests a series of camera views, in which users can conveniently specify segmentation guidance. In this way, users may focus on specifying segmentation hints instead of manually searching for desirable views of unsegmented objects, thus significantly reducing user effort. To achieve this, we introduce a novel view preference model, which is based on a set of dedicated view attributes, with weights learned from a user study. We also introduce support relations for both graph-cut-based segmentation and finding similar objects. Our experiments show that our segmentation technique helps users quickly segment various types of scenes, outperforming alternative methods.
