Airborne LiDAR(Light Detection and Ranging)is an evolving high-tech active remote sensing technology that has the capability to acquire large-area topographic data and can quickly generate DEM(Digital Elevation Model)...Airborne LiDAR(Light Detection and Ranging)is an evolving high-tech active remote sensing technology that has the capability to acquire large-area topographic data and can quickly generate DEM(Digital Elevation Model)products.Combined with image data,this technology can further enrich and extract spatial geographic information.However,practically,due to the limited operating range of airborne LiDAR and the large area of task,it would be necessary to perform registration and stitching process on point clouds of adjacent flight strips.By eliminating grow errors,the systematic errors in the data need to be effectively reduced.Thus,this paper conducts research on point cloud registration methods in urban building areas,aiming to improve the accuracy and processing efficiency of airborne LiDAR data.Meanwhile,an improved post-ICP(Iterative Closest Point)point cloud registration method was proposed in this study to determine the accurate registration and efficient stitching of point clouds,which capable to provide a potential technical support for applicants in related field.展开更多
Recent advances in 3D scanning technologies allow us to acquire accurate and dense 3D scan data of large-scale environments efficiently.Currently,there are various methods for acquiring largescale 3D scan data,such as...Recent advances in 3D scanning technologies allow us to acquire accurate and dense 3D scan data of large-scale environments efficiently.Currently,there are various methods for acquiring largescale 3D scan data,such as Mobile Laser Scanning(MLS),Airborne Laser Scanning,Terrestrial Laser Scanning,photogrammetry and Structure from Motion(SfM).Especially,MLS is useful to acquire dense point clouds of road and road-side objects,and SfM is a powerful technique to reconstruct meshes with textures from a set of digital images.In this research,a registration method of point clouds from vehicle-based MLS(MLS point cloud),and textured meshes from the SfM of aerial photographs(SfM mesh),is proposed for creating high-quality surface models of urban areas by combining them.In general,SfM mesh has non-scale information;therefore,scale,position,and orientation of the SfM mesh are adjusted in the registration process.In our method,first,2D feature points are extracted from both SfM mesh and MLS point cloud.This process consists of ground-and building-plane extraction by region growing,random sample consensus and least square method,vertical edge extraction by detecting intersections between the planes,and feature point extraction by intersection tests between the ground plane and the edges.Then,the corresponding feature points between the MLS point cloud and the SfM mesh are searched efficiently,using similarity invariant features and hashing.Next,the coordinate transformation is applied to the SfM mesh so that the ground planes and corresponding feature points are adjusted.Finally,scaling Iterative Closest Point algorithm is applied for accurate registration.Experimental results for three data-sets show that our method is effective for the registration of SfM mesh and MLS point cloud of urban areas including buildings.展开更多
Forest is one of the most challenging environments to be recorded in a three-dimensional(3D)digitized geometrical representation,because of the size and the complexity of the environment and the data-acquisition const...Forest is one of the most challenging environments to be recorded in a three-dimensional(3D)digitized geometrical representation,because of the size and the complexity of the environment and the data-acquisition constraints brought by on-site conditions.Previous studies have indicated that the data-acquisition pattern can have more influence on the registration results than other factors.In practice,the ideal short-baseline observations,i.e.,the dense collection mode,is rarely feasible,considering the low accessibility in forest environments and the commonly limited labor and time resources.The wide-baseline observations that cover a forest site using a few folds less observations than short-baseline observations,are therefore more preferable and commonly applied.Nevertheless,the wide-baseline approach is more challenging for data registration since it typically lacks the required sufficient overlaps between datasets.Until now,a robust automated registration solution that is independent of special hardware requirements has still been missing.That is,the registration accuracy is still far from the required level,and the information extractable from the merged point cloud using automated registration could not match that from the merged point cloud using manual registration.This paper proposes a discrete overlap search(DOS)method to find correspondences in the point clouds to solve the low-overlap problem in the wide-baseline point clouds.The proposed automatic method uses potential correspondences from both original data and selected feature points to reconstruct rough observation geometries without external knowledge and to retrieve precise registration parameters at data-level.An extensive experiment was carried out with 24 forest datasets of different conditions categorized in three difficulty levels.The performance of the proposed method was evaluated using various accuracy criteria,as well as based on data acquired from different hardware,platforms,viewing perspectives,and at different points of time.The proposed method achieved a 3D registration accuracy at a 0.50-cm level in all difficulty categories using static terrestrial acquisitions.In the terrestrial-aerial registration,data sets were collected from different sensors and at different points of time with scene changes,and a registration accuracy at the raw data geometric accuracy level was achieved.These results represent the highest automated registration accuracy and the strictest evaluation so far.The proposed method is applicable in multiple scenarios,such as 1)the global positioning of individual under-canopy observations,which is one of the main challenges in applying terrestrial observations lacking a global context,2)the fusion of point clouds acquired from terrestrial and aerial perspectives,which is required in order to achieve a complete forest observation,3)mobile mapping using a new stop-and-go approach,which solves the problems of lacking mobility and slow data collection in static terrestrial measurements as well as the data-quality issue in the continuous mobile approach.Furthermore,this work proposes a new error estimate that units all parameter-level errors into a single quantity and compensates for the downsides of the widely used parameter-and object-level error estimates;it also proposes a new deterministic point sets registration method as an alternative to the popular sampling methods.展开更多
In image-guided radiation therapy, extracting features from medical point cloud is the key technique for multimodality registration. This novel framework, denoted Control Point Net (CPN), provides an alternative to th...In image-guided radiation therapy, extracting features from medical point cloud is the key technique for multimodality registration. This novel framework, denoted Control Point Net (CPN), provides an alternative to the common applications of manually designed keypoint descriptors for coarse point cloud registration. The CPN directly consumes a point cloud, divides it into equally spaced 3D voxels and transforms the points within each voxel into a unified feature representation through voxel feature encoding (VFE) layer. Then all volumetric representations are aggregated by Weighted Extraction Layer which selectively extracts features and synthesize into global descriptors and coordinates of control points. Utilizing global descriptors instead of local features allows the available geometrical data to be better exploited to improve the robustness and precision. Specifically, CPN unifies feature extraction and clustering into a single network, omitting time-consuming feature matching procedure. The algorithm is tested on point cloud datasets generated from CT images. Experiments and comparisons with the state-of-the-art descriptors demonstrate that CPN is highly discriminative, efficient, and robust to noise and density changes.展开更多
Registrations based on the manual placement of spherical targets are still being employed by many professionals in the industry.However,the placement of those targets usually relies solely on personal experience witho...Registrations based on the manual placement of spherical targets are still being employed by many professionals in the industry.However,the placement of those targets usually relies solely on personal experience without scientific evidence supported by numerical analysis.This paper presents a comprehensive investigation,based on Monte Carlo simulation,into determining the optimal number and positions for efficient target placement in typical scenes consisting of a pair of facades.It demonstrates new check-up statistical rules and geometrical constraints that can effectively extract and analyze massive simulations of unregistered point clouds and their corresponding registrations.More than 6×10^(7) sets of the registrations were simulated,whereas more than IOO registrations with real data were used to verify the results of simulation.The results indicated that using five spherical targets is the best choice for the registration of a large typical registration site consisting of two vertical facades and a ground,when there is only a box set of spherical targets available.As a result,the users can avoid placing extra targets to achieve insignificant improvements in registration accuracy.The results also suggest that the higher registration accuracy can be obtained when the ratio between the facade-to-target distance and target-to-scanner distance is approximately 3:2.Therefore,the targets should be placed closer to the scanner rather than in the middle between the facades and the scanner,contradicting to the traditional thought. Besides,the results reveal that the accuracy can be increased by setting the largest projected triangular area of the targets to be large.展开更多
Point cloud registration aims to find a rigid transformation for aligning one point cloud to another.Such registration is a fundamental problem in computer vision and robotics,and has been widely used in various appli...Point cloud registration aims to find a rigid transformation for aligning one point cloud to another.Such registration is a fundamental problem in computer vision and robotics,and has been widely used in various applications,including 3D reconstruction,simultaneous localization and mapping,and autonomous driving.Over the last decades,numerous researchers have devoted themselves to tackling this challenging problem.The success of deep learning in high-level vision tasks has recently been extended to different geometric vision tasks.Various types of deep learning based point cloud registration methods have been proposed to exploit different aspects of the problem.However,a comprehensive overview of these approaches remains missing.To this end,in this paper,we summarize the recent progress in this area and present a comprehensive overview regarding deep learning based point cloud registration.We classify the popular approaches into different categories such as correspondences-based and correspondences-free approaches,with effective modules,i.e.,feature extractor,matching,outlier rejection,and motion estimation modules.Furthermore,we discuss the merits and demerits of such approaches in detail.Finally,we provide a systematic and compact framework for currently proposed methods and discuss directions of future research.展开更多
Non-rigid registration of point clouds is still far from stable,especially for the largely deformed one.Sparse initial correspondences are often adopted to facilitate the process.However,there are few studies on how t...Non-rigid registration of point clouds is still far from stable,especially for the largely deformed one.Sparse initial correspondences are often adopted to facilitate the process.However,there are few studies on how to build them automatically.Therefore,in this paper,we propose a robust method to compute such priors automatically,where a global and local combined strategy is adopted.These priors in different degrees of deformation are obtained by the locally geometrical-consistent point matches from the globally structural-consistent region correspondences.To further utilize the matches,this paper also proposes a novel registration method based on the Coherent Point Drift framework.This method takes both the spatial proximity and local structural consistency of the priors as supervision of the registration process and thus obtains a robust alignment for clouds with significantly different deformations.Qualitative and quantitative experiments demonstrate the advantages of the proposed method.展开更多
This paper surveys state-of-the-art image features and descriptors for the task of 3D scan registration based on panoramic reflectance images.As modern terrestrial laser scanners digitize their environment in a spheri...This paper surveys state-of-the-art image features and descriptors for the task of 3D scan registration based on panoramic reflectance images.As modern terrestrial laser scanners digitize their environment in a spherical way,the sphere has to be projected to a two-dimensional image.To this end,we evaluate the equirectangular,the cylindrical,the Mercator,the rectilinear,the Pannini,the stereographic,and the z-axis projection.We show that the Mercator and the Pannini projection outperform the other projection methods.展开更多
Compared with the pair-wise registration of point clouds,multi-view point cloud registration is much less studied.In this dissertation,a disordered multi-view point cloud registration method based on the soft trimmed ...Compared with the pair-wise registration of point clouds,multi-view point cloud registration is much less studied.In this dissertation,a disordered multi-view point cloud registration method based on the soft trimmed deep network is proposed.In this method,firstly,the expression ability of feature extraction module is improved and the registration accuracy is increased by enhancing feature extraction network with the point pair feature.Secondly,neighborhood and angle similarities are used to measure the consistency of candidate points to surrounding neighborhoods.By combining distance consistency and high dimensional feature consistency,our network introduces the confidence estimation module of registration,so the point cloud trimmed problem can be converted to candidate for the degree of confidence estimation problem,achieving the pair-wise registration of partially overlapping point clouds.Thirdly,the results from pair-wise registration are fed into the model fusion to achieve the rough registration of multi-view point clouds.Finally,the hierarchical clustering is used to iteratively optimize the clustering center model by gradually increasing the number of clustering categories and performing clustering and registration alternately.This method achieves rough point cloud registration quickly in the early stage,improves the accuracy of multi-view point cloud registration in the later stage,and makes full use of global information to achieve robust and accurate multi-view registration without initial value.展开更多
Multi-instance registration is a challenging problem in computer vision and robotics,where multiple instances of an object need to be registered in a standard coordinate system.Pioneers followed a non-extensible one-s...Multi-instance registration is a challenging problem in computer vision and robotics,where multiple instances of an object need to be registered in a standard coordinate system.Pioneers followed a non-extensible one-shot framework,which prioritizes the registration of simple and isolated instances,often struggling to accurately register challenging or occluded instances.To address these challenges,we propose the first iterative framework for multi-instance 3D registration(MI-3DReg)in this work,termed instance-by-instance(IBI).It successively registers instances while systematically reducing outliers,starting from the easiest and progressing to more challenging ones.This enhances the likelihood of effectively registering instances that may have been initially overlooked,allowing for successful registration in subsequent iterations.Under the IBI framework,we further propose a sparse-to-dense correspondence-based multi-instance registration method(IBI-S2DC)to enhance the robustness of MI-3DReg.Experiments on both synthetic and real datasets have demonstrated the effectiveness of IBI and suggested the new state-of-the-art performance with IBI-S2DC,e.g.,our mean registration F1 score is 12.02%/12.35%higher than the existing state-of-the-art on the synthetic/real datasets.The source codes are available online at https://github.com/caoxy01/IBI.展开更多
Multi-view laser radar （ladar） data registration in obscure environments is an important research field of obscured target detection from air to ground. There are few overlap regions of the observational data in dif...Multi-view laser radar （ladar） data registration in obscure environments is an important research field of obscured target detection from air to ground. There are few overlap regions of the observational data in different views because of the occluder, so the multi-view data registration is rather difficult. Through indepth analyses of the typical methods and problems, it is obtained that the sequence registration is more appropriate, but needs to improve the registration accuracy. On this basis, a multi-view data registration algorithm based on aggregating the adjacent frames, which are already registered, is proposed. It increases the overlap region between the pending registration frames by aggregation and further improves the registration accuracy. The experiment results show that the proposed algorithm can effectively register the multi-view ladar data in the obscure environment, and it also has a greater robustness and a higher registration accuracy compared with the sequence registration under the condition of equivalent operating efficiency.展开更多
With the rapid advancement of mechanical automation and intelligent processing technology,ac-curately measuring the surfaces of complex parts has emerged as a significant research challenge.Robotic measurement technol...With the rapid advancement of mechanical automation and intelligent processing technology,ac-curately measuring the surfaces of complex parts has emerged as a significant research challenge.Robotic measurement technology plays a crucial role in facilitating rapid quality inspections during the manufacturing process due to its inherent flexibility.However,the irregular shapes and viewpoint occlusions of complex parts complicate precise measurement.To address these challenges,this work proposes a point cloud registration network for robotic scanning systems and introduces a DBR-Net(Dual-line Registration Network)to overcome the issues of low overlap rates and perspective occlusion that currently impede the registration of certain workpieces.First,feature extraction is performed using a bilinear encoder and multi-level feature interactions of both point-wise and global features.Subsequently,the features are sampled through unanimous voting and fed into the RANSAC(Random Sample Consensus)algorithm for pose estimation,enabling multi-view point cloud registration.Experimental results demonstrate that this method significantly outperforms many existing techniques in terms of feature extraction and registration accuracy,thereby enhancing the overall performance of point cloud registration.展开更多
Estimating an accurate six-degree-of-freedom(6-Do F)pose from correspondences with outliers remains a critical issue to 3D rigid registration.Random sample consensus(RANSAC)and its variants are popular solutions to th...Estimating an accurate six-degree-of-freedom(6-Do F)pose from correspondences with outliers remains a critical issue to 3D rigid registration.Random sample consensus(RANSAC)and its variants are popular solutions to this problem.Although there have been a number of RANSAC-fashion estimators,two issues remain unsolved.First,it is unclear which estimator is more appropriate to a particular application.Second,the impacts of different sampling strategies,hypothesis generation methods,hypothesis evaluation metrics,and stop criteria on the overall estimators remain ambiguous.This work fills these gaps by first considering six existing RANSAC-fashion methods and then proposing eight variants for a comprehensive evaluation.The objective is to thoroughly compare estimators in the RANSAC family,and evaluate the effects of each key stage on the eventual 6-Do F pose estimation performance.Experiments have been carried out on four standard datasets with different application scenarios,data modalities,and nuisances.They provide us with input correspondence sets with a variety of inlier ratios,spatial distributions,and scales.Based on the experimental results,we summarize remarkable outcomes and valuable findings,so as to give practical instructions to real-world applications,and highlight current bottlenecks and potential solutions in this research realm.展开更多
As point cloud of one whole vehicle body has the traits of large geometric dimension,huge data and rigorous reverse precision,one pretreatment algorithm on automobile body point cloud is put forward.The basic idea of ...As point cloud of one whole vehicle body has the traits of large geometric dimension,huge data and rigorous reverse precision,one pretreatment algorithm on automobile body point cloud is put forward.The basic idea of the registration algorithm based on the skeleton points is to construct the skeleton points of the whole vehicle model and the mark points of the separate point cloud,to search the mapped relationship between skeleton points and mark points using congruence triangle method and to match the whole vehicle point cloud using the improved iterative closed point(ICP)algorithm.The data reduction algorithm,based on average square root of distance,condenses data by three steps,computing datasets'average square root of distance in sampling cube grid,sorting order according to the value computed from the first step,choosing sampling percentage.The accuracy of the two algorithms above is proved by a registration and reduction example of whole vehicle point cloud of a certain light truck.展开更多
Based on Bayesian theory and RANSAC,this paper applies Bayesian Sampling Consensus(BaySAC)method using convergence evaluation of hypothesis models in indoor point cloud processing.We implement a conditional sampling m...Based on Bayesian theory and RANSAC,this paper applies Bayesian Sampling Consensus(BaySAC)method using convergence evaluation of hypothesis models in indoor point cloud processing.We implement a conditional sampling method,BaySAC,to always select the minimum number of required data with the highest inlier probabilities.Because the primitive parameters calculated by the different inlier sets should be convergent,this paper presents a statistical testing algorithm for a candidate model parameter histogram to compute the prior probability of each data point.Moreover,the probability update is implemented using the simplified Bayes’formula.The performances of the BaySAC algorithm with the proposed strategies of the prior probability determination and the RANSAC framework are compared using real data-sets.The experimental results indicate that the more outliers contain the data points,the higher computational efficiency of our proposed algorithm gains compared with RANSAC.The results also indicate that the proposed statistical testing strategy can determine sound prior inlier probability free of the change of hypothesis models.展开更多
Acquisition and registration of terrestrial 3D laser scans is a fundamental task in mapping and modeling of cities in three dimensions. To automate this task marker-flee registration methods are required. Based on the...Acquisition and registration of terrestrial 3D laser scans is a fundamental task in mapping and modeling of cities in three dimensions. To automate this task marker-flee registration methods are required. Based on the existence of skyline features, this paper proposes a novel method. The skyline features are extracted from panoramic 3D scans and encoded as strings enabling the use of string matching for merging the scans. Initial results of the proposed method in the old city center of Bremen are presented.展开更多
基金Guangxi Key Laboratory of Spatial Information and Geomatics(21-238-21-12)Guangxi Young and Middle-aged Teachers’Research Fundamental Ability Enhancement Project(2023KY1196).
文摘Airborne LiDAR(Light Detection and Ranging)is an evolving high-tech active remote sensing technology that has the capability to acquire large-area topographic data and can quickly generate DEM(Digital Elevation Model)products.Combined with image data,this technology can further enrich and extract spatial geographic information.However,practically,due to the limited operating range of airborne LiDAR and the large area of task,it would be necessary to perform registration and stitching process on point clouds of adjacent flight strips.By eliminating grow errors,the systematic errors in the data need to be effectively reduced.Thus,this paper conducts research on point cloud registration methods in urban building areas,aiming to improve the accuracy and processing efficiency of airborne LiDAR data.Meanwhile,an improved post-ICP(Iterative Closest Point)point cloud registration method was proposed in this study to determine the accurate registration and efficient stitching of point clouds,which capable to provide a potential technical support for applicants in related field.
基金This work was partially supported by JSPS KAKENHI[grant number 26420073].
文摘Recent advances in 3D scanning technologies allow us to acquire accurate and dense 3D scan data of large-scale environments efficiently.Currently,there are various methods for acquiring largescale 3D scan data,such as Mobile Laser Scanning(MLS),Airborne Laser Scanning,Terrestrial Laser Scanning,photogrammetry and Structure from Motion(SfM).Especially,MLS is useful to acquire dense point clouds of road and road-side objects,and SfM is a powerful technique to reconstruct meshes with textures from a set of digital images.In this research,a registration method of point clouds from vehicle-based MLS(MLS point cloud),and textured meshes from the SfM of aerial photographs(SfM mesh),is proposed for creating high-quality surface models of urban areas by combining them.In general,SfM mesh has non-scale information;therefore,scale,position,and orientation of the SfM mesh are adjusted in the registration process.In our method,first,2D feature points are extracted from both SfM mesh and MLS point cloud.This process consists of ground-and building-plane extraction by region growing,random sample consensus and least square method,vertical edge extraction by detecting intersections between the planes,and feature point extraction by intersection tests between the ground plane and the edges.Then,the corresponding feature points between the MLS point cloud and the SfM mesh are searched efficiently,using similarity invariant features and hashing.Next,the coordinate transformation is applied to the SfM mesh so that the ground planes and corresponding feature points are adjusted.Finally,scaling Iterative Closest Point algorithm is applied for accurate registration.Experimental results for three data-sets show that our method is effective for the registration of SfM mesh and MLS point cloud of urban areas including buildings.
基金financial support from the National Natural Science Foundation of China(Grant Nos.32171789,32211530031)Wuhan University(No.WHUZZJJ202220)Academy of Finland(Nos.334060,334829,331708,344755,337656,334830,293389/314312,334830,319011)。
文摘Forest is one of the most challenging environments to be recorded in a three-dimensional(3D)digitized geometrical representation,because of the size and the complexity of the environment and the data-acquisition constraints brought by on-site conditions.Previous studies have indicated that the data-acquisition pattern can have more influence on the registration results than other factors.In practice,the ideal short-baseline observations,i.e.,the dense collection mode,is rarely feasible,considering the low accessibility in forest environments and the commonly limited labor and time resources.The wide-baseline observations that cover a forest site using a few folds less observations than short-baseline observations,are therefore more preferable and commonly applied.Nevertheless,the wide-baseline approach is more challenging for data registration since it typically lacks the required sufficient overlaps between datasets.Until now,a robust automated registration solution that is independent of special hardware requirements has still been missing.That is,the registration accuracy is still far from the required level,and the information extractable from the merged point cloud using automated registration could not match that from the merged point cloud using manual registration.This paper proposes a discrete overlap search(DOS)method to find correspondences in the point clouds to solve the low-overlap problem in the wide-baseline point clouds.The proposed automatic method uses potential correspondences from both original data and selected feature points to reconstruct rough observation geometries without external knowledge and to retrieve precise registration parameters at data-level.An extensive experiment was carried out with 24 forest datasets of different conditions categorized in three difficulty levels.The performance of the proposed method was evaluated using various accuracy criteria,as well as based on data acquired from different hardware,platforms,viewing perspectives,and at different points of time.The proposed method achieved a 3D registration accuracy at a 0.50-cm level in all difficulty categories using static terrestrial acquisitions.In the terrestrial-aerial registration,data sets were collected from different sensors and at different points of time with scene changes,and a registration accuracy at the raw data geometric accuracy level was achieved.These results represent the highest automated registration accuracy and the strictest evaluation so far.The proposed method is applicable in multiple scenarios,such as 1)the global positioning of individual under-canopy observations,which is one of the main challenges in applying terrestrial observations lacking a global context,2)the fusion of point clouds acquired from terrestrial and aerial perspectives,which is required in order to achieve a complete forest observation,3)mobile mapping using a new stop-and-go approach,which solves the problems of lacking mobility and slow data collection in static terrestrial measurements as well as the data-quality issue in the continuous mobile approach.Furthermore,this work proposes a new error estimate that units all parameter-level errors into a single quantity and compensates for the downsides of the widely used parameter-and object-level error estimates;it also proposes a new deterministic point sets registration method as an alternative to the popular sampling methods.
文摘In image-guided radiation therapy, extracting features from medical point cloud is the key technique for multimodality registration. This novel framework, denoted Control Point Net (CPN), provides an alternative to the common applications of manually designed keypoint descriptors for coarse point cloud registration. The CPN directly consumes a point cloud, divides it into equally spaced 3D voxels and transforms the points within each voxel into a unified feature representation through voxel feature encoding (VFE) layer. Then all volumetric representations are aggregated by Weighted Extraction Layer which selectively extracts features and synthesize into global descriptors and coordinates of control points. Utilizing global descriptors instead of local features allows the available geometrical data to be better exploited to improve the robustness and precision. Specifically, CPN unifies feature extraction and clustering into a single network, omitting time-consuming feature matching procedure. The algorithm is tested on point cloud datasets generated from CT images. Experiments and comparisons with the state-of-the-art descriptors demonstrate that CPN is highly discriminative, efficient, and robust to noise and density changes.
基金Key Research and Development Program of Guangdong Province (No.2020B0101130009)
文摘Registrations based on the manual placement of spherical targets are still being employed by many professionals in the industry.However,the placement of those targets usually relies solely on personal experience without scientific evidence supported by numerical analysis.This paper presents a comprehensive investigation,based on Monte Carlo simulation,into determining the optimal number and positions for efficient target placement in typical scenes consisting of a pair of facades.It demonstrates new check-up statistical rules and geometrical constraints that can effectively extract and analyze massive simulations of unregistered point clouds and their corresponding registrations.More than 6×10^(7) sets of the registrations were simulated,whereas more than IOO registrations with real data were used to verify the results of simulation.The results indicated that using five spherical targets is the best choice for the registration of a large typical registration site consisting of two vertical facades and a ground,when there is only a box set of spherical targets available.As a result,the users can avoid placing extra targets to achieve insignificant improvements in registration accuracy.The results also suggest that the higher registration accuracy can be obtained when the ratio between the facade-to-target distance and target-to-scanner distance is approximately 3:2.Therefore,the targets should be placed closer to the scanner rather than in the middle between the facades and the scanner,contradicting to the traditional thought. Besides,the results reveal that the accuracy can be increased by setting the largest projected triangular area of the targets to be large.
基金Supported by the National Key Research and Development Program of China under Grant(2018AAA0102803)the National Natural Science Foundation of China under Grants(61871325,61420106007,61671387).
文摘Point cloud registration aims to find a rigid transformation for aligning one point cloud to another.Such registration is a fundamental problem in computer vision and robotics,and has been widely used in various applications,including 3D reconstruction,simultaneous localization and mapping,and autonomous driving.Over the last decades,numerous researchers have devoted themselves to tackling this challenging problem.The success of deep learning in high-level vision tasks has recently been extended to different geometric vision tasks.Various types of deep learning based point cloud registration methods have been proposed to exploit different aspects of the problem.However,a comprehensive overview of these approaches remains missing.To this end,in this paper,we summarize the recent progress in this area and present a comprehensive overview regarding deep learning based point cloud registration.We classify the popular approaches into different categories such as correspondences-based and correspondences-free approaches,with effective modules,i.e.,feature extractor,matching,outlier rejection,and motion estimation modules.Furthermore,we discuss the merits and demerits of such approaches in detail.Finally,we provide a systematic and compact framework for currently proposed methods and discuss directions of future research.
基金supported by Natural Science Foundation of Anhui Province (2108085MF210,1908085MF187)Key Natural Science Fund of Department of Eduction of Anhui Province (KJ2021A0042)Natural Social Science Foundation of China (19BTY091).
文摘Non-rigid registration of point clouds is still far from stable,especially for the largely deformed one.Sparse initial correspondences are often adopted to facilitate the process.However,there are few studies on how to build them automatically.Therefore,in this paper,we propose a robust method to compute such priors automatically,where a global and local combined strategy is adopted.These priors in different degrees of deformation are obtained by the locally geometrical-consistent point matches from the globally structural-consistent region correspondences.To further utilize the matches,this paper also proposes a novel registration method based on the Coherent Point Drift framework.This method takes both the spatial proximity and local structural consistency of the priors as supervision of the registration process and thus obtains a robust alignment for clouds with significantly different deformations.Qualitative and quantitative experiments demonstrate the advantages of the proposed method.
文摘This paper surveys state-of-the-art image features and descriptors for the task of 3D scan registration based on panoramic reflectance images.As modern terrestrial laser scanners digitize their environment in a spherical way,the sphere has to be projected to a two-dimensional image.To this end,we evaluate the equirectangular,the cylindrical,the Mercator,the rectilinear,the Pannini,the stereographic,and the z-axis projection.We show that the Mercator and the Pannini projection outperform the other projection methods.
文摘Compared with the pair-wise registration of point clouds,multi-view point cloud registration is much less studied.In this dissertation,a disordered multi-view point cloud registration method based on the soft trimmed deep network is proposed.In this method,firstly,the expression ability of feature extraction module is improved and the registration accuracy is increased by enhancing feature extraction network with the point pair feature.Secondly,neighborhood and angle similarities are used to measure the consistency of candidate points to surrounding neighborhoods.By combining distance consistency and high dimensional feature consistency,our network introduces the confidence estimation module of registration,so the point cloud trimmed problem can be converted to candidate for the degree of confidence estimation problem,achieving the pair-wise registration of partially overlapping point clouds.Thirdly,the results from pair-wise registration are fed into the model fusion to achieve the rough registration of multi-view point clouds.Finally,the hierarchical clustering is used to iteratively optimize the clustering center model by gradually increasing the number of clustering categories and performing clustering and registration alternately.This method achieves rough point cloud registration quickly in the early stage,improves the accuracy of multi-view point cloud registration in the later stage,and makes full use of global information to achieve robust and accurate multi-view registration without initial value.
基金supported in part by the National Natural Science Foundation of China(62372377).
文摘Multi-instance registration is a challenging problem in computer vision and robotics,where multiple instances of an object need to be registered in a standard coordinate system.Pioneers followed a non-extensible one-shot framework,which prioritizes the registration of simple and isolated instances,often struggling to accurately register challenging or occluded instances.To address these challenges,we propose the first iterative framework for multi-instance 3D registration(MI-3DReg)in this work,termed instance-by-instance(IBI).It successively registers instances while systematically reducing outliers,starting from the easiest and progressing to more challenging ones.This enhances the likelihood of effectively registering instances that may have been initially overlooked,allowing for successful registration in subsequent iterations.Under the IBI framework,we further propose a sparse-to-dense correspondence-based multi-instance registration method(IBI-S2DC)to enhance the robustness of MI-3DReg.Experiments on both synthetic and real datasets have demonstrated the effectiveness of IBI and suggested the new state-of-the-art performance with IBI-S2DC,e.g.,our mean registration F1 score is 12.02%/12.35%higher than the existing state-of-the-art on the synthetic/real datasets.The source codes are available online at https://github.com/caoxy01/IBI.
文摘Multi-view laser radar （ladar） data registration in obscure environments is an important research field of obscured target detection from air to ground. There are few overlap regions of the observational data in different views because of the occluder, so the multi-view data registration is rather difficult. Through indepth analyses of the typical methods and problems, it is obtained that the sequence registration is more appropriate, but needs to improve the registration accuracy. On this basis, a multi-view data registration algorithm based on aggregating the adjacent frames, which are already registered, is proposed. It increases the overlap region between the pending registration frames by aggregation and further improves the registration accuracy. The experiment results show that the proposed algorithm can effectively register the multi-view ladar data in the obscure environment, and it also has a greater robustness and a higher registration accuracy compared with the sequence registration under the condition of equivalent operating efficiency.
基金co-supported by the National Natural Science Foundation of China(U22A20176)Guangdong Basic and Applied Basic Research Foundation,China(2022B1515120078)+2 种基金the Guangdong Basic and Applied Basic Research Foundation,China(2021A1515110898)GDAS’Project of Science and Technology Development,China(2022GDASZH-2022010108)the Key Areas R&D Program of Dongguan City,China(20201200300062).
文摘With the rapid advancement of mechanical automation and intelligent processing technology,ac-curately measuring the surfaces of complex parts has emerged as a significant research challenge.Robotic measurement technology plays a crucial role in facilitating rapid quality inspections during the manufacturing process due to its inherent flexibility.However,the irregular shapes and viewpoint occlusions of complex parts complicate precise measurement.To address these challenges,this work proposes a point cloud registration network for robotic scanning systems and introduces a DBR-Net(Dual-line Registration Network)to overcome the issues of low overlap rates and perspective occlusion that currently impede the registration of certain workpieces.First,feature extraction is performed using a bilinear encoder and multi-level feature interactions of both point-wise and global features.Subsequently,the features are sampled through unanimous voting and fed into the RANSAC(Random Sample Consensus)algorithm for pose estimation,enabling multi-view point cloud registration.Experimental results demonstrate that this method significantly outperforms many existing techniques in terms of feature extraction and registration accuracy,thereby enhancing the overall performance of point cloud registration.
基金supported in part by the National Natural Science Foundation of China(NFSC)(62002295,U19B2037)China Postdoctoral Science Foundation(2020M673319)+1 种基金Shaanxi Provincial Key R&D Program(2021KWZ-03)the Natural Science Basic Research Plan in Shaanxi Province of China(2021JQ-290,2020JQ-210)。
文摘Estimating an accurate six-degree-of-freedom(6-Do F)pose from correspondences with outliers remains a critical issue to 3D rigid registration.Random sample consensus(RANSAC)and its variants are popular solutions to this problem.Although there have been a number of RANSAC-fashion estimators,two issues remain unsolved.First,it is unclear which estimator is more appropriate to a particular application.Second,the impacts of different sampling strategies,hypothesis generation methods,hypothesis evaluation metrics,and stop criteria on the overall estimators remain ambiguous.This work fills these gaps by first considering six existing RANSAC-fashion methods and then proposing eight variants for a comprehensive evaluation.The objective is to thoroughly compare estimators in the RANSAC family,and evaluate the effects of each key stage on the eventual 6-Do F pose estimation performance.Experiments have been carried out on four standard datasets with different application scenarios,data modalities,and nuisances.They provide us with input correspondence sets with a variety of inlier ratios,spatial distributions,and scales.Based on the experimental results,we summarize remarkable outcomes and valuable findings,so as to give practical instructions to real-world applications,and highlight current bottlenecks and potential solutions in this research realm.
基金This project is supported by Provincial Technology Cooperation Program of Yunnan,China(No.2003EAAAA00D043).
文摘As point cloud of one whole vehicle body has the traits of large geometric dimension,huge data and rigorous reverse precision,one pretreatment algorithm on automobile body point cloud is put forward.The basic idea of the registration algorithm based on the skeleton points is to construct the skeleton points of the whole vehicle model and the mark points of the separate point cloud,to search the mapped relationship between skeleton points and mark points using congruence triangle method and to match the whole vehicle point cloud using the improved iterative closed point(ICP)algorithm.The data reduction algorithm,based on average square root of distance,condenses data by three steps,computing datasets'average square root of distance in sampling cube grid,sorting order according to the value computed from the first step,choosing sampling percentage.The accuracy of the two algorithms above is proved by a registration and reduction example of whole vehicle point cloud of a certain light truck.
基金This research was supported by the National Natural Science Foundation of China[grant number 41471360]the Fundamental Research Funds for the Central Universities[grant number 2652015176].
文摘Based on Bayesian theory and RANSAC,this paper applies Bayesian Sampling Consensus(BaySAC)method using convergence evaluation of hypothesis models in indoor point cloud processing.We implement a conditional sampling method,BaySAC,to always select the minimum number of required data with the highest inlier probabilities.Because the primitive parameters calculated by the different inlier sets should be convergent,this paper presents a statistical testing algorithm for a candidate model parameter histogram to compute the prior probability of each data point.Moreover,the probability update is implemented using the simplified Bayes’formula.The performances of the BaySAC algorithm with the proposed strategies of the prior probability determination and the RANSAC framework are compared using real data-sets.The experimental results indicate that the more outliers contain the data points,the higher computational efficiency of our proposed algorithm gains compared with RANSAC.The results also indicate that the proposed statistical testing strategy can determine sound prior inlier probability free of the change of hypothesis models.
文摘Acquisition and registration of terrestrial 3D laser scans is a fundamental task in mapping and modeling of cities in three dimensions. To automate this task marker-flee registration methods are required. Based on the existence of skyline features, this paper proposes a novel method. The skyline features are extracted from panoramic 3D scans and encoded as strings enabling the use of string matching for merging the scans. Initial results of the proposed method in the old city center of Bremen are presented.
