Cultural relics line graphic serves as a crucial form of traditional artifact information documentation,which is a simple and intuitive product with low cost of displaying compared with 3D models.Dimensionality reduct...Cultural relics line graphic serves as a crucial form of traditional artifact information documentation,which is a simple and intuitive product with low cost of displaying compared with 3D models.Dimensionality reduction is undoubtedly necessary for line drawings.However,most existing methods for artifact drawing rely on the principles of orthographic projection that always cannot avoid angle occlusion and data overlapping while the surface of cultural relics is complex.Therefore,conformal mapping was introduced as a dimensionality reduction way to compensate for the limitation of orthographic projection.Based on the given criteria for assessing surface complexity,this paper proposed a three-dimensional feature guideline extraction method for complex cultural relic surfaces.A 2D and 3D combined factor that measured the importance of points on describing surface features,vertex weight,was designed.Then the selection threshold for feature guideline extraction was determined based on the differences between vertex weight and shape index distributions.The feasibility and stability were verified through experiments conducted on real cultural relic surface data.Results demonstrated the ability of the method to address the challenges associated with the automatic generation of line drawings for complex surfaces.The extraction method and the obtained results will be useful for line graphic drawing,displaying and propaganda of cultural relics.展开更多
Hole repair processing is an important part of point cloud data processing in airborne 3-dimensional(3D)laser scanning technology.Due to the fragmentation and irregularity of the surface morphology,when applying the 3...Hole repair processing is an important part of point cloud data processing in airborne 3-dimensional(3D)laser scanning technology.Due to the fragmentation and irregularity of the surface morphology,when applying the 3D laser scanning technology to mountain mapping,the conventional mathematical cloud-based point cloud hole repair method is not ideal in practical applications.In order to solve this problem,we propose to repair the valley and ridge line first,and then repair the point cloud hole.The main technical steps of the method include the following points:First,the valley and ridge feature lines are extracted by the GIS slope analysis method;Then,the valley and ridge line missing from the hole are repaired by the mathematical interpolation method,and the repaired results are edited and inserted to the original point cloud;Finally,the traditional repair method is used to repair the point cloud hole whose valley line and ridge line have been repaired.Three experiments were designed and implemented in the east bank of the Xiaobaini River to test the performance of the proposed method.The results showed that compared with the direct point cloud hole repair method in Geomagic Studio software,the average repair accuracy of the proposed method,in the 16 m buffer zone of valley line and ridge line,is increased from 56.31 cm to 31.49 cm.The repair performance is significantly improved.展开更多
Point cloud registration is an essential step in the process of 3D reconstruction.In this paper,a fast registration algorithm of rock mass point cloud is proposed based on the improved iterative closest point (ICP)alg...Point cloud registration is an essential step in the process of 3D reconstruction.In this paper,a fast registration algorithm of rock mass point cloud is proposed based on the improved iterative closest point (ICP)algorithm.In our proposed algorithm,the point cloud data of single station scanner is transformed into digital images by spherical polar coordinates,then image features are extracted and edge points are removed,the features used in this algorithm is scale-invariant feature transform (SIFT).By analyzing the corresponding relationship between digital images and 3D points,the 3D feature points are extracted,from which we can search for the two-way correspondence as candidates. After the false matches are eliminated by the exhaustive search method based on random sampling,the transformation is computed via the Levenberg-Marquardt-Iterative Closest Point (LM-ICP)algorithm.Experiments on real data of rock mass show that the proposed algorithm has the similar accuracy and better registration efficiency compared with the ICP algorithm and other algorithms.展开更多
Increasing development of accurate and efficient road three-dimensional(3D)modeling presents great opportunities to improve the data exchange and integration of building information modeling(BIM)models.3D modeling of ...Increasing development of accurate and efficient road three-dimensional(3D)modeling presents great opportunities to improve the data exchange and integration of building information modeling(BIM)models.3D modeling of road scenes is crucial for reference in asset management,construction,and maintenance.Light detection and ranging(Li DAR)technology is increasingly employed to generate high-quality point clouds for road inventory.In this paper,we specifically investigate the use of Li DAR data for road 3D modeling.The purpose of this review is to provide references about the existing work on the road 3D modeling based on Li DAR point clouds,critically discuss them,and provide challenges for further study.Besides,we introduce modeling standards for roads and discuss the components,types,and distinctions of various Li DAR measurement systems.Then,we review state-of-the-art methods and provide a detailed examination of road segmentation and feature extraction.Furthermore,we systematically introduce point cloud-based 3D modeling methods,namely,parametric modeling and surface reconstruction.Parameters and rules are used to define model components based on geometric and non-geometric information,whereas surface modeling is conducted through individual faces within its geometry.Finally,we discuss and summarize future research directions in this field.This review can assist researchers in enhancing existing approaches and developing new techniques for road modeling based on Li DAR point clouds.展开更多
Point cloud based place recognition plays an important role in mobile robotics. In this paper, we propose a weighted aggregation method from structure information adaptively for point cloud place recognition. Firstly,...Point cloud based place recognition plays an important role in mobile robotics. In this paper, we propose a weighted aggregation method from structure information adaptively for point cloud place recognition. Firstly, to preserve the prior distributions and local geometric structures, we fuse learned hidden features with handcrafted features in the beginning. Secondly, we further extract and aggregate adaptively weighted features concerning density and relative spatial information from these fused features, named Weighted Aggregation with Density Estimation (WADE) module. Then, we conduct the WADE block iteratively to group the latent manifold structures. Finally, comparison results on two public datasets Oxford Robotcar and KITTI show that the proposed approach exceeds the comparison approaches on recall rate averagely 7% - 8%.展开更多
迹线是节理与岩体临空面相交形成的空间曲线,其几何形态直接反映岩体的结构特征。因此,快速准确地提取迹线信息具有重要的理论意义和工程价值。目前,基于空间点云数据的迹线提取多基于曲率开展,较少考虑点云的色彩信息,加之空间点云数...迹线是节理与岩体临空面相交形成的空间曲线,其几何形态直接反映岩体的结构特征。因此,快速准确地提取迹线信息具有重要的理论意义和工程价值。目前,基于空间点云数据的迹线提取多基于曲率开展,较少考虑点云的色彩信息,加之空间点云数量庞大,精度与效率均不理想。鉴于此,提出了一种新的基于降维投影的迹线提取方法(a new trace line extraction method based on dimensionality reduction projection,简称NTDR)。该方法将三维点云保形投影到二维平面,基于二维点云颜色信息进行高效边缘检测,结合三维点云的曲率及距离等几何特征进行聚类连线,实现了节理迹线的自动提取。研究表明:(1)相比于人工提取方法,NTDR处理大规模点云数据的时间节省了约91.07%,大幅提升了提取效率;(2)NTDR提取的迹线与人工提取相比,重合率达90.42%,且局部细节更多,在效果、精度上具有优越性;(3)NTDR在20%的噪点干扰下能保持80%以上的识别正确率,受噪点影响小;(4)相比于同类自动化迹线提取方法,NTDR在提取效果上有优势,更符合岩体实际的迹线分布情况。该方法可提升地质灾害预测效率,为隧道支护设计及工程安全评估提供数据支撑。展开更多
针对点云配准过程中,下采样时容易丢失关键点、影响配准精度的问题,本文提出一种基于特征融合和网络采样的配准方法,提高了配准的精度和速度。在PointNet分类网络基础上,引入小型注意力机制,设计一种基于深度学习网络的关键点提取方法,...针对点云配准过程中,下采样时容易丢失关键点、影响配准精度的问题,本文提出一种基于特征融合和网络采样的配准方法,提高了配准的精度和速度。在PointNet分类网络基础上,引入小型注意力机制,设计一种基于深度学习网络的关键点提取方法,将局部特征和全局特征融合,得到混合特征的特征矩阵。通过深度学习实现对应矩阵求解中相关参数的自动优化,最后利用加权奇异值分解(singular value decomposition,SVD)得到变换矩阵,完成配准。在ModelNet40数据集上的实验表明,和最远点采样相比,所提算法耗时减少45.36%;而配准结果和基于特征学习的鲁棒点匹配(robust point matching using learned features,RPM-Net)相比,平移矩阵均方误差降低5.67%,旋转矩阵均方误差降低13.1%。在自制点云数据上的实验,证实了算法在真实物体上配准的有效性。展开更多
基金National Natural Science Foundation of China(Nos.42071444,42101444)。
文摘Cultural relics line graphic serves as a crucial form of traditional artifact information documentation,which is a simple and intuitive product with low cost of displaying compared with 3D models.Dimensionality reduction is undoubtedly necessary for line drawings.However,most existing methods for artifact drawing rely on the principles of orthographic projection that always cannot avoid angle occlusion and data overlapping while the surface of cultural relics is complex.Therefore,conformal mapping was introduced as a dimensionality reduction way to compensate for the limitation of orthographic projection.Based on the given criteria for assessing surface complexity,this paper proposed a three-dimensional feature guideline extraction method for complex cultural relic surfaces.A 2D and 3D combined factor that measured the importance of points on describing surface features,vertex weight,was designed.Then the selection threshold for feature guideline extraction was determined based on the differences between vertex weight and shape index distributions.The feasibility and stability were verified through experiments conducted on real cultural relic surface data.Results demonstrated the ability of the method to address the challenges associated with the automatic generation of line drawings for complex surfaces.The extraction method and the obtained results will be useful for line graphic drawing,displaying and propaganda of cultural relics.
基金National Natural Science Foundation of China(Nos.41861054,41371423,61966010)National Key R&D Program of China(No.2016YFB0502105)。
文摘Hole repair processing is an important part of point cloud data processing in airborne 3-dimensional(3D)laser scanning technology.Due to the fragmentation and irregularity of the surface morphology,when applying the 3D laser scanning technology to mountain mapping,the conventional mathematical cloud-based point cloud hole repair method is not ideal in practical applications.In order to solve this problem,we propose to repair the valley and ridge line first,and then repair the point cloud hole.The main technical steps of the method include the following points:First,the valley and ridge feature lines are extracted by the GIS slope analysis method;Then,the valley and ridge line missing from the hole are repaired by the mathematical interpolation method,and the repaired results are edited and inserted to the original point cloud;Finally,the traditional repair method is used to repair the point cloud hole whose valley line and ridge line have been repaired.Three experiments were designed and implemented in the east bank of the Xiaobaini River to test the performance of the proposed method.The results showed that compared with the direct point cloud hole repair method in Geomagic Studio software,the average repair accuracy of the proposed method,in the 16 m buffer zone of valley line and ridge line,is increased from 56.31 cm to 31.49 cm.The repair performance is significantly improved.
基金the National Natural Science Foundation of China (Grant No.61471338)Youth Innovation Promotion Association CAS (2015361)+2 种基金Key Research Program of Frontier Sciences,CAS (QYZDY-SSW-SYS004)Beijing Nova Program (z171100001117048)President Fund of UCAS.
文摘Point cloud registration is an essential step in the process of 3D reconstruction.In this paper,a fast registration algorithm of rock mass point cloud is proposed based on the improved iterative closest point (ICP)algorithm.In our proposed algorithm,the point cloud data of single station scanner is transformed into digital images by spherical polar coordinates,then image features are extracted and edge points are removed,the features used in this algorithm is scale-invariant feature transform (SIFT).By analyzing the corresponding relationship between digital images and 3D points,the 3D feature points are extracted,from which we can search for the two-way correspondence as candidates. After the false matches are eliminated by the exhaustive search method based on random sampling,the transformation is computed via the Levenberg-Marquardt-Iterative Closest Point (LM-ICP)algorithm.Experiments on real data of rock mass show that the proposed algorithm has the similar accuracy and better registration efficiency compared with the ICP algorithm and other algorithms.
基金supported by the projects found by the Jiangsu Transportation Science and Technology Project under Grants 2020Y191(1)Postgraduate Research&Practice Innovation Program of Jiangsu Province under Grants KYCX23_0294。
文摘Increasing development of accurate and efficient road three-dimensional(3D)modeling presents great opportunities to improve the data exchange and integration of building information modeling(BIM)models.3D modeling of road scenes is crucial for reference in asset management,construction,and maintenance.Light detection and ranging(Li DAR)technology is increasingly employed to generate high-quality point clouds for road inventory.In this paper,we specifically investigate the use of Li DAR data for road 3D modeling.The purpose of this review is to provide references about the existing work on the road 3D modeling based on Li DAR point clouds,critically discuss them,and provide challenges for further study.Besides,we introduce modeling standards for roads and discuss the components,types,and distinctions of various Li DAR measurement systems.Then,we review state-of-the-art methods and provide a detailed examination of road segmentation and feature extraction.Furthermore,we systematically introduce point cloud-based 3D modeling methods,namely,parametric modeling and surface reconstruction.Parameters and rules are used to define model components based on geometric and non-geometric information,whereas surface modeling is conducted through individual faces within its geometry.Finally,we discuss and summarize future research directions in this field.This review can assist researchers in enhancing existing approaches and developing new techniques for road modeling based on Li DAR point clouds.
文摘Point cloud based place recognition plays an important role in mobile robotics. In this paper, we propose a weighted aggregation method from structure information adaptively for point cloud place recognition. Firstly, to preserve the prior distributions and local geometric structures, we fuse learned hidden features with handcrafted features in the beginning. Secondly, we further extract and aggregate adaptively weighted features concerning density and relative spatial information from these fused features, named Weighted Aggregation with Density Estimation (WADE) module. Then, we conduct the WADE block iteratively to group the latent manifold structures. Finally, comparison results on two public datasets Oxford Robotcar and KITTI show that the proposed approach exceeds the comparison approaches on recall rate averagely 7% - 8%.
文摘迹线是节理与岩体临空面相交形成的空间曲线,其几何形态直接反映岩体的结构特征。因此,快速准确地提取迹线信息具有重要的理论意义和工程价值。目前,基于空间点云数据的迹线提取多基于曲率开展,较少考虑点云的色彩信息,加之空间点云数量庞大,精度与效率均不理想。鉴于此,提出了一种新的基于降维投影的迹线提取方法(a new trace line extraction method based on dimensionality reduction projection,简称NTDR)。该方法将三维点云保形投影到二维平面,基于二维点云颜色信息进行高效边缘检测,结合三维点云的曲率及距离等几何特征进行聚类连线,实现了节理迹线的自动提取。研究表明:(1)相比于人工提取方法,NTDR处理大规模点云数据的时间节省了约91.07%,大幅提升了提取效率;(2)NTDR提取的迹线与人工提取相比,重合率达90.42%,且局部细节更多,在效果、精度上具有优越性;(3)NTDR在20%的噪点干扰下能保持80%以上的识别正确率,受噪点影响小;(4)相比于同类自动化迹线提取方法,NTDR在提取效果上有优势,更符合岩体实际的迹线分布情况。该方法可提升地质灾害预测效率,为隧道支护设计及工程安全评估提供数据支撑。
文摘针对点云配准过程中,下采样时容易丢失关键点、影响配准精度的问题,本文提出一种基于特征融合和网络采样的配准方法,提高了配准的精度和速度。在PointNet分类网络基础上,引入小型注意力机制,设计一种基于深度学习网络的关键点提取方法,将局部特征和全局特征融合,得到混合特征的特征矩阵。通过深度学习实现对应矩阵求解中相关参数的自动优化,最后利用加权奇异值分解(singular value decomposition,SVD)得到变换矩阵,完成配准。在ModelNet40数据集上的实验表明,和最远点采样相比,所提算法耗时减少45.36%;而配准结果和基于特征学习的鲁棒点匹配(robust point matching using learned features,RPM-Net)相比,平移矩阵均方误差降低5.67%,旋转矩阵均方误差降低13.1%。在自制点云数据上的实验,证实了算法在真实物体上配准的有效性。
