An effective approach is proposed for 3D urban scene reconstruction in the form of point cloud with semantic labeling. Starting from high resolution oblique aerial images,our approach proceeds through three main stage...An effective approach is proposed for 3D urban scene reconstruction in the form of point cloud with semantic labeling. Starting from high resolution oblique aerial images,our approach proceeds through three main stages: geographic reconstruction, geometrical reconstruction and semantic reconstruction. The absolute position and orientation of all the cameras relative to the real world are recovered in the geographic reconstruction stage. Then, in the geometrical reconstruction stage,an improved multi-view stereo matching method is employed to produce 3D dense points with color and normal information by taking into account the prior knowledge of aerial imagery.Finally the point cloud is classified into three classes(building,vegetation, and ground) by a rule-based hierarchical approach in the semantic reconstruction step. Experiments on complex urban scene show that our proposed 3-stage approach could generate reasonable reconstruction result robustly and efficiently.By comparing our final semantic reconstruction result with the manually labeled ground truth, classification accuracies from86.75% to 93.02% are obtained.展开更多
With the rapid development of reality capture methods,such as laser scanning and oblique photogrammetry,point cloud data have become the third most important data source,after vector maps and imagery.Point cloud data ...With the rapid development of reality capture methods,such as laser scanning and oblique photogrammetry,point cloud data have become the third most important data source,after vector maps and imagery.Point cloud data also play an increasingly important role in scientific research and engineering in the fields of Earth science,spatial cognition,and smart cities.However,how to acquire high-quality three-dimensional(3D)geospatial information from point clouds has become a scientific frontier,for which there is an urgent demand in the fields of surveying and mapping,as well as geoscience applications.To address the challenges mentioned above,point cloud intelligence came into being.This paper summarizes the state-of-the-art of point cloud intelligence,with regard to acquisition equipment,intelligent processing,scientific research,and engineering applications.For this purpose,we refer to a recent project on the hybrid georeferencing of images and LiDAR data for high-quality point cloud collection,as well as a current benchmark for the semantic segmentation of high-resolution 3D point clouds.These projects were conducted at the Institute for Photogrammetry,the University of Stuttgart,which was initially headed by the late Prof.Ackermann.Finally,the development prospects of point cloud intelligence are summarized.展开更多
Non-rigid point matching has received more and more attention.Recently,many works have been developed to discover global relationships in the point set which is treated as an instance of a joint distribution.However,t...Non-rigid point matching has received more and more attention.Recently,many works have been developed to discover global relationships in the point set which is treated as an instance of a joint distribution.However,the local relationship among neighboring points is more effective under non-rigid transformations.Thus,a new algorithm taking advantage of shape context and relaxation labeling technique,called SC-RL,is proposed for non-rigid point matching.It is a strategy that joints estimation for correspondences as well as the transformation.In this work,correspondence assignment is treated as a soft-assign process in which the matching probability is updated by relaxation labeling technique with a newly defined compatibility coefficient.The compatibility coefficient is one or zero depending on whether neighboring points preserving their relative position in a local coordinate system.The comparative analysis has been performed against four state-of-the-art algorithms including SC,ICP,TPS-RPM and RPM-LNS,and the results denote that SC-RL performs better in the presence of deformations,outliers and noise.展开更多
基金supported in part by the National Natural Science Foundation of China (61421004,61402316,61333015,61632003)Doctoral Research Fund of Taiyuan University of Science and Technology under grant (20162009)National Key Technologies R&D Program(2016YFB0502002)
文摘An effective approach is proposed for 3D urban scene reconstruction in the form of point cloud with semantic labeling. Starting from high resolution oblique aerial images,our approach proceeds through three main stages: geographic reconstruction, geometrical reconstruction and semantic reconstruction. The absolute position and orientation of all the cameras relative to the real world are recovered in the geographic reconstruction stage. Then, in the geometrical reconstruction stage,an improved multi-view stereo matching method is employed to produce 3D dense points with color and normal information by taking into account the prior knowledge of aerial imagery.Finally the point cloud is classified into three classes(building,vegetation, and ground) by a rule-based hierarchical approach in the semantic reconstruction step. Experiments on complex urban scene show that our proposed 3-stage approach could generate reasonable reconstruction result robustly and efficiently.By comparing our final semantic reconstruction result with the manually labeled ground truth, classification accuracies from86.75% to 93.02% are obtained.
基金supported by the National Natural Science Foundation Project(No.42130105)Key Laboratory of Spatial-temporal Big Data Analysis and Application of Natural Resources in_Megacities,MNR(No.KFKT-2022-01).
文摘With the rapid development of reality capture methods,such as laser scanning and oblique photogrammetry,point cloud data have become the third most important data source,after vector maps and imagery.Point cloud data also play an increasingly important role in scientific research and engineering in the fields of Earth science,spatial cognition,and smart cities.However,how to acquire high-quality three-dimensional(3D)geospatial information from point clouds has become a scientific frontier,for which there is an urgent demand in the fields of surveying and mapping,as well as geoscience applications.To address the challenges mentioned above,point cloud intelligence came into being.This paper summarizes the state-of-the-art of point cloud intelligence,with regard to acquisition equipment,intelligent processing,scientific research,and engineering applications.For this purpose,we refer to a recent project on the hybrid georeferencing of images and LiDAR data for high-quality point cloud collection,as well as a current benchmark for the semantic segmentation of high-resolution 3D point clouds.These projects were conducted at the Institute for Photogrammetry,the University of Stuttgart,which was initially headed by the late Prof.Ackermann.Finally,the development prospects of point cloud intelligence are summarized.
基金Project(61002022)supported by the National Natural Science Foundation of ChinaProject(2012M512168)supported by China Postdoctoral Science Foundation
文摘Non-rigid point matching has received more and more attention.Recently,many works have been developed to discover global relationships in the point set which is treated as an instance of a joint distribution.However,the local relationship among neighboring points is more effective under non-rigid transformations.Thus,a new algorithm taking advantage of shape context and relaxation labeling technique,called SC-RL,is proposed for non-rigid point matching.It is a strategy that joints estimation for correspondences as well as the transformation.In this work,correspondence assignment is treated as a soft-assign process in which the matching probability is updated by relaxation labeling technique with a newly defined compatibility coefficient.The compatibility coefficient is one or zero depending on whether neighboring points preserving their relative position in a local coordinate system.The comparative analysis has been performed against four state-of-the-art algorithms including SC,ICP,TPS-RPM and RPM-LNS,and the results denote that SC-RL performs better in the presence of deformations,outliers and noise.
