中国科学院机构知识库网格
Chinese Academy of Sciences Institutional Repositories Grid
首页 机构 成果 学者
  
  1. CAS IR Grid
  2. 数学与系统科学研究院
  3. 中国科学院数学与系统科学研究院
GraphReg: Dynamical Point Cloud Registration With Geometry-Aware Graph Signal Processing

文献类型：期刊论文

作者Zhao, Mingyang2,3; Ma, Lei2,4,5; Jia, Xiaohong6,7; Yan, Dong-Ming8,9; Huang, Tiejun1,10,11
刊名IEEE TRANSACTIONS ON IMAGE PROCESSING
出版日期2022
卷号31页码:7449-7464
关键词Point cloud registration graph signal processing rigid dynamics robust statistics simulated annealing
ISSN号1057-7149
DOI10.1109/TIP.2022.3223793
英文摘要This study presents a high-accuracy, efficient, and physically induced method for 3D point cloud registration, which is the core of many important 3D vision problems. In contrast to existing physics-based methods that merely consider spatial point information and ignore surface geometry, we explore geometry aware rigid-body dynamics to regulate the particle (point) motion, which results in more precise and robust registration. Our proposed method consists of four major modules. First, we leverage the graph signal processing (GSP) framework to define a new signature, i.e., point response intensity for each point, by which we succeed in describing the local surface variation, resampling keypoints, and distinguishing different particles. Then, to address the shortcomings of current physics based approaches that are sensitive to outliers, we accommodate the defined point response intensity to median absolute deviation (MAD) in robust statistics and adopt the X84 principle for adaptive outlier depression, ensuring a robust and stable registration. Subsequently, we propose a novel geometric invariant under rigid transformations to incorporate higher-order features of point clouds, which is further embedded for force modeling to guide the correspondence between pairwise scans credibly. Finally, we introduce an adaptive simulated annealing (ASA) method to search for the global optimum and substantially accelerate the registration process. We perform comprehensive experiments to evaluate the proposed method on various datasets captured from range scanners to LiDAR. Results demonstrate that our proposed method outperforms representative state-of-the-art approaches in terms of accuracy and is more suitable for registering large-scale point clouds. Furthermore, it is considerably faster and more robust than most competitors. Our implementation is publicly available at https://github.com/zikail/GraphReg.
资助项目National Key Research and DevelopmentProgram of China[2020AAA0105200] ; NationalNatural Science of Foundation for Outstanding Young Scholars[12022117] ; CAS Project for Young Scientists in BasicResearch[YSBR-034] ; National Natural ScienceFoundation of China[61872354] ; National Natural ScienceFoundation of China[62172415] ; Open Research Fund Program of State key Laboratory ofHydroscience and Engineering, Tsinghua University[sklhse-2022-D-04]
WOS研究方向Computer Science ; Engineering
语种英语
WOS记录号WOS:000896645500002
出版者IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
源URL[http://ir.amss.ac.cn/handle/2S8OKBNM/60494]  
专题中国科学院数学与系统科学研究院
通讯作者Ma, Lei; Yan, Dong-Ming
作者单位1.Peking Univ, Inst Artificial Intelligence, Beijing 100871, Peoples R China
2.Beijing Acad Artificial Intelligence, Beijing 100190, Peoples R China
3.Chinese Acad Sci, Inst Automat, NLPR, Beijing 100190, Peoples R China
4.Inst Artificial Intelligence, Beijing 100190, Peoples R China
5.Peking Univ, Natl Biomed Imaging Ctr, Beijing 100871, Peoples R China
6.Chinese Acad Sci, Acad Math & Syst Sci, Beijing 100190, Peoples R China
7.UCAS, Sch Math Sci, Beijing 100149, Peoples R China
8.Chinese Acad Sci, Inst Automat, NLPR, Beijing 100864, Peoples R China
9.UCAS, Sch AI, Beijing 101408, Peoples R China
10.Peking Univ, Natl Engn Res Ctr Visual Technol, Sch Comp Sci, Beijing 100871, Peoples R China
推荐引用方式
GB/T 7714		 Zhao, Mingyang,Ma, Lei,Jia, Xiaohong,et al. GraphReg: Dynamical Point Cloud Registration With Geometry-Aware Graph Signal Processing[J]. IEEE TRANSACTIONS ON IMAGE PROCESSING,2022,31:7449-7464.
APA Zhao, Mingyang,Ma, Lei,Jia, Xiaohong,Yan, Dong-Ming,&Huang, Tiejun.(2022).GraphReg: Dynamical Point Cloud Registration With Geometry-Aware Graph Signal Processing.IEEE TRANSACTIONS ON IMAGE PROCESSING,31,7449-7464.
MLA Zhao, Mingyang,et al."GraphReg: Dynamical Point Cloud Registration With Geometry-Aware Graph Signal Processing".IEEE TRANSACTIONS ON IMAGE PROCESSING 31(2022):7449-7464.

入库方式： OAI收割

来源：数学与系统科学研究院

浏览0
下载0
收藏0
其他版本

除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。