Efficient Pairwise 3-D Registration of Urban Scenes via Hybrid Structural Descriptors

doi:10.1109/TGRS.2021.3091380

CORC > 自动化研究所 > 中国科学院自动化研究所 > 模式识别国家重点实验室 > 三维可视计算

	Efficient Pairwise 3-D Registration of Urban Scenes via Hybrid Structural Descriptors
	Zhang, Long 1; Guo, Jianwei 1,3; Cheng, Zhanglin 2; Xiao, Jun 1; Zhang, Xiaopeng 1,3
刊名	IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
	2021-07-01
页码	17
关键词	Three-dimensional displays Shape Feature extraction Semantics Robustness Cloud computing Virtual reality Descriptor hybrid structure point cloud registration urban scene
ISSN号	0196-2892
DOI	10.1109/TGRS.2021.3091380
通讯作者	Xiao, Jun(xiaojun@ucas.ac.cn)
英文摘要	Automatic registration of point clouds captured by terrestrial laser scanning (TLS) plays an important role in many fields including remote sensing (e.g., transportation management, 3-D reconstruction in large-scale urban areas and environment monitoring), computer vision, and virtual reality and robotics. However, noise, outliers, nonuniform point density, and small overlaps are inevitable when collecting multiple views of data, which poses great challenges to 3-D registration of point clouds. Since conventional registration methods aim to find point correspondences and estimate transformation parameters directly in the original point space, the traditional way to address these difficulties is to introduce many restrictions during the scanning process (e.g., more scanning and careful selection of scanning positions), thus making the data acquisition more difficult. In this article, we present a novel 3-D registration framework that performs in a ``middle-level structural space'' and is capable of robustly and efficiently reconstructing urban, semiurban, and indoor scenes, despite disturbances introduced in the scanning process. The new structural space is constructed by extracting multiple types of middle-level geometric primitives (planes, spheres, cylinders, and cones) from the 3-D point cloud. We design a robust method to find effective primitive combinations corresponding to the 6-D poses of the raw point clouds and then construct hybrid-structure-based descriptors. By matching descriptors and computing rotation and translation parameters, successful registration is achieved. Note that the whole process of our method is performed in the structural space, which has the advantages of capturing geometric structures (the relationship between primitives) and semantic features (primitive types and parameters) in larger fields. Experiments show that our method achieves state-of-the-art performance in several point cloud registration benchmark datasets at different scales and even obtains good registration results for data without overlapping areas.
资助项目	National Key Research and Development Program of China[2018YFB2100602] ; Strategic Priority Research Program of the Chinese Academy of Sciences[XDA23090304] ; National Natural Science Foundation of China[61802406] ; National Natural Science Foundation of China[U2003109] ; National Natural Science Foundation of China[61972388] ; National Natural Science Foundation of China[61972459] ; Key Research Program of Frontier Sciences CAS[QYZDYSSW-SYS004] ; Shenzhen Basic Research Program[JCYJ20180507182222355] ; Youth Innovation Promotion Association of the Chinese Academy of Sciences[Y201935] ; Fundamental Research Funds for the Central Universities
WOS关键词	POINT CLOUDS ; NONRIGID REGISTRATION ; SEGMENTATION ; HISTOGRAMS ; RANSAC
WOS研究方向	Geochemistry & Geophysics ; Engineering ; Remote Sensing ; Imaging Science & Photographic Technology
语种	英语
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
WOS记录号	WOS:000733509500001
资助机构	National Key Research and Development Program of China ; Strategic Priority Research Program of the Chinese Academy of Sciences ; National Natural Science Foundation of China ; Key Research Program of Frontier Sciences CAS ; Shenzhen Basic Research Program ; Youth Innovation Promotion Association of the Chinese Academy of Sciences ; Fundamental Research Funds for the Central Universities
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/46908]
专题	模式识别国家重点实验室_三维可视计算
通讯作者	Xiao, Jun
作者单位	1.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China 2.Chinese Acad Sci, Shenzhen Inst Adv Technol, Shenzhen Key Lab Visual Comp & Analyt VisuCA, Shenzhen 518055, Peoples R China 3.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
推荐引用方式 GB/T 7714	Zhang, Long,Guo, Jianwei,Cheng, Zhanglin,et al. Efficient Pairwise 3-D Registration of Urban Scenes via Hybrid Structural Descriptors[J]. IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING,2021:17.
APA	Zhang, Long,Guo, Jianwei,Cheng, Zhanglin,Xiao, Jun,&Zhang, Xiaopeng.(2021).Efficient Pairwise 3-D Registration of Urban Scenes via Hybrid Structural Descriptors.IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING,17.
MLA	Zhang, Long,et al."Efficient Pairwise 3-D Registration of Urban Scenes via Hybrid Structural Descriptors".IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING (2021):17.