Efficient Center Voting for Object Detection and 6D Pose Estimation in 3D Point Cloud

doi:10.1109/TIP.2021.3078109

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 Center Voting for Object Detection and 6D Pose Estimation in 3D Point Cloud
	Guo, Jianwei 1,2; Xing, Xuejun 1,2; Quan, Weize 1,2; Yan, Dong-Ming 1,2; Gu, Qingyi 3; Liu, Yang 4; Zhang, Xiaopeng 1,2
刊名	IEEE TRANSACTIONS ON IMAGE PROCESSING
	2021
卷号	30 页码:5072-5084
关键词	Three-dimensional displays Pose estimation Shape Object detection Feature extraction Object recognition Transmission line matrix methods 6D pose estimation 3D object recognition point pair features 3D point cloud
ISSN号	1057-7149
DOI	10.1109/TIP.2021.3078109
通讯作者	Yan, Dong-Ming(yandongming@gmail.com) ; Zhang, Xiaopeng(xiaopeng.zhang@ia.ac.cn)
英文摘要	We present a novel and efficient approach to estimate 6D object poses of known objects in complex scenes represented by point clouds. Our approach is based on the well-known point pair feature (PPF) matching, which utilizes self-similar point pairs to compute potential matches and thereby cast votes for the object pose by a voting scheme. The main contribution of this paper is to present an improved PPF-based recognition framework, especially a new center voting strategy based on the relative geometric relationship between the object center and point pair features. Using this geometric relationship, we first generate votes to object centers resulting in vote clusters near real object centers. Then we group and aggregate these votes to generate a set of pose hypotheses. Finally, a pose verification operator is performed to filter out false positives and predict appropriate 6D poses of the target object. Our approach is also suitable to solve the multi-instance and multi-object detection tasks. Extensive experiments on a variety of challenging benchmark datasets demonstrate that the proposed algorithm is discriminative and robust towards similar-looking distractors, sensor noise, and geometrically simple shapes. The advantage of our work is further verified by comparing to the state-of-the-art approaches.
资助项目	National Natural Science Foundation of China[61802406] ; National Natural Science Foundation of China[61772523] ; National Natural Science Foundation of China[61972459] ; National Key Research and Development Program[2018YFB2100602] ; Scientific Instrument Developing Project of the Chinese Academy of Sciences[YJKYYQ20200045] ; Alibaba Group through Alibaba Innovative Research Program ; Suzhou Key Industrial Technology Innovation-Prospective Application Research[SYG201929]
WOS关键词	RECOGNITION
WOS研究方向	Computer Science ; Engineering
语种	英语
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
WOS记录号	WOS:000652060600006
资助机构	National Natural Science Foundation of China ; National Key Research and Development Program ; Scientific Instrument Developing Project of the Chinese Academy of Sciences ; Alibaba Group through Alibaba Innovative Research Program ; Suzhou Key Industrial Technology Innovation-Prospective Application Research
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/45220]
专题	模式识别国家重点实验室_三维可视计算
通讯作者	Yan, Dong-Ming; Zhang, Xiaopeng
作者单位	1.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit NLPR, Beijing 100190, Peoples R China 2.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China 3.Chinese Acad Sci, Inst Automat, Beijing 100190, Peoples R China 4.Suzhou CASIA All Phase Intelligence Technol Co Lt, Suzhou 215413, Peoples R China
推荐引用方式 GB/T 7714	Guo, Jianwei,Xing, Xuejun,Quan, Weize,et al. Efficient Center Voting for Object Detection and 6D Pose Estimation in 3D Point Cloud[J]. IEEE TRANSACTIONS ON IMAGE PROCESSING,2021,30:5072-5084.
APA	Guo, Jianwei.,Xing, Xuejun.,Quan, Weize.,Yan, Dong-Ming.,Gu, Qingyi.,...&Zhang, Xiaopeng.(2021).Efficient Center Voting for Object Detection and 6D Pose Estimation in 3D Point Cloud.IEEE TRANSACTIONS ON IMAGE PROCESSING,30,5072-5084.
MLA	Guo, Jianwei,et al."Efficient Center Voting for Object Detection and 6D Pose Estimation in 3D Point Cloud".IEEE TRANSACTIONS ON IMAGE PROCESSING 30(2021):5072-5084.