CASIA OpenIR
Minimal Case Relative Pose Computation Using Ray-Point-Ray Features
Zhao, Ji1; Kneip, Laurent2; He, Yijia3; Ma, Jiayi4
Source PublicationIEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
ISSN0162-8828
2020-05-01
Volume42Issue:5Pages:1176-1190
Corresponding AuthorKneip, Laurent(lkneip@shanghaitech.edu.cn)
AbstractCorners are popular features for relative pose computation with 2D-2D point correspondences. Stable corners may be formed by two 3D rays sharing a common starting point. We call such elements ray-point-ray (RPR) structures. Besides a local invariant keypoint given by the lines' intersection, their reprojection also defines a corner orientation and an inscribed angle in the image plane. The present paper investigates such RPR features, and aims at answering the fundamental question of what additional constraints can be formed from correspondences between RPR features in two views. In particular, we show that knowing the value of the inscribed angle between the two 3D rays poses additional constraints on the relative orientation. Using the latter enables the solution of the relative pose problem with as few as 3 correspondences across the two images. We provide a detailed analysis of all minimal cases distinguishing between 90-degree RPR-structures and structures with an arbitrary, known inscribed angle. We furthermore investigate the special cases of a known directional correspondence and planar motion, the latter being solvable with only a single RPR correspondence. We complete the exposition by outlining an image processing technique for robust RPR-feature extraction. Our results suggest high practicality in man-made environments, where 90-degree RPR-structures naturally occur.
KeywordThree-dimensional displays Transmission line matrix methods Cameras Pose estimation Feature extraction Geometry Computer vision Structure-from-motion visual odometry minimal relative pose automatic solver generation Grobner bases ray-point-ray structures
DOI10.1109/TPAMI.2019.2892372
WOS KeywordCLOSED-FORM SOLUTION ; EGOMOTION ESTIMATION ; MOTION
Indexed BySCI
Language英语
Funding ProjectNational Natural Science Foundation of China[61773295] ; ShanghaiTech University
Funding OrganizationNational Natural Science Foundation of China ; ShanghaiTech University
WOS Research AreaComputer Science ; Engineering
WOS SubjectComputer Science, Artificial Intelligence ; Engineering, Electrical & Electronic
WOS IDWOS:000523685800012
PublisherIEEE COMPUTER SOC
Citation statistics
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/38768
Collection中国科学院自动化研究所
Corresponding AuthorKneip, Laurent
Affiliation1.TuSimple, Beijing 100020, Peoples R China
2.ShanghaiTech Univ, Shanghai 201210, Peoples R China
3.Chinese Acad Sci, Inst Automat, Beijing 100190, Peoples R China
4.Wuhan Univ, Elect Informat Sch, Wuhan 430072, Peoples R China
Recommended Citation
GB/T 7714
Zhao, Ji,Kneip, Laurent,He, Yijia,et al. Minimal Case Relative Pose Computation Using Ray-Point-Ray Features[J]. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE,2020,42(5):1176-1190.
APA Zhao, Ji,Kneip, Laurent,He, Yijia,&Ma, Jiayi.(2020).Minimal Case Relative Pose Computation Using Ray-Point-Ray Features.IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE,42(5),1176-1190.
MLA Zhao, Ji,et al."Minimal Case Relative Pose Computation Using Ray-Point-Ray Features".IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 42.5(2020):1176-1190.
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