Minimal Case Relative Pose Computation using Ray-Point-Ray Features
2020-05-01
发表期刊IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
ISSN0162-8828
卷号PP期号:99页码:1
发表状态已发表
DOI10.1109/TPAMI.2019.2892372
摘要

Corners 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 with only 3 correspondences across 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.

关键词Three-dimensional displays Transmission line matrix methods Cameras Pose estimation Feature extraction Geometry Computer vision
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收录类别SCI ; SCIE ; EI
EI入藏号20201508411092
EI主题词Artificial intelligence ; Computer vision
EI分类号Artificial Intelligence:723.4 ; Computer Applications:723.5
原始文献类型Early Access Articles
来源库IEEE
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文献类型期刊论文
条目标识符https://kms.shanghaitech.edu.cn/handle/2MSLDSTB/29918
专题信息科学与技术学院_PI研究组_Laurent Kneip组
作者单位
1.TuSimple, Beijing, China
2.ShanghaiTech University, Shanghai, China
3.Institute of Automation, Chinese Academy of Sciences, Beijing, China
4.Electronic Information School, Wuhan University, Wuhan, China
推荐引用方式
GB/T 7714
Ji Zhao,Laurent Kneip,Yijia He,et al. Minimal Case Relative Pose Computation using Ray-Point-Ray Features[J]. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE,2020,PP(99):1.
APA Ji Zhao,Laurent Kneip,Yijia He,&Jiayi Ma.(2020).Minimal Case Relative Pose Computation using Ray-Point-Ray Features.IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE,PP(99),1.
MLA Ji Zhao,et al."Minimal Case Relative Pose Computation using Ray-Point-Ray Features".IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE PP.99(2020):1.
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