ShanghaiTech University Knowledge Management System
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 |
ISSN | 0162-8828 |
卷号 | PP期号:99页码:1 |
发表状态 | 已发表 |
DOI | 10.1109/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 |
URL | 查看原文 |
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 |
引用统计 | |
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. |
条目包含的文件 | ||||||
文件名称/大小 | 文献类型 | 版本类型 | 开放类型 | 使用许可 |
个性服务 |
查看访问统计 |
谷歌学术 |
谷歌学术中相似的文章 |
[Ji Zhao]的文章 |
[Laurent Kneip]的文章 |
[Yijia He]的文章 |
百度学术 |
百度学术中相似的文章 |
[Ji Zhao]的文章 |
[Laurent Kneip]的文章 |
[Yijia He]的文章 |
必应学术 |
必应学术中相似的文章 |
[Ji Zhao]的文章 |
[Laurent Kneip]的文章 |
[Yijia He]的文章 |
相关权益政策 |
暂无数据 |
收藏/分享 |
修改评论
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。