Accelerating Globally Optimal Consensus Maximization in Geometric Vision

doi:10.1109/TPAMI.2024.3357067

	Accelerating Globally Optimal Consensus Maximization in Geometric Vision
	Zhang, Xinyue1 ; Peng, Liangzu1 ; Xu, Wanting1 ; Kneip, Laurent 2
	2024
发表期刊	IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE (IF:20.8[JCR-2023],22.2[5-Year])
ISSN	0162-8828
EISSN	1939-3539
卷号	PP 期号:99 页码:1-18
发表状态	已发表
DOI	10.1109/TPAMI.2024.3357067
摘要	Branch-and-bound-based consensus maximization stands out due to its important ability of retrieving the globally optimal solution to outlier-affected geometric problems. However, while the discovery of such solutions caries high scientific value, its application in practical scenarios is often prohibited by its computational complexity growing exponentially as a function of the dimensionality of the problem at hand. In this work, we convey a novel, general technique that allows us to branch over an $n-1$ dimensional space for an n-dimensional problem. The remaining degree of freedom can be solved globally optimally within each bound calculation by applying the efficient interval stabbing technique. While each individual bound derivation is harder to compute owing to the additional need for solving a sorting problem, the reduced number of intervals and tighter bounds in practice lead to a significant reduction in the overall number of required iterations. Besides an abstract introduction of the approach, we present applications to four fundamental geometric computer vision problems: camera resectioning, relative camera pose estimation, point set registration, and rotation and focal length estimation. Through our exhaustive tests, we demonstrate significant speed-up factors at times exceeding two orders of magnitude, thereby increasing the viability of globally optimal consensus maximizers in online application scenarios. IEEE
关键词	Branch and bound method Computer vision Degrees of freedom (mechanics) Geometrical optics Geometry Problem solving Three dimensional displays Branch and bounds Computational modelling Consensus maximization Fitting Geometric vision Interval stabbing Optimal solutions Optimisations Pose-estimation Three-dimensional display
URL	查看原文
收录类别	EI
语种	英语
出版者	IEEE Computer Society
EI入藏号	20240615488405
EI主题词	Cameras
EI分类号	722.2 Computer Peripheral Equipment ; 723.5 Computer Applications ; 741.1 Light/Optics ; 741.2 Vision ; 742.2 Photographic Equipment ; 921 Mathematics ; 921.5 Optimization Techniques ; 931.1 Mechanics
原始文献类型	Article in Press
来源库	IEEE
引用统计	正在获取...
文献类型	期刊论文
条目标识符	https://kms.shanghaitech.edu.cn/handle/2MSLDSTB/349740
专题	信息科学与技术学院_博士生信息科学与技术学院_硕士生
通讯作者	Kneip, Laurent
作者单位	1.Mobile Perception Lab, ShanghaiTech University, China 2.Shanghai Engineering Research Center of Intelligent Vision and Imaging, China
第一作者单位	上海科技大学
第一作者的第一单位	上海科技大学
推荐引用方式 GB/T 7714	Zhang, Xinyue,Peng, Liangzu,Xu, Wanting,et al. Accelerating Globally Optimal Consensus Maximization in Geometric Vision[J]. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE,2024,PP(99):1-18.
APA	Zhang, Xinyue,Peng, Liangzu,Xu, Wanting,&Kneip, Laurent.(2024).Accelerating Globally Optimal Consensus Maximization in Geometric Vision.IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE,PP(99),1-18.
MLA	Zhang, Xinyue,et al."Accelerating Globally Optimal Consensus Maximization in Geometric Vision".IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE PP.99(2024):1-18.