Tracks selection for robust, efficient and scalable large-scale structure from motion

doi:10.1016/j.patcog.2017.08.002

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 > 机器人视觉团队

	Tracks selection for robust, efficient and scalable large-scale structure from motion
	Cui, Hainan1 ; Shen, Shuhan1,2 ; Hu, Zhanyi1,2,3 ; Cui Hainan(崔海楠)
刊名	PATTERN RECOGNITION
	2017-12-01
卷号	72 期号:72 页码:341-354
关键词	Tracks Selection Bundle Adjustment Structure From Motion
DOI	10.1016/j.patcog.2017.08.002
文献子类	Article
英文摘要	Currently global structure-from-motion (SfM) pipeline consists of four steps: estimating camera rotations first, then computing camera positions, triangulating tracks, and finally doing bundle adjustment. However, for large-scale SfM problems, the tracks are usually too noisy and redundant for the bundle adjustment. Thus in this work, we propose a novel fast tracks selection method to improve both efficiency and robustness of the bundle adjustment. Firstly, three selection criteria: Compactness, Accurateness, and Connectedness, are introduced, where the first two are to calculate a selection priority for each track and the third is to guarantee the completeness of scene structure. Then, to satisfy these criteria, a more informative subset of tracks is selected by covering multiple spanning trees of epipolar geometry graph. Since tracks selection acts only an intermediate step in the whole SfM pipeline, it can be in principle embedded into any global SfM pipelines. To validate the effectiveness of our tracks selection module, we insert it into a state-of-the-art global SfM system and compare it with three other selection methods. Extensive experiments show that by embedding our tracks selection module, the new SfM system performs similarly or better than the original one in terms of reconstruction completeness and accuracy, but is much more efficient and scalable for large-scale scene reconstructions. Finally, our tracks selection module is further embedded into two other global SfM systems to demonstrated its versatility. (C) 2017 Elsevier Ltd. All rights reserved.
WOS研究方向	Computer Science ; Engineering
语种	英语
WOS记录号	WOS:000411545400025
资助机构	NSFC (Natural Science Foundation of China)(61333015) ; National Key R&D Program of China(2016YFB0502002) ; NSFC(61421004 ; 61632003)
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/19643]
专题	自动化研究所_模式识别国家重点实验室_机器人视觉团队
通讯作者	Cui Hainan(崔海楠)
作者单位	1.Chinese Acad Sci, Inst Automat, NLPR, Beijing, Peoples R China 2.Univ Chinese Acad Sci, Beijing, Peoples R China 3.CAS Ctr Excellence Brain Sci & Intelligence Techn, Beijing, Peoples R China
推荐引用方式 GB/T 7714	Cui, Hainan,Shen, Shuhan,Hu, Zhanyi,et al. Tracks selection for robust, efficient and scalable large-scale structure from motion[J]. PATTERN RECOGNITION,2017,72(72):341-354.
APA	Cui, Hainan,Shen, Shuhan,Hu, Zhanyi,&Cui Hainan.(2017).Tracks selection for robust, efficient and scalable large-scale structure from motion.PATTERN RECOGNITION,72(72),341-354.
MLA	Cui, Hainan,et al."Tracks selection for robust, efficient and scalable large-scale structure from motion".PATTERN RECOGNITION 72.72(2017):341-354.