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

doi:10.1016/j.patcog.2017.08.002

	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)
发表期刊	PATTERN RECOGNITION
	2017-12-01
卷号	72 期号:72 页码:341-354
文章类型	Article
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.
关键词	Tracks Selection Bundle Adjustment Structure From Motion
WOS标题词	Science & Technology ; Technology
DOI	10.1016/j.patcog.2017.08.002
收录类别	SCI
语种	英语
项目资助者	NSFC (Natural Science Foundation of China)(61333015) ; National Key R&D Program of China(2016YFB0502002) ; NSFC(61421004 ; 61632003)
WOS研究方向	Computer Science ; Engineering
WOS类目	Computer Science, Artificial Intelligence ; Engineering, Electrical & Electronic
WOS记录号	WOS:000411545400025
引用统计	被引频次：13[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/19643
专题	多模态人工智能系统全国重点实验室_机器人视觉
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
第一作者单位	模式识别国家重点实验室
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.

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