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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)
2017-12-01
发表期刊PATTERN RECOGNITION
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
DOI10.1016/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
文献类型期刊论文
条目标识符http://ir.ia.ac.cn/handle/173211/19643
专题模式识别国家重点实验室_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
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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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