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	HSfM: Hybrid Structure-from-Motion
	Cui Hainan(崔海楠); Xiang Gao(高翔); Shen Shuhan(申抒含); Hu Zhanyi(胡占义)
	2017
会议日期	2017-06
会议地点	USA,Honolulu
英文摘要	Structure-from-Motion (SfM) methods can be broadly categorized as incremental or global according to their ways to estimate initial camera poses. While incremental system has advanced in robustness and accuracy, the ef- ficiency remains its key challenge. To solve this problem, global reconstruction system simultaneously estimates al- l camera poses from the epipolar geometry graph, but it is usually sensitive to outliers. In this work, we propose a new hybrid SfM method to tackle the issues of efficien- cy, accuracy and robustness in a unified framework. More specifically, we propose an adaptive community-based ro- tation averaging method first to estimate camera rotations in a global manner. Then, based on these estimated camera rotations, camera centers are computed in an incremental way. Extensive experiments show that our hybrid method performs similarly or better than many of the state-of-the- art global SfM approaches, in terms of computational effi- ciency, while achieves similar reconstruction accuracy and robustness with two other state-of-the-art incremental SfM approaches
内容类型	会议论文
源URL	[http://ir.ia.ac.cn/handle/173211/19765]
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 机器人视觉团队
推荐引用方式 GB/T 7714	Cui Hainan,Xiang Gao,Shen Shuhan,et al. HSfM: Hybrid Structure-from-Motion[C]. 见:. USA,Honolulu. 2017-06.

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