CSFM: COMMUNITY-BASED STRUCTURE FROM MOTION

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	CSFM: COMMUNITY-BASED STRUCTURE FROM MOTION
	Cui Hainan(崔海楠); Shen Shuhan(申抒含); Gao Xiang(高翔); Hu Zhanyi(胡占义)
	2017
会议日期	2017-09
会议地点	Beijing, China
英文摘要	Structure-from-Motion approaches could be broadly divided into two classes: incremental and global. While incremental manner is robust to outliers, it suffers from error accumulation and heavy computation load. To tackle these problems, global manner simultaneously estimates all camera poses, but is usu- ally sensitive to epipolar geometry outliers. In this paper, we propose an adaptive community-based SfM (CSfM) method which takes both robustness and efficiency into consideration. First, the epipolar geometry graph is parted into independent communities. Then, the reconstruction problem is solved for each community in parallel. Finally, a global similarity aver- aging method is proposed to merge the reconstruction results by solving three convex L1 optimization problems. Experi- mental results demonstrate our method performs better than many of the global SfM approaches in terms of efficiency, while achieves similar or better reconstruction accuracy and robustness than many of the state-of-the-art incremental SfM approaches.
内容类型	会议论文
源URL	[http://ir.ia.ac.cn/handle/173211/19774]
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
推荐引用方式 GB/T 7714	Cui Hainan,Shen Shuhan,Gao Xiang,et al. CSFM: COMMUNITY-BASED STRUCTURE FROM MOTION[C]. 见:. Beijing, China. 2017-09.