CASIA OpenIR  > 模式识别国家重点实验室  > 机器人视觉
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
Conference NameIEEE International Conference on Image Processing (ICIP)
Conference Date2017-09
Conference PlaceBeijing, China
Abstract
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.
Document Type会议论文
Identifierhttp://ir.ia.ac.cn/handle/173211/19774
Collection模式识别国家重点实验室_机器人视觉
Recommended Citation
GB/T 7714
Cui Hainan,Shen Shuhan,Gao Xiang,et al. CSFM: COMMUNITY-BASED STRUCTURE FROM MOTION[C],2017.
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