Knowledge Commons of Institute of Automation,CAS
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 | |
会议名称 | IEEE International Conference on Image Processing (ICIP) |
会议日期 | 2017-09 |
会议地点 | Beijing, China |
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. |
文献类型 | 会议论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/19774 |
专题 | 多模态人工智能系统全国重点实验室_机器人视觉 |
推荐引用方式 GB/T 7714 | Cui Hainan,Shen Shuhan,Gao Xiang,et al. CSFM: COMMUNITY-BASED STRUCTURE FROM MOTION[C],2017. |
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文件名称/大小 | 文献类型 | 版本类型 | 开放类型 | 使用许可 | ||
ICIP2017_Cuihainan.p(6880KB) | 会议论文 | 开放获取 | CC BY-NC-SA | 浏览 下载 |
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