Knowledge Commons of Institute of Automation,CAS
Voting-based Incremental Structure-from-Motion | |
Cui HN(崔海楠)![]() ![]() ![]() | |
2018 | |
会议名称 | International Conference on Pattern Recognition |
会议日期 | 2018-08 |
会议地点 | Beijing,China |
摘要 |
Incremental Structure-from-Motion (SfM) technique is the most prevalent way for image-based reconstruction, but its robustness is highly relying on each camera registration, where a false calibration could make everything following fail. In this paper, we propose a voting-based incremental SfM approach to improve upon the camera registration process. First, the degree of closeness between cameras is used as the vote to determine which cameras are going to register. Then, for each camera, two methods are simultaneously used to estimate the camera pose, and the number of inliers is used as the vote to determine which pose is more accurate. Finally, by estimating the priori global camera rotations from the view-graph, the camera poses that are consistent with the priori camera rotations are considered as getting double votes and preferentially kept. After all these prioritized cameras are calibrated, the other cameras are then incrementally registered. Compared to the state-of-the-art incremental SfM approaches, extensive experiments demonstrate that our system performs similarly or better in terms of reconstruction efficiency, while achieves a better robustness and accuracy. Especially for the ambiguous datasets, our system has a better potential to reconstruct them. |
收录类别 | EI |
语种 | 英语 |
文献类型 | 会议论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/21977 |
专题 | 多模态人工智能系统全国重点实验室_机器人视觉 |
作者单位 | 中国科学院自动化研究所 |
推荐引用方式 GB/T 7714 | Cui HN,Shuhan Shen,Wei Gao. Voting-based Incremental Structure-from-Motion[C],2018. |
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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 | ||
ICPR2018.pdf(5959KB) | 会议论文 | 开放获取 | CC BY-NC-SA | 浏览 下载 |
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