Estimation of Camera Locations in Highly Corrupted Scenarios: All About That Base, No Shape Trouble
Abstract
We propose a strategy for improving camera location estimation in structure from motion. Our setting assumes highly corrupted pairwise directions (i.e., normalized relative location vectors), so there is a clear room for improving current state-of-the-art solutions for this problem. Our strategy identifies severely corrupted pairwise directions by using a geometric consistency condition. It then selects a cleaner set of pairwise directions as a preprocessing step for common solvers. We theoretically guarantee the successful performance of a basic version of our strategy under a synthetic corruption model. Numerical results on artificial and real data demonstrate the significant improvement obtained by our strategy.
Cite
Text
Shi and Lerman. "Estimation of Camera Locations in Highly Corrupted Scenarios: All About That Base, No Shape Trouble." Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2018. doi:10.1109/CVPR.2018.00303Markdown
[Shi and Lerman. "Estimation of Camera Locations in Highly Corrupted Scenarios: All About That Base, No Shape Trouble." Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2018.](https://mlanthology.org/cvpr/2018/shi2018cvpr-estimation/) doi:10.1109/CVPR.2018.00303BibTeX
@inproceedings{shi2018cvpr-estimation,
title = {{Estimation of Camera Locations in Highly Corrupted Scenarios: All About That Base, No Shape Trouble}},
author = {Shi, Yunpeng and Lerman, Gilad},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
year = {2018},
doi = {10.1109/CVPR.2018.00303},
url = {https://mlanthology.org/cvpr/2018/shi2018cvpr-estimation/}
}