Shonan Rotation Averaging: Global Optimality by Surfing SO(p)(n)
Abstract
Shonan Rotation Averaging is a fast, simple, and elegant rotation averaging algorithm that is guaranteed to recover globally optimal solutions under mild assumptions on the measurement noise. Our method employs semidefinite relaxation in order to recover provably globally optimal solutions of the rotation averaging problem. In contrast to prior work, we show how to solve large-scale instances of these relaxations using manifold minimization on (only slightly) higher-dimensional rotation manifolds, re-using existing high-performance (but local) structure-from-motion pipelines. Our method thus preserves the speed and scalability of those, while enabling the recovery of globally optimal solutions.
Cite
Text
Dellaert et al. "Shonan Rotation Averaging: Global Optimality by Surfing SO(p)(n)." Proceedings of the European Conference on Computer Vision (ECCV), 2020. doi:10.1007/978-3-030-58539-6_18Markdown
[Dellaert et al. "Shonan Rotation Averaging: Global Optimality by Surfing SO(p)(n)." Proceedings of the European Conference on Computer Vision (ECCV), 2020.](https://mlanthology.org/eccv/2020/dellaert2020eccv-shonan/) doi:10.1007/978-3-030-58539-6_18BibTeX
@inproceedings{dellaert2020eccv-shonan,
title = {{Shonan Rotation Averaging: Global Optimality by Surfing SO(p)(n)}},
author = {Dellaert, Frank and Rosen, David M. and Wu, Jing and Mahony, Robert and Carlone, Luca},
booktitle = {Proceedings of the European Conference on Computer Vision (ECCV)},
year = {2020},
doi = {10.1007/978-3-030-58539-6_18},
url = {https://mlanthology.org/eccv/2020/dellaert2020eccv-shonan/}
}