Robust Rotation Synchronization via Low-rank and Sparse Matrix Decomposition
Computer Vision and Pattern Recognition
2019-02-05 v2
Abstract
This paper deals with the rotation synchronization problem, which arises in global registration of 3D point-sets and in structure from motion. The problem is formulated in an unprecedented way as a "low-rank and sparse" matrix decomposition that handles both outliers and missing data. A minimization strategy, dubbed R-GoDec, is also proposed and evaluated experimentally against state-of-the-art algorithms on simulated and real data. The results show that R-GoDec is the fastest among the robust algorithms.
Cite
@article{arxiv.1505.06079,
title = {Robust Rotation Synchronization via Low-rank and Sparse Matrix Decomposition},
author = {Federica Arrigoni and Andrea Fusiello and Beatrice Rossi and Pasqualina Fragneto},
journal= {arXiv preprint arXiv:1505.06079},
year = {2019}
}
Comments
The material contained in this paper is part of a manuscript submitted to CVIU