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arXiv · 1505.06079

Robust Rotation Synchronization via Low-rank and Sparse Matrix Decomposition

Federica Arrigoni·Andrea Fusiello·Beatrice Rossi·Pasqualina Fragneto
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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.

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Federica Arrigoni, Andrea Fusiello, Beatrice Rossi, Pasqualina Fragneto. 2017-07-12. Robust Rotation Synchronization via Low-rank and Sparse Matrix Decomposition. https://doi.org/10.1016/j.cviu.2018.08.001

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