Robust Rotation Synchronization via Low-rank and Sparse Matrix Decomposition
This addresses robust global alignment in computer vision, though it appears incremental as it builds on existing decomposition methods.
The paper tackled the rotation synchronization problem for 3D point-set registration and structure from motion by formulating it as a low-rank and sparse matrix decomposition to handle outliers and missing data, resulting in R-GoDec being the fastest robust algorithm in experiments.
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.