Name
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. |
Year | Venue | Field |
|---|---|---|
2015 | CoRR | Structure from motion,Synchronization,Matrix decomposition,Outlier,Theoretical computer science,Minification,Artificial intelligence,Missing data,Mathematics,Sparse matrix,Machine learning |
DocType | Volume | Citations |
Journal | abs/1505.06079 | 1 |
PageRank | References | Authors |
0.36 | 47 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| federica arrigoni | 1 | 34 | 5.86 |
| Andrea Fusiello | 2 | 1470 | 99.31 |
| Beatrice Rossi | 3 | 64 | 10.56 |
| Pasqualina Fragneto | 4 | 131 | 14.36 |