Message Passing Least Squares Framework and its Application to Rotation Synchronization
About
We propose an efficient algorithm for solving group synchronization under high levels of corruption and noise, while we focus on rotation synchronization. We first describe our recent theoretically guaranteed message passing algorithm that estimates the corruption levels of the measured group ratios. We then propose a novel reweighted least squares method to estimate the group elements, where the weights are initialized and iteratively updated using the estimated corruption levels. We demonstrate the superior performance of our algorithm over state-of-the-art methods for rotation synchronization using both synthetic and real data.
Yunpeng Shi, Gilad Lerman• 2020
Related benchmarks
| Task | Dataset | Result | Rank | |
|---|---|---|---|---|
| Rotation Averaging | 1DSfM Alamo 1.0 | Mean Angular Error3.44 | 6 | |
| Rotation Averaging | 1DSfM 1.0 (Montreal Notre Dame) | Mean Angular Error1.04 | 6 | |
| Rotation Averaging | 1DSfM Yorkminster 1.0 | Mean Angular Error2.47 | 6 | |
| Rotation Averaging | 1DSfM Piccadilly 1.0 | Mean Angular Error3.93 | 6 | |
| Rotation Averaging | 1DSfM 1.0 (Ellis Island) | Mean Angular Error2.61 | 6 | |
| Rotation Averaging | 1DSfM Madrid Metropolis 1.0 | Mean Angular Error4.65 | 6 | |
| Rotation Averaging | 1DSfM Piazza del Popolo 1.0 | Mean Angular Error3.73 | 6 | |
| Rotation Averaging | 1DSfM Roman Forum 1.0 | Mean Angular Error (mn)2.62 | 6 | |
| Rotation Averaging | 1DSfM Tower of London 1.0 | Mean Angular Error (mn)3.16 | 6 | |
| Rotation Averaging | 1DSfM Union Square 1.0 | Mean Angular Error6.54 | 6 |
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