Research map / Optimization / Nonconvex and smooth optimization
Rotation Coordinate Descent for Fast Globally Optimal Rotation Averaging
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| use RCD instead of Shonan | rotation averaging · SfM camera graphs, n=1,800, d_G=0.4 | runtime [s]: 312.9s -> 1.5s | Helped |
| use RCD instead of Shonan | rotation averaging · Orebro Castle | runtime [s]: 1.10 -> 26.17 | Helped |
| add local optimisation to RCD (ablation) | RCD · sphere | runtime [s]: 105.70 -> 0.66 | Helped |
| add local optimisation to RCD (ablation) | RCD · torus | runtime [s]: 1808.86 -> 4.86 | Helped |
| add local optimisation to RCD (ablation) | RCD · grid3D | runtime [s]: 1199.30 -> 14.78 | Helped |
| add local optimisation to RCD (ablation) | RCD · garage | runtime [s]: 3.74 -> 0.28 | Helped |
| add local optimisation to RCD (ablation) | RCD · San Marco | runtime [s]: 1.61 -> 145.46 | Hurt |
| use RCD instead of Shonan | rotation averaging · San Marco | runtime [s]: 110.07 -> 1.61 | Helped |
| use RCD without local optimisation instead of Shonan | rotation averaging · torus | runtime [s]: 15.76 -> 1808.86 | Hurt |
| add local optimisation to RCD (ablation) | RCD · real-world SfM datasets | runtime [s] | Mixed |
| increase graph density | RCD relative to Shonan · synthetic camera graphs | runtime [s] | Helped |
| use RCD instead of Shonan on very sparse graphs | rotation averaging · synthetic camera graphs with d_G < 0.04 | runtime [s] | No clear effect |
Closest papers by meaning
- When Cyclic Coordinate Descent Outperforms Randomized Coordinate Descent (NeurIPS 2017 · similarity 0.85)
- Rotation Averaging and Strong Duality (CVPR 2018 · similarity 0.85)
- Global Optimality for Point Set Registration Using Semidefinite Programming (CVPR 2020 · similarity 0.85)
- Provable Benefit of Random Permutations over Uniform Sampling in Stochastic Coordinate Descent (ICML 2025 · similarity 0.84)
- Enhanced Cyclic Coordinate Descent Methods for Elastic Net Penalized Linear Models (NeurIPS 2025 · similarity 0.82)
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