Research map / Probabilistic and scientific modeling / Variational and generative sampling
Probabilistic Permutation Synchronization Using the Riemannian Structure of the Birkhoff Polytope
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use Birkhoff-LRBFGS instead of the Wang method | Multiway matching on Willow Object Class · Willow Object Class average | recall: 0.99 -> 0.77 | Hurt |
| Initialize Wang's geometric verification with Birkhoff-LRBFGS | Willow Object Class · Willow Object Class average | recall: 0.99 -> 0.99 | No clear effect |
| Use Birkhoff-LRBFGS instead of MatchEIG | Synthetic synchronization problems · Synthetic data | recall: 83% -> 91% | Helped |
| Use Birkhoff-LRBFGS instead of Wang et al. | 3D correspondence refinement · Tosca Cat, N=200; N=50; N=20 | recall: 37.83% -> 39.08%; 56.13% -> 56.93%; 38.33% -> 40.33% | Helped |
| Use Birkhoff-LRBFGS instead of Wang et al. | 3D correspondence refinement · Tosca Michael, N=200; N=50; N=20 | recall: 35.92% -> 36.12%; 54.93% -> 54.62%; 51.63% -> 55.95% | Mixed |
| Add one probabilistically ranked sample instead of a random hypothesis | Willow top-K matching · Willow top-K average | recall: 0.71 -> 0.75 | Helped |
| Add two probabilistically ranked samples instead of random hypotheses | Willow top-K matching · Willow top-K average | recall: 0.74 -> 0.79 | Helped |
Closest papers by meaning
- Synchronizing Probability Measures on Rotations via Optimal Transport (CVPR 2020 · similarity 0.88)
- Efficient Detection of Long Consistent Cycles and its Application to Distributed Synchronization (CVPR 2024 · similarity 0.84)
- Riemannian Consistency Model (NeurIPS 2025 · similarity 0.84)
- Fully Dynamic Euclidean Bi-Chromatic Matching in Sublinear Update Time (ICML 2025 · similarity 0.83)
- Tensor-Based Synchronization and the Low-Rankness of the Block Trifocal Tensor (NeurIPS 2024 · similarity 0.83)
- Graph Alignment via Birkhoff Relaxation (NeurIPS 2025 · similarity 0.82)
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