Research map / Learning theory and algorithms / Clustering
Finding Geometric Models by Clustering in the Consensus Space
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use the connected-component sampler instead of P-NAPSAC (ablation) | Proposed multi-model fitting · AdelaideRMF two-view motions | ME (%): 5.3 -> 5.0 | Helped |
| Use the connected-component sampler instead of P-NAPSAC (ablation) | Proposed multi-model fitting · AdelaideRMF two-view motions | time (secs): 0.05 -> 0.02 | Helped |
| Use the connected-component sampler instead of P-NAPSAC (ablation) | Proposed multi-model fitting · AdelaideRMF homographies | ME (%): 3.1 -> 5.7 | Hurt |
| Use the connected-component sampler instead of P-NAPSAC (ablation) | Proposed multi-model fitting · AdelaideRMF homographies | time (secs): 0.11 -> 0.11 | No clear effect |
| Use multiple homographies alongside the essential matrix to select relative pose | Relative pose estimation · 1DSfM | avg. rotation error (degrees): 9.51 -> 8.24 | Helped |
| Use multiple homographies alongside the essential matrix to select relative pose | Relative pose estimation · 1DSfM | avg. translation error (degrees): 18.15 -> 17.81 | Helped |
| Initialize global SfM with poses estimated from E and multiple homographies using CC | Global SfM · 1DSfM | avg. rotation error (degrees): 11.15 -> 5.56 | Helped |
| Initialize global SfM with poses estimated from E and multiple homographies using CC | Global SfM · 1DSfM | avg. position error (m): 10.25 -> 9.57 | Helped |
| Select pose from generalized E and multiple generalized homographies | Generalized-camera pose estimation · KITTI | avg. rotation error (degrees): 1.19 -> 0.32 | Helped |
| Select pose from generalized E and multiple generalized homographies | Generalized-camera pose estimation · KITTI | avg. position error (m): 0.033 -> 0.026 | Helped |
| Use soft point-to-model assignment rather than hard assignment (ablation) | Fast-moving object trajectory fitting · TbD and TbD-3D | average trajectory error | Helped |
Closest papers by meaning
- PARSAC: Accelerating Robust Multi-Model Fitting with Parallel Sample Consensus (AAAI 2024 · similarity 0.88)
- Noisy One-point Homographies are Surprisingly Good (CVPR 2024 · similarity 0.86)
- Geometric Analysis of Nonlinear Manifold Clustering (NeurIPS 2024 · similarity 0.85)
- Revisiting Sampson Approximations for Geometric Estimation Problems (CVPR 2024 · similarity 0.85)
- Cycle-Sync: Robust Global Camera Pose Estimation through Enhanced Cycle-Consistent Synchronization (NeurIPS 2025 · similarity 0.85)
- Learning to Filter Outlier Edges in Global SfM (CVPR 2025 · similarity 0.84)
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