Research map / Point clouds / Registration and matching
Inverse Composition Discriminative Optimization for Point Cloud Registration
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use ICDO instead of SVR | Point cloud registration · Stanford's dragon, ±24° | successful registrations: 30 vs 30 | No clear effect |
| Use ICDO instead of SVR | Point cloud registration · Stanford's dragon, ±48° | successful registrations: 29 vs 26 | Hurt |
| Use ICDO instead of SVR | Point cloud registration · Stanford's dragon, ±72° | successful registrations: 16 vs 15 | Hurt |
| Use ICDO instead of SVR | Point cloud registration · Stanford's dragon, ±96° | successful registrations: 4 vs 0 | Hurt |
| Use ICDO instead of the other evaluated methods | Point cloud registration · ETH laser registration dataset | registration error | Helped |
| Train the update maps to learn an annealing schedule | ICDO · Synthetic point cloud registration | success rate | Helped |
Closest papers by meaning
- PSReg: Prior-guided Sparse Mixture of Experts for Point Cloud Registration (AAAI 2025 · similarity 0.87)
- HybridReg: Robust 3D Point Cloud Registration with Hybrid Motions (AAAI 2025 · similarity 0.86)
- FreeReg: Image-to-Point Cloud Registration Leveraging Pretrained Diffusion Models and Monocular Depth Estimators (ICLR 2024 · similarity 0.86)
- A robust inlier identification algorithm for point cloud registration via $\mathbf{\ell_0}$-minimization (NeurIPS 2024 · similarity 0.85)
- PointMC: Multi-instance Point Cloud Registration based on Maximal Cliques (ICML 2024 · similarity 0.85)
- PosDiffNet: Positional Neural Diffusion for Point Cloud Registration in a Large Field of View with Perturbations (AAAI 2024 · similarity 0.85)
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