Research map / Point clouds / Registration and matching
Global Optimality for Point Set Registration Using Semidefinite Programming
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use the alternative Schur-complement spectral bound (Theorem 2) instead of the first spectral bound (Theorem 1) | Point set registration optimality certification · Synthetic point-set registration experiments | certificate bound tightness | Helped |
| Increase the amount of missing data | SDP relaxation for point set registration · Synthetic point-set registration | relaxation tightness | Hurt |
| Make the target point cloud closer to planar | Theorem 1 certificate for point set registration · Synthetic point-set registration | certificate usefulness | Hurt |
| Use a sequence with more movement, more missing data, and fewer shared points across source sets | SDP relaxation for point set registration · TUM RGB-D fr3/teddy | relaxation tightness | Hurt |
| Use Theorem 2 certification on the local-optimizer solution | Point set registration · TUM RGB-D fr1/teddy | certificate ε′: 2.0963 -> 0.1769 | Helped |
Closest papers by meaning
- Convex Global 3D Registration With Lagrangian Duality (CVPR 2017 · similarity 0.88)
- SDRSAC: Semidefinite-Based Randomized Approach for Robust Point Cloud Registration Without Correspondences (CVPR 2019 · similarity 0.87)
- Scalable and Differentiable Point-Cloud Registration Using Maximum Mean Discrepancy (ICML 2026 · similarity 0.87)
- GRICP: Granular-Ball Iterative Closest Point with Multikernel Correntropy for Point Cloud Fine Registration (AAAI 2025 · similarity 0.84)
- A robust inlier identification algorithm for point cloud registration via $\mathbf{\ell_0}$-minimization (NeurIPS 2024 · similarity 0.84)
- Correspondence-Free Non-Rigid Point Set Registration Using Unsupervised Clustering Analysis (CVPR 2024 · similarity 0.84)
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