Research map / Point clouds / Registration and matching
Non-Rigid Point Set Registration with Robust Transformation Estimation under Manifold Regularization
Read the paperCheck your idea against 108,967 papers
AI-extracted summary. Written by AI from the paper's title only (no summary yet). Numbers and claims can be wrong or missing context, so check the paper before you cite or build on it.
Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| add manifold regularization term on top of VFC-style robust transformation estimation | MR-RPM · 2D fish and Chinese character registration (deformation, noise, occlusion) | registration error | Helped |
| MR-RPM vs VFC under large noise levels | MR-RPM · 2D shapes, noise | registration error | Mixed |
| manifold regularization (MR-RPM vs VFC) on 3D point clouds | MR-RPM · 3D wolf point cloud pair 2 | average registration error: 1.01 -> 0.53 | Helped |
| manifold regularization (MR-RPM vs VFC) on 3D point clouds | MR-RPM · 3D wolf point cloud pair 1 | average registration error: 1.15 -> 0.78 | Helped |
Closest papers by meaning
- Correspondence-Free Non-Rigid Point Set Registration Using Unsupervised Clustering Analysis (CVPR 2024 · similarity 0.90)
- A robust inlier identification algorithm for point cloud registration via $\mathbf{\ell_0}$-minimization (NeurIPS 2024 · similarity 0.86)
- Scalable 3D Registration via Truncated Entry-wise Absolute Residuals (CVPR 2024 · similarity 0.86)
- Locality Preserving Refinement for Shape Matching with Functional Maps (AAAI 2024 · similarity 0.86)
- Dual Manifold Regularization Steered Robust Representation Learning for Point Cloud Analysis (AAAI 2025 · similarity 0.85)
- Progressive Correspondence Regenerator for Robust 3D Registration (CVPR 2025 · similarity 0.85)
Writing a paper in this area? scime draft is a free LaTeX editor with AI included, real-time co-authors and a check of your references and venue rules before you submit. It also imports OpenAI Prism projects.