Research map / Learning theory and algorithms / Prediction and decision losses
Group-Wise Point-Set Registration Based on Renyi's Second Order Entropy
Check 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 |
|---|---|---|---|
| Normalize the information-potential cost by the square root of total variance (ablation) | Information-potential group-wise registration · CC7 | KS statistic: 0.0503 -> 0.0317 | Helped |
| Normalize the information-potential cost by the square root of total variance (ablation) | Information-potential group-wise registration · CC7(+out) | KS statistic: 0.0539 -> 0.0405 | Helped |
| Normalize the information-potential cost by the square root of total variance (ablation) | Information-potential group-wise registration · CC7(-out) | KS statistic: 0.0529 -> 0.0317 | Helped |
| Normalize the information-potential cost by the square root of total variance (ablation) | Information-potential group-wise registration · Olympic logo | KS statistic: 0.0177 -> 0.0177 | No clear effect |
| Normalize the information-potential cost by the square root of total variance (ablation) | Information-potential group-wise registration · Noisy fish | KS statistic: 0.0383 -> 0.0383 | No clear effect |
| Use the information-potential objective instead of CDF-HC | Group-wise point-set registration · CC7 | runtime: 252s -> 30s | Helped |
| Use the information-potential objective instead of CDF-HC | Group-wise point-set registration · CC7(+out) | runtime: 270s -> 30s | Helped |
| Use the information-potential objective instead of CDF-HC | Group-wise point-set registration · Olympic logo | runtime: 746s -> 57s | Helped |
| Use the information-potential objective instead of CDF-HC | Group-wise point-set registration · Noisy fish | runtime: 1946s -> 111s | Helped |
| Use normalized information potential instead of CDF-HC | Group-wise point-set registration · Olympic logo | KS statistic: 0.0295 -> 0.0177 | Helped |
| Use normalized information potential instead of CDF-HC | Group-wise point-set registration · Noisy fish | KS statistic: 0.0462 -> 0.0383 | Helped |
| Use normalized information potential instead of CDF-JS | Group-wise point-set registration · Olympic logo | KS statistic: 0.1103 -> 0.0177 | Helped |
Closest papers by meaning
- Correspondence-Free Non-Rigid Point Set Registration Using Unsupervised Clustering Analysis (CVPR 2024 · similarity 0.87)
- SDRSAC: Semidefinite-Based Randomized Approach for Robust Point Cloud Registration Without Correspondences (CVPR 2019 · similarity 0.87)
- Scalable 3D Registration via Truncated Entry-wise Absolute Residuals (CVPR 2024 · similarity 0.85)
- PointMC: Multi-instance Point Cloud Registration based on Maximal Cliques (ICML 2024 · similarity 0.84)
- A robust inlier identification algorithm for point cloud registration via $\mathbf{\ell_0}$-minimization (NeurIPS 2024 · similarity 0.84)
- GRICP: Granular-Ball Iterative Closest Point with Multikernel Correntropy for Point Cloud Fine Registration (AAAI 2025 · similarity 0.84)
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.