Research map / Point clouds / Point cloud classification
Recognizing Rigid Patterns of Unlabeled Point Clouds by Complete and Continuous Isometry Invariants With No False Negatives and No False Positives
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 |
|---|---|---|---|
| Use the Simplexwise Centered Distribution (SCD) with oriented relative distances weighted by simplex strength | Unlabeled point clouds in Euclidean space · Isometry recognition | completeness and continuity | Helped |
| Weight orientation signs by a continuous simplex-strength function that vanishes for degenerate simplices | Oriented centered distributions · Euclidean point clouds | continuity | Helped |
Closest papers by meaning
- Complete Neural Networks for Complete Euclidean Graphs (AAAI 2024 · similarity 0.84)
- An in depth look at the Procrustes-Wasserstein distance: properties and barycenters (ICML 2025 · similarity 0.84)
- Occlusion-aware Non-Rigid Point Cloud Registration via Unsupervised Neural Deformation Correntropy (ICLR 2025 · similarity 0.83)
- Consistent Normal Orientation for 3D Point Clouds via Least Squares on Delaunay Graph (CVPR 2025 · similarity 0.83)
- Unsupervised Template-assisted Point Cloud Shape Correspondence Network (CVPR 2024 · similarity 0.83)
- DV-Matcher: Deformation-based Non-rigid Point Cloud Matching Guided by Pre-trained Visual Features (CVPR 2025 · similarity 0.83)
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.