Context without Commitment: Robust Dense Correspondence under Non-Rigid Deformation

arXiv:2609.37071 · cs.CV · Submitted 2026-09-29 · Read on arXiv

cs.CV

Submitted: 2026-09-29

Updated: 2026-09-29

Terminology

• Conference on Computer Vision and Pattern Recognition — 6× in this paper
• Point Clouds — 6× in this paper · explained in 2 episodes
• Consistency — 4× in this paper · explained in 3 episodes
• Transformer — 4× in this paper · explained in 2 episodes
• Digital Twin — 2× in this paper · explained in 5 episodes
• Mannheim Institute for Intelligent Systems in Medicine — 2× in this paper
• Representation Learning — 2× in this paper · explained in 2 episodes
• Robustness — 2× in this paper · explained in 5 episodes
• Accuracy — 1× in this paper · explained in 1 episode
• Benchmark — 1× in this paper · explained in 2 episodes
• Optimal Transport — 1× in this paper · explained in 5 episodes
• Pruning — 1× in this paper · explained in 4 episodes
• Self-Supervised Learning — 1× in this paper · explained in 8 episodes

Sources

• SynBench: A Synthetic Benchmark for Non-rigid 3D Point Cloud Registration
• DINE: Distance Is Not Enough -- Learning Global Deformation Priors for Robust Soft-Tissue Point Cloud Registration

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