Geometry-Grounded Articulation and Test-Time Adaptation for Zero-Shot, Scalable Shape Matching in the Wild
Dense correspondence on in-the-wild 3D scans must handle severe non-isometric deformation, partial observations, topology artifacts, irregular discretization, and extreme resolution. Together, these conditions make shape matching both a robustness and scalability problem. Existing methods typically handle only part of this regime and often require expensive spectral or dense feature processing, repeated pairwise optimization, or dataset-specific training. To address the above issues, we present GAMMA, a zero-shot framework based on Geometry-Grounded Articulation. For each scan, GAMMA renders multiple views and uses a pretrained estimator with a category-specific parametric model to produc...
| Authors | Qilong Liu, Qinfeng Xiao, Chenyuan Yi et al. |
|---|---|
| Primary category | cs.CV |
| Published | 28 June 2026 |
| Last updated at source | 18 September 2026 |
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