Poppy: Polarization-based Plug-and-Play Guidance for Enhancing Monocular Normal Estimation
Monocular surface normal estimators trained on large-scale RGB-normal data often perform poorly in the edge cases of reflective, textureless, and dark surfaces. Polarization encodes surface orientation independently of texture and albedo, offering a physics-based complement for these cases. Existing polarization methods, however, require multi-view capture or specialized training data, limiting generalization. We introduce Poppy, a training-free framework that refines normals from any frozen RGB backbone using single-shot polarization measurements at test time. Keeping backbone weights frozen, Poppy optimizes per-pixel offsets to the input RGB and output normal along with a learned reflec...
| Authors | Irene Kim, Sai Tanmay Reddy Chakkera, Alexandros Graikos et al. |
|---|---|
| Primary category | cs.CV |
| Published | 29 March 2026 |
| Last updated at source | 29 March 2026 |
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