Poppy: Polarization-based Plug-and-Play Guidance for Enhancing Monocular Normal Estimation

• URL: http://arxiv.org/abs/2603.27891v1
• Date: Sun, 29 Mar 2026 22:05:09 GMT
• Title: Poppy: Polarization-based Plug-and-Play Guidance for Enhancing Monocular Normal Estimation
• Abstract summary: Poppy is a training-free framework that refines normals from any frozen RGB backbone using single-shot polarization measurements at test time.<n>A differentiable rendering layer converts the refined normals into polarization predictions and penalizes mismatches with the observed signal.<n>These results show that guiding learned RGB-based normal estimators with polarization cues at test time refines normals on challenging surfaces without retraining.
• Score: 69.74622862203036
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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 reflectance decomposition. A differentiable rendering layer converts the refined normals into polarization predictions and penalizes mismatches with the observed signal. Across seven benchmarks and three backbone architectures (diffusion, flow, and feed-forward), Poppy reduces mean angular error by 23-26% on synthetic data and 6-16% on real data. These results show that guiding learned RGB-based normal estimators with polarization cues at test time refines normals on challenging surfaces without retraining.

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