# Physics-informed Active Polarimetric 3D Imaging for Specular Surfaces

arXiv: https://arxiv.org/abs/2602.19470 · HTML: https://mlsanity.com/paper/2602.19470/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 23 references that are in the map.
- First posted on arXiv 23 Feb 2026.

## Builds on (most cited first, 7 of 7)

- [FiLM: Visual Reasoning with a General Conditioning Layer](https://mlsanity.com/paper/1709.07871/) (2017)
- [Shape from Polarization for Complex Scenes in the Wild](https://mlsanity.com/paper/2112.11377/) (2021)
- [Event-based Shape from Polarization](https://mlsanity.com/paper/2301.06855/) (2023)
- [Accurate Eye Tracking from Dense 3D Surface Reconstructions using Single-Shot Deflectometry](https://mlsanity.com/paper/2308.07298/) (2023)
- [Deflectometry for specular surfaces: an overview](https://mlsanity.com/paper/2204.11592/) (2022)
- [3D Imaging of Complex Specular Surfaces by Fusing Polarimetric and Deflectometric Information](https://mlsanity.com/paper/2406.01994/) (2024)
- [Event-based Motion-Robust Accurate Shape Estimation for Mixed Reflectance Scenes](https://mlsanity.com/paper/2311.09652/) (2023)

## Abstract (arXiv)

3D imaging of specular surfaces remains challenging in real-world scenarios, such as in-line inspection or hand-held scanning, requiring fast and accurate measurement of complex geometries. Optical metrology techniques such as deflectometry achieve high accuracy but typically rely on multi-shot acquisition, making them unsuitable for dynamic environments. Fourier-based single-shot approaches alleviate this constraint, yet their performance deteriorates when measuring surfaces with high spatial frequency structure or large curvature. Alternatively, polarimetric 3D imaging in computer vision operates in a single-shot fashion and exhibits robustness to geometric complexity. However, its accuracy is fundamentally limited by the orthographic imaging assumption. In this paper, we propose a physics-informed deep learning framework for single-shot 3D imaging of complex specular surfaces. Polarization cues provide orientation priors that assist in interpreting geometric information encoded by structured illumination. These complementary cues are processed through a dual-encoder architecture with mutual feature modulation, allowing the network to resolve their nonlinear coupling and directly infer surface normals. The proposed method achieves accurate and robust normal estimation in single-shot with fast inference, enabling practical 3D imaging of complex specular surfaces.