# Shape from Mixed Polarization

arXiv: https://arxiv.org/abs/1605.02066 · HTML: https://mlsanity.com/paper/1605.02066/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 May 2016.

## Built on by (most cited first, 3 of 3)

- [Physics vs. Learned Priors: Rethinking Camera and Algorithm Design for Task-Specific Imaging](https://mlsanity.com/paper/2204.09871/) (2022)
- [SS-SfP:Neural Inverse Rendering for Self Supervised Shape from (Mixed) Polarization](https://mlsanity.com/paper/2407.09294/) (2024)
- [A Geometric Model for Polarization Imaging on Projective Cameras](https://mlsanity.com/paper/2211.16986/) (2022)

## Abstract (arXiv)

Shape from Polarization (SfP) estimates surface normals using photos captured at different polarizer rotations. Fundamentally, the SfP model assumes that light is reflected either diffusely or specularly. However, this model is not valid for many real-world surfaces exhibiting a mixture of diffuse and specular properties. To address this challenge, previous methods have used a sequential solution: first, use an existing algorithm to separate the scene into diffuse and specular components, then apply the appropriate SfP model. In this paper, we propose a new method that jointly uses viewpoint and polarization data to holistically separate diffuse and specular components, recover refractive index, and ultimately recover 3D shape. By involving the physics of polarization in the separation process, we demonstrate competitive results with a benchmark method, while recovering additional information (e.g. refractive index).