# Perspective Phase Angle Model for Polarimetric 3D Reconstruction

arXiv: https://arxiv.org/abs/2207.09629 · HTML: https://mlsanity.com/paper/2207.09629/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 34 references that are in the map.
- First posted on arXiv 20 Jul 2022.

## Builds on (most cited first, 6 of 6)

- [Multi-Scale Geometric Consistency Guided Multi-View Stereo](https://mlsanity.com/paper/1904.08103/) (2019)
- [Shape from Polarization for Complex Scenes in the Wild](https://mlsanity.com/paper/2112.11377/) (2021)
- [Polarimetric Multi-View Inverse Rendering](https://mlsanity.com/paper/2007.08830/) (2020)
- [Linear Differential Constraints for Photo-polarimetric Height Estimation](https://mlsanity.com/paper/1708.07718/) (2017)
- [Polarimetric Monocular Dense Mapping Using Relative Deep Depth Prior](https://mlsanity.com/paper/2102.05212/) (2021)
- [Deep Snapshot HDR Reconstruction Based on the Polarization Camera](https://mlsanity.com/paper/2105.05824/) (2021)

## Built on by (most cited first, 11 of 11)

- [NeISF: Neural Incident Stokes Field for Geometry and Material Estimation](https://mlsanity.com/paper/2311.13187/) (2023)
- [PISR: Polarimetric Neural Implicit Surface Reconstruction for Textureless and Specular Objects](https://mlsanity.com/paper/2409.14331/) (2024)
- [Fresnel Microfacet BRDF: Unification of Polari-Radiometric Surface-Body Reflection](https://mlsanity.com/paper/2212.04483/) (2022)
- [Robust Depth Enhancement via Polarization Prompt Fusion Tuning](https://mlsanity.com/paper/2404.04318/) (2024)
- [Deep Polarization Cues for Single-shot Shape and Subsurface Scattering Estimation](https://mlsanity.com/paper/2407.08149/) (2024)
- [GNeRP: Gaussian-guided Neural Reconstruction of Reflective Objects with Noisy Polarization Priors](https://mlsanity.com/paper/2403.11899/) (2024)
- [PolarAnything: Diffusion-based Polarimetric Image Synthesis](https://mlsanity.com/paper/2507.17268/) (2025)
- [NeST: Neural Stress Tensor Tomography by leveraging 3D Photoelasticity](https://mlsanity.com/paper/2406.10212/) (2024)
- [PolarAPP: Beyond Polarization Demosaicking for Polarimetric Applications](https://mlsanity.com/paper/2603.23071/) (2026)
- [A Geometric Model for Polarization Imaging on Projective Cameras](https://mlsanity.com/paper/2211.16986/) (2022)
- [Revisiting Shape from Polarization in the Era of Vision Foundation Models](https://mlsanity.com/paper/2603.04817/) (2026)

## Abstract (arXiv)

Current polarimetric 3D reconstruction methods, including those in the well-established shape from polarization literature, are all developed under the orthographic projection assumption. In the case of a large field of view, however, this assumption does not hold and may result in significant reconstruction errors in methods that make this assumption. To address this problem, we present the perspective phase angle (PPA) model that is applicable to perspective cameras. Compared with the orthographic model, the proposed PPA model accurately describes the relationship between polarization phase angle and surface normal under perspective projection. In addition, the PPA model makes it possible to estimate surface normals from only one single-view phase angle map and does not suffer from the so-called π-ambiguity problem. Experiments on real data show that the PPA model is more accurate for surface normal estimation with a perspective camera than the orthographic model.