Perspective Phase Angle Model for Polarimetric 3D Reconstruction
Not on the map. Why?
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.
Key facts. Cited by 11 papers in the map (data through 7 Oct 2026). Builds on 6 of its 34 references that are in the map. First posted on arXiv 20 Jul 2022.
Builds on
6 of 34 references.
References · 6
- 2019Multi-Scale Geometric Consistency Guided Multi-View Stereocited by 105
- 2021Shape from Polarization for Complex Scenes in the Wildcited by 53
- 2020Polarimetric Multi-View Inverse Renderingcited by 28
- 2017Linear Differential Constraints for Photo-polarimetric Height Estimationcited by 9
- 2021Polarimetric Monocular Dense Mapping Using Relative Deep Depth Priorcited by 8
- 2021Deep Snapshot HDR Reconstruction Based on the Polarization Cameracited by 7
Built on by
Citing papers per year: 2022: 2; 2023: 1; 2024: 5; 2025: 1; 2026: 2.
11 papers.
- 2023NeISF: Neural Incident Stokes Field for Geometry and Material Estimationcited by 25
- 2024PISR: Polarimetric Neural Implicit Surface Reconstruction for Textureless and Specular Objectscited by 15
- 2022Fresnel Microfacet BRDF: Unification of Polari-Radiometric Surface-Body Reflectioncited by 6
- 2024Robust Depth Enhancement via Polarization Prompt Fusion Tuningcited by 6
- 2024Deep Polarization Cues for Single-shot Shape and Subsurface Scattering Estimationcited by 6
- 2024GNeRP: Gaussian-guided Neural Reconstruction of Reflective Objects with Noisy Polarization Priorscited by 5
- 2025PolarAnything: Diffusion-based Polarimetric Image Synthesiscited by 5
- 2024NeST: Neural Stress Tensor Tomography by leveraging 3D Photoelasticitycited by 2
- 2026PolarAPP: Beyond Polarization Demosaicking for Polarimetric Applicationscited by 1
- 2022A Geometric Model for Polarization Imaging on Projective Camerascited by 0
- 2026Revisiting Shape from Polarization in the Era of Vision Foundation Modelscited by 0
- 2026PolarAPP: Beyond Polarization Demosaicking for Polarimetric Applications24 Mar 2026
- 2026Revisiting Shape from Polarization in the Era of Vision Foundation Models5 Mar 2026
- 2025PolarAnything: Diffusion-based Polarimetric Image Synthesis23 Jul 2025
- 2024PISR: Polarimetric Neural Implicit Surface Reconstruction for Textureless and Specular Objects22 Sep 2024
- 2024Deep Polarization Cues for Single-shot Shape and Subsurface Scattering Estimation11 Jul 2024
- 2024NeST: Neural Stress Tensor Tomography by leveraging 3D Photoelasticity14 Jun 2024
- 2024Robust Depth Enhancement via Polarization Prompt Fusion Tuning5 Apr 2024
- 2024GNeRP: Gaussian-guided Neural Reconstruction of Reflective Objects with Noisy Polarization Priors18 Mar 2024
- 2023NeISF: Neural Incident Stokes Field for Geometry and Material Estimation22 Nov 2023
- 2022Fresnel Microfacet BRDF: Unification of Polari-Radiometric Surface-Body Reflection8 Dec 2022
- 2022A Geometric Model for Polarization Imaging on Projective Cameras29 Nov 2022