# Depth from a polarisation + RGB stereo pair

arXiv: https://arxiv.org/abs/1903.12061 · HTML: https://mlsanity.com/paper/1903.12061/

## Key facts

- Cited by 29 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Mar 2019.

## Built on by (most cited first, 29 of 29)

- [Shape from Polarization for Complex Scenes in the Wild](https://mlsanity.com/paper/2112.11377/) (2021)
- [On the Importance of Accurate Geometry Data for Dense 3D Vision Tasks](https://mlsanity.com/paper/2303.14840/) (2023)
- [Is my Depth Ground-Truth Good Enough? HAMMER -- Highly Accurate Multi-Modal Dataset for DEnse 3D Scene Regression](https://mlsanity.com/paper/2205.04565/) (2022)
- [Polarimetric Pose Prediction](https://mlsanity.com/paper/2112.03810/) (2021)
- [Event-based Shape from Polarization](https://mlsanity.com/paper/2301.06855/) (2023)
- [Multi-View Azimuth Stereo via Tangent Space Consistency](https://mlsanity.com/paper/2303.16447/) (2023)
- [Polarization Guided Specular Reflection Separation](https://mlsanity.com/paper/2103.11652/) (2021)
- [CroMo: Cross-Modal Learning for Monocular Depth Estimation](https://mlsanity.com/paper/2203.12485/) (2022)
- [Simultaneous Acquisition of High Quality RGB Image and Polarization Information using a Sparse Polarization Sensor](https://mlsanity.com/paper/2209.13106/) (2022)
- [Polarimetric Monocular Dense Mapping Using Relative Deep Depth Prior](https://mlsanity.com/paper/2102.05212/) (2021)
- [Sparse Ellipsometry: Portable Acquisition of Polarimetric SVBRDF and Shape with Unstructured Flash Photography](https://mlsanity.com/paper/2207.04236/) (2022)
- [Polarization Wavefront Lidar: Learning Large Scene Reconstruction from Polarized Wavefronts](https://mlsanity.com/paper/2406.03461/) (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)
- [Learning to Remove Wrinkled Transparent Film with Polarized Prior](https://mlsanity.com/paper/2403.04368/) (2024)
- [SS-SfP:Neural Inverse Rendering for Self Supervised Shape from (Mixed) Polarization](https://mlsanity.com/paper/2407.09294/) (2024)
- [Polarimetric BSSRDF Acquisition of Dynamic Faces](https://mlsanity.com/paper/2501.01980/) (2024)
- [Benchmarking Burst Super-Resolution for Polarization Images: Noise Dataset and Analysis](https://mlsanity.com/paper/2503.18705/) (2025)
- [S2P3: Self-Supervised Polarimetric Pose Prediction](https://mlsanity.com/paper/2312.01105/) (2023)
- [Physically-admissible polarimetric data augmentation for road-scene analysis](https://mlsanity.com/paper/2206.07431/) (2022)
- [Polarized Color Image Denoising using Pocoformer](https://mlsanity.com/paper/2207.00215/) (2022)
- [Pola4All: survey of polarimetric applications and an open-source toolkit to analyze polarization](https://mlsanity.com/paper/2312.14697/) (2023)
- [Event-based Shape from Polarization with Spiking Neural Networks](https://mlsanity.com/paper/2312.16071/) (2023)
- [High Resolution Surface Reconstruction of Cultural Heritage Objects Using Shape from Polarization Method](https://mlsanity.com/paper/2406.15121/) (2024)
- [Polarimetric Multi-View Inverse Rendering](https://mlsanity.com/paper/2212.12721/) (2022)
- [Polarimetric Information for Multi-Modal 6D Pose Estimation of Photometrically Challenging Objects with Limited Data](https://mlsanity.com/paper/2308.10627/) (2023)
- [Segmentation-Driven Monocular Shape from Polarization based on Physical Model](https://mlsanity.com/paper/2601.04776/) (2026)
- [CDPR: Cross-modal Diffusion with Polarization for Reliable Monocular Depth Estimation](https://mlsanity.com/paper/2604.11097/) (2026)

## Abstract (arXiv)

In this paper, we propose a hybrid depth imaging system in which a polarisation camera is augmented by a second image from a standard digital camera. For this modest increase in equipment complexity over conventional shape-from-polarisation, we obtain a number of benefits that enable us to overcome longstanding problems with the polarisation shape cue. The stereo cue provides a depth map which, although coarse, is metrically accurate. This is used as a guide surface for disambiguation of the polarisation surface normal estimates using a higher order graphical model. In turn, these are used to estimate diffuse albedo. By extending a previous shape-from-polarisation method to the perspective case, we show how to compute dense, detailed maps of absolute depth, while retaining a linear formulation. We show that our hybrid method is able to recover dense 3D geometry that is superior to state-of-the-art shape-from-polarisation or two view stereo alone.