# Deep Polarization Cues for Single-shot Shape and Subsurface Scattering Estimation

arXiv: https://arxiv.org/abs/2407.08149 · HTML: https://mlsanity.com/paper/2407.08149/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 33 of its 79 references that are in the map.
- First posted on arXiv 11 Jul 2024.

## Builds on (most cited first, 30 of 33)

- [ShapeNet: An Information-Rich 3D Model Repository](https://mlsanity.com/paper/1512.03012/) (2015)
- [Towards Robust Monocular Depth Estimation: Mixing Datasets for Zero-shot Cross-dataset Transfer](https://mlsanity.com/paper/1907.01341/) (2019)
- [Deep Ordinal Regression Network for Monocular Depth Estimation](https://mlsanity.com/paper/1806.02446/) (2018)
- [Inverse Rendering for Complex Indoor Scenes: Shape, Spatially-Varying Lighting and SVBRDF from a Single Image](https://mlsanity.com/paper/1905.02722/) (2019)
- [Learning to Predict Indoor Illumination from a Single Image](https://mlsanity.com/paper/1704.00090/) (2017)
- [Single-Image SVBRDF Capture with a Rendering-Aware Deep Network](https://mlsanity.com/paper/1810.09718/) (2018)
- [Neural Inverse Rendering of an Indoor Scene from a Single Image](https://mlsanity.com/paper/1901.02453/) (2019)
- [Learning Indoor Inverse Rendering with 3D Spatially-Varying Lighting](https://mlsanity.com/paper/2109.06061/) (2021)
- [Polarized Reflection Removal with Perfect Alignment in the Wild](https://mlsanity.com/paper/2003.12789/) (2020)
- [Materials for Masses: SVBRDF Acquisition with a Single Mobile Phone Image](https://mlsanity.com/paper/1804.05790/) (2018)
- [IRISformer: Dense Vision Transformers for Single-Image Inverse Rendering in Indoor Scenes](https://mlsanity.com/paper/2206.08423/) (2022)
- [Two-shot Spatially-varying BRDF and Shape Estimation](https://mlsanity.com/paper/2004.00403/) (2020)
- [Deep Polarization Imaging for 3D shape and SVBRDF Acquisition](https://mlsanity.com/paper/2105.02875/) (2021)
- [Shape from Polarization for Complex Scenes in the Wild](https://mlsanity.com/paper/2112.11377/) (2021)
- [PANDORA: Polarization-Aided Neural Decomposition Of Radiance](https://mlsanity.com/paper/2203.13458/) (2022)
- [Modeling Surface Appearance from a Single Photograph using Self-augmented Convolutional Neural Networks](https://mlsanity.com/paper/1809.00886/) (2018)
- [Deep attention-based classification network for robust depth prediction](https://mlsanity.com/paper/1807.03959/) (2018)
- [Shape and Material Capture at Home](https://mlsanity.com/paper/2104.06397/) (2021)
- [LIME: Live Intrinsic Material Estimation](https://mlsanity.com/paper/1801.01075/) (2018)
- [Depth from a polarisation + RGB stereo pair](https://mlsanity.com/paper/1903.12061/) (2019)
- [Polarimetric Multi-View Inverse Rendering](https://mlsanity.com/paper/2007.08830/) (2020)
- [3D Human Shape Reconstruction from a Polarization Image](https://mlsanity.com/paper/2007.09268/) (2020)
- [De-rendering the World's Revolutionary Artefacts](https://mlsanity.com/paper/2104.03954/) (2021)
- [NeISF: Neural Incident Stokes Field for Geometry and Material Estimation](https://mlsanity.com/paper/2311.13187/) (2023)
- [Polarimetric Pose Prediction](https://mlsanity.com/paper/2112.03810/) (2021)
- [Transparent Shape from a Single View Polarization Image](https://mlsanity.com/paper/2204.06331/) (2022)
- [Human Pose and Shape Estimation from Single Polarization Images](https://mlsanity.com/paper/2108.06834/) (2021)
- [Neural Relightable Participating Media Rendering](https://mlsanity.com/paper/2110.12993/) (2021)
- [All-photon Polarimetric Time-of-Flight Imaging](https://mlsanity.com/paper/2112.09278/) (2021)
- [Perspective Phase Angle Model for Polarimetric 3D Reconstruction](https://mlsanity.com/paper/2207.09629/) (2022)

## Built on by (most cited first, 6 of 6)

- [NeISF++: Neural Incident Stokes Field for Polarized Inverse Rendering of Conductors and Dielectrics](https://mlsanity.com/paper/2411.10189/) (2024)
- [PolGS: Polarimetric Gaussian Splatting for Fast Reflective Surface Reconstruction](https://mlsanity.com/paper/2509.19726/) (2025)
- [TensoIS: A Step Towards Feed-Forward Tensorial Inverse Subsurface Scattering for Perlin Distributed Heterogeneous Media](https://mlsanity.com/paper/2509.04047/) (2025)
- [Revisiting Shape from Polarization in the Era of Vision Foundation Models](https://mlsanity.com/paper/2603.04817/) (2026)
- [PolGS++: Physically-Guided Polarimetric Gaussian Splatting for Fast Reflective Surface Reconstruction](https://mlsanity.com/paper/2603.10801/) (2026)
- [Structure-Aware Consistency Priors for Shape from Polarization in Complex Media](https://mlsanity.com/paper/2606.00509/) (2026)

## Abstract (arXiv)

In this work, we propose a novel learning-based method to jointly estimate the shape and subsurface scattering (SSS) parameters of translucent objects by utilizing polarization cues. Although polarization cues have been used in various applications, such as shape from polarization (SfP), BRDF estimation, and reflection removal, their application in SSS estimation has not yet been explored. Our observations indicate that the SSS affects not only the light intensity but also the polarization signal. Hence, the polarization signal can provide additional cues for SSS estimation. We also introduce the first large-scale synthetic dataset of polarized translucent objects for training our model. Our method outperforms several baselines from the SfP and inverse rendering realms on both synthetic and real data, as demonstrated by qualitative and quantitative results.