# Transparent Shape from a Single View Polarization Image

arXiv: https://arxiv.org/abs/2204.06331 · HTML: https://mlsanity.com/paper/2204.06331/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 35 references that are in the map.
- First posted on arXiv 13 Apr 2022.

## Builds on (most cited first, 16 of 16)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift](https://mlsanity.com/paper/1502.03167/) (2015)
- [DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs](https://mlsanity.com/paper/1606.00915/) (2016)
- [Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation](https://mlsanity.com/paper/1802.02611/) (2018)
- [ClearGrasp: 3D Shape Estimation of Transparent Objects for Manipulation](https://mlsanity.com/paper/1910.02550/) (2019)
- [RGB-D Local Implicit Function for Depth Completion of Transparent Objects](https://mlsanity.com/paper/2104.00622/) (2021)
- [Multi-View Photometric Stereo: A Robust Solution and Benchmark Dataset for Spatially Varying Isotropic Materials](https://mlsanity.com/paper/2001.06659/) (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)
- [PS-FCN: A Flexible Learning Framework for Photometric Stereo](https://mlsanity.com/paper/1807.08696/) (2018)
- [Through the Looking Glass: Neural 3D Reconstruction of Transparent Shapes](https://mlsanity.com/paper/2004.10904/) (2020)
- [Full 3D Reconstruction of Transparent Objects](https://mlsanity.com/paper/1805.03482/) (2018)
- [EPSANet: An Efficient Pyramid Squeeze Attention Block on Convolutional Neural Network](https://mlsanity.com/paper/2105.14447/) (2021)
- [Human Pose and Shape Estimation from Single Polarization Images](https://mlsanity.com/paper/2108.06834/) (2021)

## Built on by (most cited first, 16 of 16)

- [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)
- [NeISF++: Neural Incident Stokes Field for Polarized Inverse Rendering of Conductors and Dielectrics](https://mlsanity.com/paper/2411.10189/) (2024)
- [Polarimetric Inverse Rendering for Transparent Shapes Reconstruction](https://mlsanity.com/paper/2208.11836/) (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)
- [Shape from Polarization of Thermal Emission and Reflection](https://mlsanity.com/paper/2506.18217/) (2025)
- [TransNormal: Dense Visual Semantics for Diffusion-based Transparent Object Normal Estimation](https://mlsanity.com/paper/2602.00839/) (2026)
- [MuS-Polar3D: A Benchmark Dataset for Computational Polarimetric 3D Imaging under Multi-Scattering Conditions](https://mlsanity.com/paper/2512.21513/) (2025)
- [PolarAPP: Beyond Polarization Demosaicking for Polarimetric Applications](https://mlsanity.com/paper/2603.23071/) (2026)
- [PolarVLM: Bridging the Semantic-Physical Gap in Vision-Language Models](https://mlsanity.com/paper/2605.07574/) (2026)
- [UD-SfPNet: An Underwater Descattering Shape-from-Polarization Network for 3D Normal Reconstruction](https://mlsanity.com/paper/2603.00908/) (2026)
- [Revisiting Shape from Polarization in the Era of Vision Foundation Models](https://mlsanity.com/paper/2603.04817/) (2026)
- [CDPR: Cross-modal Diffusion with Polarization for Reliable Monocular Depth Estimation](https://mlsanity.com/paper/2604.11097/) (2026)
- [Structure-Aware Consistency Priors for Shape from Polarization in Complex Media](https://mlsanity.com/paper/2606.00509/) (2026)
- [TransNormal-2: Geometry-Grounded Rectified Flow with Edge-Aware Decoding for Precise Normal Estimation](https://mlsanity.com/paper/2609.06665/) (2026)

## Abstract (arXiv)

This paper presents a learning-based method for transparent surface estimation from a single view polarization image. Existing shape from polarization(SfP) methods have the difficulty in estimating transparent shape since the inherent transmission interference heavily reduces the reliability of physics-based prior. To address this challenge, we propose the concept of physics-based prior, which is inspired by the characteristic that the transmission component in the polarization image has more noise than reflection. The confidence is used to determine the contribution of the interfered physics-based prior. Then, we build a network(TransSfP) with multi-branch architecture to avoid the destruction of relationships between different hierarchical inputs. To train and test our method, we construct a dataset for transparent shape from polarization with paired polarization images and ground-truth normal maps. Extensive experiments and comparisons demonstrate the superior accuracy of our method.