# Polarimetric Inverse Rendering for Transparent Shapes Reconstruction

arXiv: https://arxiv.org/abs/2208.11836 · HTML: https://mlsanity.com/paper/2208.11836/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 56 references that are in the map.
- First posted on arXiv 25 Aug 2022.

## Builds on (most cited first, 22 of 22)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [DeepSDF: Learning Continuous Signed Distance Functions for Shape Representation](https://mlsanity.com/paper/1901.05103/) (2019)
- [NeuS: Learning Neural Implicit Surfaces by Volume Rendering for Multi-view Reconstruction](https://mlsanity.com/paper/2106.10689/) (2021)
- [Volume Rendering of Neural Implicit Surfaces](https://mlsanity.com/paper/2106.12052/) (2021)
- [Accelerating 3D Deep Learning with PyTorch3D](https://mlsanity.com/paper/2007.08501/) (2020)
- [Implicit Geometric Regularization for Learning Shapes](https://mlsanity.com/paper/2002.10099/) (2020)
- [Differentiable Volumetric Rendering: Learning Implicit 3D Representations without 3D Supervision](https://mlsanity.com/paper/1912.07372/) (2019)
- [UNISURF: Unifying Neural Implicit Surfaces and Radiance Fields for Multi-View Reconstruction](https://mlsanity.com/paper/2104.10078/) (2021)
- [Advances in Neural Rendering](https://mlsanity.com/paper/2111.05849/) (2021)
- [ClearGrasp: 3D Shape Estimation of Transparent Objects for Manipulation](https://mlsanity.com/paper/1910.02550/) (2019)
- [Polarized Reflection Removal with Perfect Alignment in the Wild](https://mlsanity.com/paper/2003.12789/) (2020)
- [Deep Polarization Imaging for 3D shape and SVBRDF Acquisition](https://mlsanity.com/paper/2105.02875/) (2021)
- [PANDORA: Polarization-Aided Neural Decomposition Of Radiance](https://mlsanity.com/paper/2203.13458/) (2022)
- [Through the Looking Glass: Neural 3D Reconstruction of Transparent Shapes](https://mlsanity.com/paper/2004.10904/) (2020)
- [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)
- [Full 3D Reconstruction of Transparent Objects](https://mlsanity.com/paper/1805.03482/) (2018)
- [Differentiable Refraction-Tracing for Mesh Reconstruction of Transparent Objects](https://mlsanity.com/paper/2009.09144/) (2020)
- [Transparent Shape from a Single View Polarization Image](https://mlsanity.com/paper/2204.06331/) (2022)
- [Hybrid Mesh-neural Representation for 3D Transparent Object Reconstruction](https://mlsanity.com/paper/2203.12613/) (2022)
- [Prior-Guided Multi-View 3D Head Reconstruction](https://mlsanity.com/paper/2107.04277/) (2021)

## Built on by (most cited first, 8 of 8)

- [TransparentGS: Fast Inverse Rendering of Transparent Objects with Gaussians](https://mlsanity.com/paper/2504.18768/) (2025)
- [NeTO:Neural Reconstruction of Transparent Objects with Self-Occlusion Aware Refraction-Tracing](https://mlsanity.com/paper/2303.11219/) (2023)
- [ClearDepth: Enhanced Stereo Perception of Transparent Objects for Robotic Manipulation](https://mlsanity.com/paper/2409.08926/) (2024)
- [Glossy Object Reconstruction with Cost-effective Polarized Acquisition](https://mlsanity.com/paper/2504.07025/) (2025)
- [An Efficient Implicit Neural Representation Image Codec Based on Mixed Autoregressive Model for Low-Complexity Decoding](https://mlsanity.com/paper/2401.12587/) (2024)
- [From Transparent to Opaque: Rethinking Neural Implicit Surfaces with α-NeuS](https://mlsanity.com/paper/2411.05362/) (2024)
- [Transparent Fragments Contour Estimation via Visual-Tactile Fusion for Autonomous Reassembly](https://mlsanity.com/paper/2603.20290/) (2026)
- [CDPR: Cross-modal Diffusion with Polarization for Reliable Monocular Depth Estimation](https://mlsanity.com/paper/2604.11097/) (2026)

## Abstract (arXiv)

In this work, we propose a novel method for the detailed reconstruction of transparent objects by exploiting polarimetric cues. Most of the existing methods usually lack sufficient constraints and suffer from the over-smooth problem. Hence, we introduce polarization information as a complementary cue. We implicitly represent the object's geometry as a neural network, while the polarization render is capable of rendering the object's polarization images from the given shape and illumination configuration. Direct comparison of the rendered polarization images to the real-world captured images will have additional errors due to the transmission in the transparent object. To address this issue, the concept of reflection percentage which represents the proportion of the reflection component is introduced. The reflection percentage is calculated by a ray tracer and then used for weighting the polarization loss. We build a polarization dataset for multi-view transparent shapes reconstruction to verify our method. The experimental results show that our method is capable of recovering detailed shapes and improving the reconstruction quality of transparent objects. Our dataset and code will be publicly available at https://github.com/shaomq2187/TransPIR.