# Event-based Shape from Polarization

arXiv: https://arxiv.org/abs/2301.06855 · HTML: https://mlsanity.com/paper/2301.06855/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 55 references that are in the map.
- First posted on arXiv 17 Jan 2023.

## Builds on (most cited first, 12 of 12)

- [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)
- [Google Scanned Objects: A High-Quality Dataset of 3D Scanned Household Items](https://mlsanity.com/paper/2204.11918/) (2022)
- [High Speed and High Dynamic Range Video with an Event Camera](https://mlsanity.com/paper/1906.07165/) (2019)
- [E-RAFT: Dense Optical Flow from Event Cameras](https://mlsanity.com/paper/2108.10552/) (2021)
- [Time Lens++: Event-based Frame Interpolation with Parametric Non-linear Flow and Multi-scale Fusion](https://mlsanity.com/paper/2203.17191/) (2022)
- [Learning Monocular Dense Depth from Events](https://mlsanity.com/paper/2010.08350/) (2020)
- [Polarized Reflection Removal with Perfect Alignment in the Wild](https://mlsanity.com/paper/2003.12789/) (2020)
- [Shape from Polarization for Complex Scenes in the Wild](https://mlsanity.com/paper/2112.11377/) (2021)
- [Depth from a polarisation + RGB stereo pair](https://mlsanity.com/paper/1903.12061/) (2019)
- [ESL: Event-based Structured Light](https://mlsanity.com/paper/2111.15510/) (2021)
- [Event Guided Depth Sensing](https://mlsanity.com/paper/2110.10505/) (2021)

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

- [Recent Event Camera Innovations: A Survey](https://mlsanity.com/paper/2408.13627/) (2024)
- [Microsaccade-inspired Event Camera for Robotics](https://mlsanity.com/paper/2405.17769/) (2024)
- [Event Quality Score (EQS): Assessing the Realism of Simulated Event Camera Streams via Distances in Latent Space](https://mlsanity.com/paper/2504.12515/) (2025)
- [Robust Depth Enhancement via Polarization Prompt Fusion Tuning](https://mlsanity.com/paper/2404.04318/) (2024)
- [Event Camera Meets Mobile Embodied Perception: Abstraction, Algorithm, Acceleration, Application](https://mlsanity.com/paper/2503.22943/) (2025)
- [Event Ellipsometer: Event-based Mueller-Matrix Video Imaging](https://mlsanity.com/paper/2411.17313/) (2024)
- [3D Imaging of Complex Specular Surfaces by Fusing Polarimetric and Deflectometric Information](https://mlsanity.com/paper/2406.01994/) (2024)
- [SS-SfP:Neural Inverse Rendering for Self Supervised Shape from (Mixed) Polarization](https://mlsanity.com/paper/2407.09294/) (2024)
- [Event-based Motion-Robust Accurate Shape Estimation for Mixed Reflectance Scenes](https://mlsanity.com/paper/2311.09652/) (2023)
- [Neural Ganglion Sensors: Learning Task-specific Event Cameras Inspired by the Neural Circuit of the Human Retina](https://mlsanity.com/paper/2504.13457/) (2025)
- [Learning to Remove Lens Flare in Event Camera](https://mlsanity.com/paper/2512.09016/) (2025)
- [Event-based Shape from Polarization with Spiking Neural Networks](https://mlsanity.com/paper/2312.16071/) (2023)
- [Probabilistic Online Event Downsampling](https://mlsanity.com/paper/2506.02547/) (2025)
- [PS-EIP: Robust Photometric Stereo Based on Event Interval Profile](https://mlsanity.com/paper/2503.18341/) (2025)
- [Event-based high temporal resolution measurement of shock wave motion field](https://mlsanity.com/paper/2512.22474/) (2025)
- [Physics-informed Active Polarimetric 3D Imaging for Specular Surfaces](https://mlsanity.com/paper/2602.19470/) (2026)

## Abstract (arXiv)

State-of-the-art solutions for Shape-from-Polarization (SfP) suffer from a speed-resolution tradeoff: they either sacrifice the number of polarization angles measured or necessitate lengthy acquisition times due to framerate constraints, thus compromising either accuracy or latency. We tackle this tradeoff using event cameras. Event cameras operate at microseconds resolution with negligible motion blur, and output a continuous stream of events that precisely measures how light changes over time asynchronously. We propose a setup that consists of a linear polarizer rotating at high-speeds in front of an event camera. Our method uses the continuous event stream caused by the rotation to reconstruct relative intensities at multiple polarizer angles. Experiments demonstrate that our method outperforms physics-based baselines using frames, reducing the MAE by 25% in synthetic and real-world dataset. In the real world, we observe, however, that the challenging conditions (i.e., when few events are generated) harm the performance of physics-based solutions. To overcome this, we propose a learning-based approach that learns to estimate surface normals even at low event-rates, improving the physics-based approach by 52% on the real world dataset. The proposed system achieves an acquisition speed equivalent to 50 fps (>twice the framerate of the commercial polarization sensor) while retaining the spatial resolution of 1MP. Our evaluation is based on the first large-scale dataset for event-based SfP