# Event-Based De-Snowing for Autonomous Driving

arXiv: https://arxiv.org/abs/2507.20901 · HTML: https://mlsanity.com/paper/2507.20901/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 54 references that are in the map.
- First posted on arXiv 25 Jul 2025.

## Builds on (most cited first, 21 of 21)

- [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)
- [RAFT: Recurrent All-Pairs Field Transforms for Optical Flow](https://mlsanity.com/paper/2003.12039/) (2020)
- [Vision Transformers for Dense Prediction](https://mlsanity.com/paper/2103.13413/) (2021)
- [Restormer: Efficient Transformer for High-Resolution Image Restoration](https://mlsanity.com/paper/2111.09881/) (2021)
- [Event-based Vision: A Survey](https://mlsanity.com/paper/1904.08405/) (2019)
- [Benchmarking Single Image Dehazing and Beyond](https://mlsanity.com/paper/1712.04143/) (2017)
- [High Speed and High Dynamic Range Video with an Event Camera](https://mlsanity.com/paper/1906.07165/) (2019)
- [DSEC: A Stereo Event Camera Dataset for Driving Scenarios](https://mlsanity.com/paper/2103.06011/) (2021)
- [DesnowNet: Context-Aware Deep Network for Snow Removal](https://mlsanity.com/paper/1708.04512/) (2017)
- [Time Lens++: Event-based Frame Interpolation with Parametric Non-linear Flow and Multi-scale Fusion](https://mlsanity.com/paper/2203.17191/) (2022)
- [Single Image Deraining: From Model-Based to Data-Driven and Beyond](https://mlsanity.com/paper/1912.07150/) (2019)
- [Restoring Images in Adverse Weather Conditions via Histogram Transformer](https://mlsanity.com/paper/2407.10172/) (2024)
- [Robust Video Content Alignment and Compensation for Rain Removal in a CNN Framework](https://mlsanity.com/paper/1803.10433/) (2018)
- [MUSES: The Multi-Sensor Semantic Perception Dataset for Driving under Uncertainty](https://mlsanity.com/paper/2401.12761/) (2024)
- [Semi-Supervised Video Deraining with Dynamical Rain Generator](https://mlsanity.com/paper/2103.07939/) (2021)
- [v2e: From Video Frames to Realistic DVS Events](https://mlsanity.com/paper/2006.07722/) (2020)
- [Video Rain/Snow Removal by Transformed Online Multiscale Convolutional Sparse Coding](https://mlsanity.com/paper/1909.06148/) (2019)
- [Seeing Behind Dynamic Occlusions with Event Cameras](https://mlsanity.com/paper/2307.15829/) (2023)
- [Event Cameras Meet SPADs for High-Speed, Low-Bandwidth Imaging](https://mlsanity.com/paper/2404.11511/) (2024)
- [EBSnoR: Event-Based Snow Removal by Optimal Dwell Time Thresholding](https://mlsanity.com/paper/2208.10581/) (2022)

## Built on by (most cited first, 1 of 1)

- [From Events to Clarity: The Event-Guided Diffusion Framework for Dehazing](https://mlsanity.com/paper/2511.11944/) (2025)

## Abstract (arXiv)

Adverse weather conditions, particularly heavy snowfall, pose significant challenges to both human drivers and autonomous vehicles. Traditional image-based de-snowing methods often introduce hallucination artifacts as they rely solely on spatial information, while video-based approaches require high frame rates and suffer from alignment artifacts at lower frame rates. Camera parameters, such as exposure time, also influence the appearance of snowflakes, making the problem difficult to solve and heavily dependent on network generalization. In this paper, we propose to address the challenge of desnowing by using event cameras, which offer compressed visual information with submillisecond latency, making them ideal for de-snowing images, even in the presence of ego-motion. Our method leverages the fact that snowflake occlusions appear with a very distinctive streak signature in the spatio-temporal representation of event data. We design an attention-based module that focuses on events along these streaks to determine when a background point was occluded and use this information to recover its original intensity. We benchmark our method on DSEC-Snow, a new dataset created using a green-screen technique that overlays pre-recorded snowfall data onto the existing DSEC driving dataset, resulting in precise ground truth and synchronized image and event streams. Our approach outperforms state-of-the-art de-snowing methods by 3 dB in PSNR for image reconstruction. Moreover, we show that off-the-shelf computer vision algorithms can be applied to our reconstructions for tasks such as depth estimation and optical flow, achieving a 20% performance improvement over other de-snowing methods. Our work represents a crucial step towards enhancing the reliability and safety of vision systems in challenging winter conditions, paving the way for more robust, all-weather-capable applications.