# LiSnowNet: Real-time Snow Removal for LiDAR Point Cloud

arXiv: https://arxiv.org/abs/2211.10023 · HTML: https://mlsanity.com/paper/2211.10023/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 22 references that are in the map.
- First posted on arXiv 18 Nov 2022.

## Builds on (most cited first, 15 of 15)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift](https://mlsanity.com/paper/1502.03167/) (2015)
- [Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning](https://mlsanity.com/paper/1506.02142/) (2015)
- [Scalability in Perception for Autonomous Driving: Waymo Open Dataset](https://mlsanity.com/paper/1912.04838/) (2019)
- [PointPillars: Fast Encoders for Object Detection from Point Clouds](https://mlsanity.com/paper/1812.05784/) (2018)
- [From Points to Parts: 3D Object Detection from Point Cloud with Part-aware and Part-aggregation Network](https://mlsanity.com/paper/1907.03670/) (2019)
- [One Thousand and One Hours: Self-driving Motion Prediction Dataset](https://mlsanity.com/paper/2006.14480/) (2020)
- [SalsaNext: Fast, Uncertainty-aware Semantic Segmentation of LiDAR Point Clouds for Autonomous Driving](https://mlsanity.com/paper/2003.03653/) (2020)
- [Panoptic nuScenes: A Large-Scale Benchmark for LiDAR Panoptic Segmentation and Tracking](https://mlsanity.com/paper/2109.03805/) (2021)
- [Canadian Adverse Driving Conditions Dataset](https://mlsanity.com/paper/2001.10117/) (2020)
- [Multi-level Wavelet Convolutional Neural Networks](https://mlsanity.com/paper/1907.03128/) (2019)
- [CNN-based Lidar Point Cloud De-Noising in Adverse Weather](https://mlsanity.com/paper/1912.03874/) (2019)
- [DSOR: A Scalable Statistical Filter for Removing Falling Snow from LiDAR Point Clouds in Severe Winter Weather](https://mlsanity.com/paper/2109.07078/) (2021)
- [Boosting LiDAR-based Semantic Labeling by Cross-Modal Training Data Generation](https://mlsanity.com/paper/1804.09915/) (2018)
- [S3Net: 3D LiDAR Sparse Semantic Segmentation Network](https://mlsanity.com/paper/2103.08745/) (2021)

## Built on by (most cited first, 10 of 10)

- [Survey on LiDAR Perception in Adverse Weather Conditions](https://mlsanity.com/paper/2304.06312/) (2023)
- [TripleMixer: A 3D Point Cloud Denoising Model for Adverse Weather](https://mlsanity.com/paper/2408.13802/) (2024)
- [DoGFlow: Self-Supervised LiDAR Scene Flow via Cross-Modal Doppler Guidance](https://mlsanity.com/paper/2508.18506/) (2025)
- [How Hard Is Snow? A Paired Domain Adaptation Dataset for Clear and Snowy Weather: CADC+](https://mlsanity.com/paper/2506.16531/) (2025)
- [Multi-Echo Denoising in Adverse Weather](https://mlsanity.com/paper/2305.14008/) (2023)
- [Deep Learning For Point Cloud Denoising: A Survey](https://mlsanity.com/paper/2508.11932/) (2025)
- [REHEARSE-3D: A Multi-modal Emulated Rain Dataset for 3D Point Cloud De-raining](https://mlsanity.com/paper/2504.21699/) (2025)
- [A Survey of Deep Learning-based Point Cloud Denoising](https://mlsanity.com/paper/2508.17011/) (2025)
- [Adaptive Dual-Weighted Gravitational Point Cloud Denoising Method](https://mlsanity.com/paper/2512.10386/) (2025)
- [LIORNet: Self-Supervised LiDAR Snow Removal Framework for Autonomous Driving under Adverse Weather Conditions](https://mlsanity.com/paper/2603.19936/) (2026)

## Abstract (arXiv)

LiDARs have been widely adopted to modern self-driving vehicles, providing 3D information of the scene and surrounding objects. However, adverser weather conditions still pose significant challenges to LiDARs since point clouds captured during snowfall can easily be corrupted. The resulting noisy point clouds degrade downstream tasks such as mapping. Existing works in de-noising point clouds corrupted by snow are based on nearest-neighbor search, and thus do not scale well with modern LiDARs which usually capture 100k or more points at 10Hz. In this paper, we introduce an unsupervised de-noising algorithm, LiSnowNet, running 52× faster than the state-of-the-art methods while achieving superior performance in de-noising. Unlike previous methods, the proposed algorithm is based on a deep convolutional neural network and can be easily deployed to hardware accelerators such as GPUs. In addition, we demonstrate how to use the proposed method for mapping even with corrupted point clouds.