# DSOR: A Scalable Statistical Filter for Removing Falling Snow from LiDAR Point Clouds in Severe Winter Weather

arXiv: https://arxiv.org/abs/2109.07078 · HTML: https://mlsanity.com/paper/2109.07078/

## Key facts

- Cited by 27 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 22 references that are in the map.
- First posted on arXiv 15 Sep 2021.

## Builds on (most cited first, 9 of 9)

- [nuScenes: A multimodal dataset for autonomous driving](https://mlsanity.com/paper/1903.11027/) (2019)
- [Semantic Foggy Scene Understanding with Synthetic Data](https://mlsanity.com/paper/1708.07819/) (2017)
- [The ApolloScape Open Dataset for Autonomous Driving and its Application](https://mlsanity.com/paper/1803.06184/) (2018)
- [SPG: Unsupervised Domain Adaptation for 3D Object Detection via Semantic Point Generation](https://mlsanity.com/paper/2108.06709/) (2021)
- [Canadian Adverse Driving Conditions Dataset](https://mlsanity.com/paper/2001.10117/) (2020)
- [CNN-based Lidar Point Cloud De-Noising in Adverse Weather](https://mlsanity.com/paper/1912.03874/) (2019)
- [Linking Points With Labels in 3D: A Review of Point Cloud Semantic Segmentation](https://mlsanity.com/paper/1908.08854/) (2019)
- [Optimal Sensor Data Fusion Architecture for Object Detection in Adverse Weather Conditions](https://mlsanity.com/paper/1807.02323/) (2018)
- [Low-complexity Point Cloud Filtering for LiDAR by PCA-based Dimension Reduction](https://mlsanity.com/paper/2007.14180/) (2020)

## Built on by (most cited first, 27 of 27)

- [K-Radar: 4D Radar Object Detection for Autonomous Driving in Various Weather Conditions](https://mlsanity.com/paper/2206.08171/) (2022)
- [Neural LiDAR Fields for Novel View Synthesis](https://mlsanity.com/paper/2305.01643/) (2023)
- [A Survey on Autonomous Driving Datasets: Statistics, Annotation Quality, and a Future Outlook](https://mlsanity.com/paper/2401.01454/) (2024)
- [Survey on LiDAR Perception in Adverse Weather Conditions](https://mlsanity.com/paper/2304.06312/) (2023)
- [Enhanced K-Radar: Optimal Density Reduction to Improve Detection Performance and Accessibility of 4D Radar Tensor-based Object Detection](https://mlsanity.com/paper/2303.06342/) (2023)
- [4DenoiseNet: Adverse Weather Denoising from Adjacent Point Clouds](https://mlsanity.com/paper/2209.07121/) (2022)
- [LiSnowNet: Real-time Snow Removal for LiDAR Point Cloud](https://mlsanity.com/paper/2211.10023/) (2022)
- [Energy-based Detection of Adverse Weather Effects in LiDAR Data](https://mlsanity.com/paper/2305.16129/) (2023)
- [TripleMixer: A 3D Point Cloud Denoising Model for Adverse Weather](https://mlsanity.com/paper/2408.13802/) (2024)
- [Vulnerable Road User Detection and Safety Enhancement: A Comprehensive Survey](https://mlsanity.com/paper/2405.19202/) (2024)
- [Towards Robust 3D Object Detection In Rainy Conditions](https://mlsanity.com/paper/2310.00944/) (2023)
- [SemanticSpray++: A Multimodal Dataset for Autonomous Driving in Wet Surface Conditions](https://mlsanity.com/paper/2406.09945/) (2024)
- [How Hard Is Snow? A Paired Domain Adaptation Dataset for Clear and Snowy Weather: CADC+](https://mlsanity.com/paper/2506.16531/) (2025)
- [RADE-Net: Robust Attention Network for Radar-Only Object Detection in Adverse Weather](https://mlsanity.com/paper/2602.19994/) (2026)
- [Deep learning for radar data exploitation of autonomous vehicle](https://mlsanity.com/paper/2203.08038/) (2022)
- [ThermRad: A Multi-modal Dataset for Robust 3D Object Detection under Challenging Conditions](https://mlsanity.com/paper/2308.10161/) (2023)
- [DMNR: Unsupervised De-noising of Point Clouds Corrupted by Airborne Particles](https://mlsanity.com/paper/2305.05991/) (2023)
- [Efficient Real-time Smoke Filtration with 3D LiDAR for Search and Rescue with Autonomous Heterogeneous Robotic Systems](https://mlsanity.com/paper/2308.07264/) (2023)
- [Enhancing Lidar-based Object Detection in Adverse Weather using Offset Sequences in Time](https://mlsanity.com/paper/2401.09049/) (2024)
- [Label-Efficient Semantic Segmentation of LiDAR Point Clouds in Adverse Weather Conditions](https://mlsanity.com/paper/2406.09906/) (2024)
- [Towards Robust Unsupervised Attention Prediction in Autonomous Driving](https://mlsanity.com/paper/2501.15045/) (2025)
- [An Investigation of Beam Density on LiDAR Object Detection Performance](https://mlsanity.com/paper/2503.15087/) (2025)
- [REHEARSE-3D: A Multi-modal Emulated Rain Dataset for 3D Point Cloud De-raining](https://mlsanity.com/paper/2504.21699/) (2025)
- [AdaRadar: Rate Adaptive Spectral Compression for Radar-based Perception](https://mlsanity.com/paper/2603.17979/) (2026)
- [LIORNet: Self-Supervised LiDAR Snow Removal Framework for Autonomous Driving under Adverse Weather Conditions](https://mlsanity.com/paper/2603.19936/) (2026)
- [DinoRADE: Full Spectral Radar-Camera Fusion with Vision Foundation Model Features for Multi-class Object Detection in Adverse Weather](https://mlsanity.com/paper/2604.08074/) (2026)
- [Beyond the Snowflakes: Disentangling Weather Effects from Confounding Meta-Factors in LiDAR Object Detection](https://mlsanity.com/paper/2610.13891/) (2026)

## Abstract (arXiv)

For autonomous vehicles to viably replace human drivers they must contend with inclement weather. Falling rain and snow introduce noise in LiDAR returns resulting in both false positive and false negative object detections. In this article we introduce the Winter Adverse Driving dataSet (WADS) collected in the snow belt region of Michigan's Upper Peninsula. WADS is the first multi-modal dataset featuring dense point-wise labeled sequential LiDAR scans collected in severe winter weather; weather that would cause an experienced driver to alter their driving behavior. We have labelled and will make available over 7 GB or 3.6 billion labelled LiDAR points out of over 26 TB of total LiDAR and camera data collected. We also present the Dynamic Statistical Outlier Removal (DSOR) filter, a statistical PCL-based filter capable or removing snow with a higher recall than the state of the art snow de-noising filter while being 28% faster. Further, the DSOR filter is shown to have a lower time complexity compared to the state of the art resulting in an improved scalability. Our labeled dataset and DSOR filter will be made available at https://bitbucket.org/autonomymtu/dsorfilter