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

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arxiv 2109.07078 v2 pith:L4X5ZJVX submitted 2021-09-15 cs.CV cs.RO

Akhil Kurup , Jeremy Bos This is my paper · ORCID
classification cs.CVcs.RO
keywords filterdsorlidarsnowweathercollecteddatasetstatistical
verification ladder T0 review T1 audit T2 compute T3 formal

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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/dsor_filter

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Forward citations

Cited by 5 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. An Iterative Task-Driven Framework for Resilient LiDAR Place Recognition in Adverse Weather

    cs.RO 2025-04 conditional novelty 7.0 of 10

    ITDNet jointly optimizes LiDAR restoration and place recognition via alternating task-driven training, and reports state-of-the-art retrieval accuracy in rain, snow, and fog.

  2. How Hard Is Snow? A Paired Domain Adaptation Dataset for Clear and Snowy Weather: CADC+

    cs.CV 2025-06 conditional novelty 6.0 of 10

    CADC+ pairs each snowy CADC driving sequence with a matching clear-weather sequence from the same roads and period, enabling controlled evaluation of snow's impact on LiDAR 3D detection.

  3. REHEARSE-3D: A Multi-modal Emulated Rain Dataset for 3D Point Cloud De-raining

    cs.CV 2025-04 conditional novelty 6.0 of 10

    REHEARSE-3D provides 9.2 billion point-wise annotated LiDAR-256 and 4D radar points in emulated rain, plus a benchmark for raindrop detection and removal.

  4. Generalizing Unsupervised Lidar Odometry Model from Normal to Snowy Weather Conditions

    cs.RO 2025-09 conditional novelty 5.0 of 10

    An unsupervised LiDAR odometry system trained on clear weather attains low drift on snowy and dynamic test scenes using patch dispersion scoring, point weights, and an intensity mask.

  5. Towards Robust Unsupervised Attention Prediction in Autonomous Driving

    cs.CV 2025-01 conditional novelty 5.0 of 10

    A new unsupervised driver attention predictor with knowledge embedding, uncertainty mining, and RoboMixup augmentation matches or beats several supervised baselines on clean and corrupted driving benchmarks.

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