mlsanityData to 14 Oct 2026

LiSnowNet: Real-time Snow Removal for LiDAR Point Cloud

arXiv 2211.10023 ↗First posted 18 Nov 2022cs.CV cs.LG eess.IVcited by 10

Not on the map. Why?

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.

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

15 of 22 references.

References · 15

  1. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  2. 2015Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shiftcited by 7,005
  3. 2015Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learningcited by 4,448
  4. 2019Scalability in Perception for Autonomous Driving: Waymo Open Datasetcited by 2,451
  5. 2018PointPillars: Fast Encoders for Object Detection from Point Cloudscited by 1,715
  6. 2019From Points to Parts: 3D Object Detection from Point Cloud with Part-aware and Part-aggregation Networkcited by 368
  7. 2020One Thousand and One Hours: Self-driving Motion Prediction Datasetcited by 240
  8. 2020SalsaNext: Fast, Uncertainty-aware Semantic Segmentation of LiDAR Point Clouds for Autonomous Drivingcited by 226
  9. 2021Panoptic nuScenes: A Large-Scale Benchmark for LiDAR Panoptic Segmentation and Trackingcited by 166
  10. 2020Canadian Adverse Driving Conditions Datasetcited by 87
  11. 2019Multi-level Wavelet Convolutional Neural Networkscited by 59
  12. 2019CNN-based Lidar Point Cloud De-Noising in Adverse Weathercited by 33
3 more
  1. 2021DSOR: A Scalable Statistical Filter for Removing Falling Snow from LiDAR Point Clouds in Severe Winter Weathercited by 27
  2. 2018Boosting LiDAR-based Semantic Labeling by Cross-Modal Training Data Generationcited by 14
  3. 2021S3Net: 3D LiDAR Sparse Semantic Segmentation Networkcited by 9

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 2; 2024: 1; 2025: 6; 2026: 1.

10 papers.

  1. 2023Survey on LiDAR Perception in Adverse Weather Conditionscited by 24
  2. 2024TripleMixer: A 3D Point Cloud Denoising Model for Adverse Weathercited by 7
  3. 2025DoGFlow: Self-Supervised LiDAR Scene Flow via Cross-Modal Doppler Guidancecited by 4
  4. 2025How Hard Is Snow? A Paired Domain Adaptation Dataset for Clear and Snowy Weather: CADC+cited by 2
  5. 2023Multi-Echo Denoising in Adverse Weathercited by 1
  6. 2025Deep Learning For Point Cloud Denoising: A Surveycited by 1
  7. 2025REHEARSE-3D: A Multi-modal Emulated Rain Dataset for 3D Point Cloud De-rainingcited by 0
  8. 2025A Survey of Deep Learning-based Point Cloud Denoisingcited by 0
  9. 2025Adaptive Dual-Weighted Gravitational Point Cloud Denoising Methodcited by 0
  10. 2026LIORNet: Self-Supervised LiDAR Snow Removal Framework for Autonomous Driving under Adverse Weather Conditionscited by 0