mlsanityData to 14 Oct 2026

CNN-based Lidar Point Cloud De-Noising in Adverse Weather

arXiv 1912.03874 ↗First posted 9 Dec 2019cs.CV cs.ROcited by 33

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Abstract (arXiv)

Lidar sensors are frequently used in environment perception for autonomous vehicles and mobile robotics to complement camera, radar, and ultrasonic sensors. Adverse weather conditions are significantly impacting the performance of lidar-based scene understanding by causing undesired measurement points that in turn effect missing detections and false positives. In heavy rain or dense fog, water drops could be misinterpreted as objects in front of the vehicle which brings a mobile robot to a full stop. In this paper, we present the first CNN-based approach to understand and filter out such adverse weather effects in point cloud data. Using a large data set obtained in controlled weather environments, we demonstrate a significant performance improvement of our method over state-of-the-art involving geometric filtering. Data is available at https://github.com/rheinzler/PointCloudDeNoising.

Key facts. Cited by 33 papers in the map (data through 14 Oct 2026). First posted on arXiv 9 Dec 2019.

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 5; 2022: 7; 2023: 7; 2024: 6; 2025: 5; 2026: 3.

33 papers.

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  1. 2023Virtual Sparse Convolution for Multimodal 3D Object Detectioncited by 81
  2. 2022LiDAR Snowfall Simulation for Robust 3D Object Detectioncited by 73
  3. 2021A Survey on Deep Domain Adaptation for LiDAR Perceptioncited by 47
  4. 2021Lidar Light Scattering Augmentation (LISA): Physics-based Simulation of Adverse Weather Conditions for 3D Object Detectioncited by 43
  5. 2024L4DR: LiDAR-4DRadar Fusion for Weather-Robust 3D Object Detectioncited by 34
  6. 2021DSOR: A Scalable Statistical Filter for Removing Falling Snow from LiDAR Point Clouds in Severe Winter Weathercited by 27
  7. 2023Survey on LiDAR Perception in Adverse Weather Conditionscited by 24
  8. 20224DenoiseNet: Adverse Weather Denoising from Adjacent Point Cloudscited by 14
  9. 2024Sunshine to Rainstorm: Cross-Weather Knowledge Distillation for Robust 3D Object Detectioncited by 11
  10. 2022LiSnowNet: Real-time Snow Removal for LiDAR Point Cloudcited by 10
  11. 2022Robust 3D Object Detection in Cold Weather Conditionscited by 7
  12. 2023Energy-based Detection of Adverse Weather Effects in LiDAR Datacited by 7
21 more
  1. 2024TripleMixer: A 3D Point Cloud Denoising Model for Adverse Weathercited by 7
  2. 2022Pay "Attention" to Adverse Weather: Weather-aware Attention-based Object Detectioncited by 5
  3. 2022SLiDE: Self-supervised LiDAR De-snowing through Reconstruction Difficultycited by 5
  4. 2021Roadmap on Signal Processing for Next Generation Measurement Systemscited by 4
  5. 2022Detection of Condensed Vehicle Gas Exhaust in LiDAR Point Cloudscited by 4
  6. 2021RiWNet: A moving object instance segmentation Network being Robust in adverse Weather conditionscited by 2
  7. 2023Towards Robust 3D Object Detection In Rainy Conditionscited by 2
  8. 2024SemanticSpray++: A Multimodal Dataset for Autonomous Driving in Wet Surface Conditionscited by 2
  9. 2025How Hard Is Snow? A Paired Domain Adaptation Dataset for Clear and Snowy Weather: CADC+cited by 2
  10. 2026PRAM-R: A Perception-Reasoning-Action-Memory Framework with LLM-Guided Modality Routing for Adaptive Autonomous Drivingcited by 2
  11. 2023Multi-Echo Denoising in Adverse Weathercited by 1
  12. 2024WARLearn: Weather-Adaptive Representation Learningcited by 1
  13. 2025Deep Learning For Point Cloud Denoising: A Surveycited by 1
  14. 2023DMNR: Unsupervised De-noising of Point Clouds Corrupted by Airborne Particlescited by 0
  15. 2023Efficient Real-time Smoke Filtration with 3D LiDAR for Search and Rescue with Autonomous Heterogeneous Robotic Systemscited by 0
  16. 2024Label-Efficient Semantic Segmentation of LiDAR Point Clouds in Adverse Weather Conditionscited by 0
  17. 2025REHEARSE-3D: A Multi-modal Emulated Rain Dataset for 3D Point Cloud De-rainingcited by 0
  18. 2025DenoiseCP-Net: Efficient Collective Perception in Adverse Weather via Joint LiDAR-Based 3D Object Detection and Denoisingcited by 0
  19. 2025A Survey of Deep Learning-based Point Cloud Denoisingcited by 0
  20. 2026LIORNet: Self-Supervised LiDAR Snow Removal Framework for Autonomous Driving under Adverse Weather Conditionscited by 0
  21. 2026VLM-CASE: Vision-Language Model Enabled Context-Adaptive Safety Envelopes for Anticipatory Safe Autonomous Drivingcited by 0