LIDSOR: A FILTER FOR REMOVING RAIN AND SNOW NOISE POINTS FROM LIDAR POINT CLOUDS IN RAINY AND SNOWY WEATHER

12 citations as recorded by crossref.

1. AdverseNet: A LiDAR Point Cloud Denoising Network for Autonomous Driving in Rainy, Snowy, and Foggy Weather X. Yan et al. https://doi.org/10.1109/JSEN.2024.3505234
2. LiDAR Adverse-Weather Simulation with Ground Effect for Robust 3D Object Detection X. Ju et al. https://doi.org/10.3390/app16094409
3. LAWA: LiDAR Adverse Weather Augmentation for Robust Point Cloud Semantic Segmentation H. Kang et al. https://doi.org/10.1109/JSEN.2025.3580941
4. High-Speed Outlier Removal Filter for LiDAR Sensor Point Cloud Data Y. Byun & R. Jeong https://doi.org/10.1109/ACCESS.2024.3517750
5. Point cloud processing under adverse weather: a survey of datasets, enhancement, and denoising methods Z. Gu et al. https://doi.org/10.1007/s11760-025-04352-9
6. LiDAR Desnowing via Adaptive Snow-Candidate Generation and Confidence-Based Label Correction J. Choi & J. Kim https://doi.org/10.1109/ACCESS.2026.3663286
7. LiDAR-Based Long-Term Mapping in Snow-Covered Environments J. Lee et al. https://doi.org/10.3390/s25216805
8. LiDAR Denoising Methods in Adverse Environments: A Review J. Park et al. https://doi.org/10.1109/JSEN.2025.3526175
9. Denoising Framework Based on Multiframe Continuous Point Clouds for Autonomous Driving LiDAR in Snowy Weather X. Yan et al. https://doi.org/10.1109/JSEN.2024.3358341
10. A Parametric-Adaptive Filter for Improving LiDAR Performance Under Snowfall Conditions X. Zhao et al. https://doi.org/10.1109/TITS.2026.3665537
11. LiDAR De-Snow Score (DSS): Combining Quality and Perception Metrics for Optimized De-Noising V. Donzella et al. https://doi.org/10.1109/JSEN.2025.3570478
12. D-LIOR: Adaptive Real-Time Filtering Algorithm for LiDAR Point Clouds in Snowfall Conditions H. Kim et al. https://doi.org/10.1109/JSEN.2025.3621097