mlsanityData to 7 Oct 2026

4DenoiseNet: Adverse Weather Denoising from Adjacent Point Clouds

arXiv 2209.07121 ↗First posted 15 Sep 2022cs.CV cs.ROcited by 14

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Abstract (arXiv)

Reliable point cloud data is essential for perception tasks textite.g. in robotics and autonomous driving applications. Adverse weather causes a specific type of noise to light detection and ranging (LiDAR) sensor data, which degrades the quality of the point clouds significantly. To address this issue, this letter presents a novel point cloud adverse weather denoising deep learning algorithm (4DenoiseNet). Our algorithm takes advantage of the time dimension unlike deep learning adverse weather denoising methods in the literature. It performs about 10% better in terms of intersection over union metric compared to the previous work and is more computationally efficient. These results are achieved on our novel SnowyKITTI dataset, which has over 40000 adverse weather annotated point clouds. Moreover, strong qualitative results on the Canadian Adverse Driving Conditions dataset indicate good generalizability to domain shifts and to different sensor intrinsics.

Key facts. Cited by 14 papers in the map (data through 7 Oct 2026). Builds on 28 of its 49 references that are in the map. First posted on arXiv 15 Sep 2022.

Builds on

28 of 49 references.

References · 28

  1. 2014Adam: A Method for Stochastic Optimizationcited by 36,960
  2. 2016PointNet: Deep Learning on Point Sets for 3D Classification and Segmentationcited by 5,223
  3. 2017PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Spacecited by 4,384
  4. 2019RandLA-Net: Efficient Semantic Segmentation of Large-Scale Point Cloudscited by 584
  5. 2020Searching Efficient 3D Architectures with Sparse Point-Voxel Convolutioncited by 422
  6. 2020Cylindrical and Asymmetrical 3D Convolution Networks for LiDAR Segmentationcited by 388
  7. 2017Large-scale Point Cloud Semantic Segmentation with Superpoint Graphscited by 384
  8. 2017SqueezeSeg: Convolutional Neural Nets with Recurrent CRF for Real-Time Road-Object Segmentation from 3D LiDAR Point Cloudcited by 231
  9. 2018SqueezeSegV2: Improved Model Structure and Unsupervised Domain Adaptation for Road-Object Segmentation from a LiDAR Point Cloudcited by 228
  10. 2020SalsaNext: Fast, Uncertainty-aware Semantic Segmentation of LiDAR Point Clouds for Autonomous Drivingcited by 226
  11. 2020Sparse Single Sweep LiDAR Point Cloud Segmentation via Learning Contextual Shape Priors from Scene Completioncited by 222
  12. 2018Tangent Convolutions for Dense Prediction in 3Dcited by 173
16 more
  1. 2021RPVNet: A Deep and Efficient Range-Point-Voxel Fusion Network for LiDAR Point Cloud Segmentationcited by 173
  2. 2020SqueezeSegV3: Spatially-Adaptive Convolution for Efficient Point-Cloud Segmentationcited by 170
  3. 2018SPLATNet: Sparse Lattice Networks for Point Cloud Processingcited by 158
  4. 2021Panoptic-PolarNet: Proposal-free LiDAR Point Cloud Panoptic Segmentationcited by 107
  5. 2021Fog Simulation on Real LiDAR Point Clouds for 3D Object Detection in Adverse Weathercited by 95
  6. 2020Canadian Adverse Driving Conditions Datasetcited by 86
  7. 2021EfficientLPS: Efficient LiDAR Panoptic Segmentationcited by 76
  8. 2019Motion Guided Attention for Video Salient Object Detectioncited by 72
  9. 2022LiDAR Snowfall Simulation for Robust 3D Object Detectioncited by 72
  10. 2019SalsaNet: Fast Road and Vehicle Segmentation in LiDAR Point Clouds for Autonomous Drivingcited by 57
  11. 2021Lite-HDSeg: LiDAR Semantic Segmentation Using Lite Harmonic Dense Convolutionscited by 37
  12. 2019CNN-based Lidar Point Cloud De-Noising in Adverse Weathercited by 33
  13. 2019A Benchmark for Lidar Sensors in Fog: Is Detection Breaking Down?cited by 32
  14. 2021DSOR: A Scalable Statistical Filter for Removing Falling Snow from LiDAR Point Clouds in Severe Winter Weathercited by 26
  15. 2019Weather Influence and Classification with Automotive Lidar Sensorscited by 24
  16. 2021LatticeNet: Fast Spatio-Temporal Point Cloud Segmentation Using Permutohedral Latticescited by 11

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 3; 2024: 4; 2025: 6; 2026: 1.

14 papers.

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  1. 2024Is Your LiDAR Placement Optimized for 3D Scene Understanding?cited by 25
  2. 2024Multi-Space Alignments Towards Universal LiDAR Segmentationcited by 24
  3. 2023Survey on LiDAR Perception in Adverse Weather Conditionscited by 23
  4. 2025WeatherGen: A Unified Diverse Weather Generator for LiDAR Point Clouds via Spider Mamba Diffusioncited by 12
  5. 2024TripleMixer: A 3D Point Cloud Denoising Model for Adverse Weathercited by 7
  6. 2023Multi-Echo Denoising in Adverse Weathercited by 1
  7. 2025How Hard Is Snow? A Paired Domain Adaptation Dataset for Clear and Snowy Weather: CADC+cited by 1
  8. 2023Efficient Real-time Smoke Filtration with 3D LiDAR for Search and Rescue with Autonomous Heterogeneous Robotic Systemscited by 0
  9. 2024Label-Efficient Semantic Segmentation of LiDAR Point Clouds in Adverse Weather Conditionscited by 0
  10. 2025REHEARSE-3D: A Multi-modal Emulated Rain Dataset for 3D Point Cloud De-rainingcited by 0
  11. 2025Deep Learning For Point Cloud Denoising: A Surveycited by 0
  12. 2025A Survey of Deep Learning-based Point Cloud Denoisingcited by 0
2 more
  1. 2025Adaptive Dual-Weighted Gravitational Point Cloud Denoising Methodcited by 0
  2. 2026LIORNet: Self-Supervised LiDAR Snow Removal Framework for Autonomous Driving under Adverse Weather Conditionscited by 0