# Survey on LiDAR Perception in Adverse Weather Conditions

arXiv: https://arxiv.org/abs/2304.06312 · HTML: https://mlsanity.com/paper/2304.06312/

## Key facts

- Cited by 24 papers in the map (data through 14 Oct 2026).
- Builds on 32 of its 81 references that are in the map.
- First posted on arXiv 13 Apr 2023.

## Builds on (most cited first, 30 of 32)

- [nuScenes: A multimodal dataset for autonomous driving](https://mlsanity.com/paper/1903.11027/) (2019)
- [The Cityscapes Dataset for Semantic Urban Scene Understanding](https://mlsanity.com/paper/1604.01685/) (2016)
- [Scalability in Perception for Autonomous Driving: Waymo Open Dataset](https://mlsanity.com/paper/1912.04838/) (2019)
- [CARLA: An Open Urban Driving Simulator](https://mlsanity.com/paper/1711.03938/) (2017)
- [SHIFT: A Synthetic Driving Dataset for Continuous Multi-Task Domain Adaptation](https://mlsanity.com/paper/2206.08367/) (2022)
- [The Oxford Radar RobotCar Dataset: A Radar Extension to the Oxford RobotCar Dataset](https://mlsanity.com/paper/1909.01300/) (2019)
- [RADIATE: A Radar Dataset for Automotive Perception in Bad Weather](https://mlsanity.com/paper/2010.09076/) (2020)
- [Fog Simulation on Real LiDAR Point Clouds for 3D Object Detection in Adverse Weather](https://mlsanity.com/paper/2108.05249/) (2021)
- [Canadian Adverse Driving Conditions Dataset](https://mlsanity.com/paper/2001.10117/) (2020)
- [LiDAR Snowfall Simulation for Robust 3D Object Detection](https://mlsanity.com/paper/2203.15118/) (2022)
- [A Survey on Deep Domain Adaptation for LiDAR Perception](https://mlsanity.com/paper/2106.02377/) (2021)
- [Lidar Light Scattering Augmentation (LISA): Physics-based Simulation of Adverse Weather Conditions for 3D Object Detection](https://mlsanity.com/paper/2107.07004/) (2021)
- [CNN-based Lidar Point Cloud De-Noising in Adverse Weather](https://mlsanity.com/paper/1912.03874/) (2019)
- [A Benchmark for Lidar Sensors in Fog: Is Detection Breaking Down?](https://mlsanity.com/paper/1912.03251/) (2019)
- [Pseudo-labeling for Scalable 3D Object Detection](https://mlsanity.com/paper/2103.02093/) (2021)
- [Robustness of Object Detectors in Degrading Weather Conditions](https://mlsanity.com/paper/2106.08795/) (2021)
- [DSOR: A Scalable Statistical Filter for Removing Falling Snow from LiDAR Point Clouds in Severe Winter Weather](https://mlsanity.com/paper/2109.07078/) (2021)
- [LIBRE: The Multiple 3D LiDAR Dataset](https://mlsanity.com/paper/2003.06129/) (2020)
- [Weather Influence and Classification with Automotive Lidar Sensors](https://mlsanity.com/paper/1906.07675/) (2019)
- [Robust Semantic Segmentation in Adverse Weather Conditions by means of Sensor Data Fusion](https://mlsanity.com/paper/1905.10117/) (2019)
- [How Do We Fail? Stress Testing Perception in Autonomous Vehicles](https://mlsanity.com/paper/2203.14155/) (2022)
- [Boosting LiDAR-based Semantic Labeling by Cross-Modal Training Data Generation](https://mlsanity.com/paper/1804.09915/) (2018)
- [4DenoiseNet: Adverse Weather Denoising from Adjacent Point Clouds](https://mlsanity.com/paper/2209.07121/) (2022)
- [Optimal Sensor Data Fusion Architecture for Object Detection in Adverse Weather Conditions](https://mlsanity.com/paper/1807.02323/) (2018)
- [Worsening Perception: Real-time Degradation of Autonomous Vehicle Perception Performance for Simulation of Adverse Weather Conditions](https://mlsanity.com/paper/2103.02760/) (2021)
- [LiSnowNet: Real-time Snow Removal for LiDAR Point Cloud](https://mlsanity.com/paper/2211.10023/) (2022)
- [An Unsupervised Domain Adaptive Approach for Multimodal 2D Object Detection in Adverse Weather Conditions](https://mlsanity.com/paper/2203.03568/) (2022)
- [Pay "Attention" to Adverse Weather: Weather-aware Attention-based Object Detection](https://mlsanity.com/paper/2204.10803/) (2022)
- [Simulating Road Spray Effects in Automotive Lidar Sensor Models](https://mlsanity.com/paper/2212.08558/) (2022)
- [All Weather Perception: Joint Data Association, Tracking, and Classification for Autonomous Ground Vehicles](https://mlsanity.com/paper/1605.02196/) (2016)

## Built on by (most cited first, 24 of 24)

- [Text2LiDAR: Text-guided LiDAR Point Cloud Generation via Equirectangular Transformer](https://mlsanity.com/paper/2407.19628/) (2024)
- [UniMix: Towards Domain Adaptive and Generalizable LiDAR Semantic Segmentation in Adverse Weather](https://mlsanity.com/paper/2404.05145/) (2024)
- [Self-supervised Multi-future Occupancy Forecasting for Autonomous Driving](https://mlsanity.com/paper/2407.21126/) (2024)
- [TripleMixer: A 3D Point Cloud Denoising Model for Adverse Weather](https://mlsanity.com/paper/2408.13802/) (2024)
- [Realistic Rainy Weather Simulation for LiDARs in CARLA Simulator](https://mlsanity.com/paper/2312.12772/) (2023)
- [Improving Robustness of LiDAR-Camera Fusion Model against Weather Corruption from Fusion Strategy Perspective](https://mlsanity.com/paper/2402.02738/) (2024)
- [DoGFlow: Self-Supervised LiDAR Scene Flow via Cross-Modal Doppler Guidance](https://mlsanity.com/paper/2508.18506/) (2025)
- [Large Foundation Models for Trajectory Prediction in Autonomous Driving: A Comprehensive Survey](https://mlsanity.com/paper/2509.10570/) (2025)
- [SemanticSpray++: A Multimodal Dataset for Autonomous Driving in Wet Surface Conditions](https://mlsanity.com/paper/2406.09945/) (2024)
- [Adverse Weather Conditions Augmentation of LiDAR Scenes with Latent Diffusion Models](https://mlsanity.com/paper/2501.01761/) (2025)
- [RC-GeoCP: Geometric Consensus for 4D Radar-Camera Collaborative Perception](https://mlsanity.com/paper/2603.00654/) (2026)
- [Synthesizing and Identifying Noise Levels in Autonomous Vehicle Camera Radar Datasets](https://mlsanity.com/paper/2505.00584/) (2025)
- [Weather-Dependent Variations in Driver Gaze Behavior: A Case Study in Rainy Conditions](https://mlsanity.com/paper/2509.01013/) (2025)
- [MLF-4DRCNet: Multi-Level Fusion with 4D Radar and Camera for 3D Object Detection in Autonomous Driving](https://mlsanity.com/paper/2509.18613/) (2025)
- [ObsDriveBench: Benchmarking Multimodal Understanding under Adverse Weather with Observability Awareness](https://mlsanity.com/paper/2607.23537/) (2026)
- [Label-Efficient Semantic Segmentation of LiDAR Point Clouds in Adverse Weather Conditions](https://mlsanity.com/paper/2406.09906/) (2024)
- [A Generative Adversarial Network-based Method for LiDAR-Assisted Radar Image Enhancement](https://mlsanity.com/paper/2409.00196/) (2024)
- [Automated Road Extraction and Centreline Fitting in LiDAR Point Clouds](https://mlsanity.com/paper/2502.07486/) (2025)
- [An Investigation of Beam Density on LiDAR Object Detection Performance](https://mlsanity.com/paper/2503.15087/) (2025)
- [Geo-Registration of Terrestrial LiDAR Point Clouds with Satellite Images without GNSS](https://mlsanity.com/paper/2507.05999/) (2025)
- [3D Human Pose and Shape Estimation from LiDAR Point Clouds: A Review](https://mlsanity.com/paper/2509.12197/) (2025)
- [MambaTrack3D: A State Space Model Framework for LiDAR-Based Object Tracking under High Temporal Variation](https://mlsanity.com/paper/2511.15077/) (2025)
- [A Lightweight Model-Driven 4D Radar Framework for Pervasive Human Detection in Harsh Conditions](https://mlsanity.com/paper/2601.13373/) (2026)
- [Beyond the Snowflakes: Disentangling Weather Effects from Confounding Meta-Factors in LiDAR Object Detection](https://mlsanity.com/paper/2610.13891/) (2026)

## Abstract (arXiv)

Autonomous vehicles rely on a variety of sensors to gather information about their surrounding. The vehicle's behavior is planned based on the environment perception, making its reliability crucial for safety reasons. The active LiDAR sensor is able to create an accurate 3D representation of a scene, making it a valuable addition for environment perception for autonomous vehicles. Due to light scattering and occlusion, the LiDAR's performance change under adverse weather conditions like fog, snow or rain. This limitation recently fostered a large body of research on approaches to alleviate the decrease in perception performance. In this survey, we gathered, analyzed, and discussed different aspects on dealing with adverse weather conditions in LiDAR-based environment perception. We address topics such as the availability of appropriate data, raw point cloud processing and denoising, robust perception algorithms and sensor fusion to mitigate adverse weather induced shortcomings. We furthermore identify the most pressing gaps in the current literature and pinpoint promising research directions.