YoungJae Cheong ·
Jhonhyun An
ICRA 2026
- [2026/02/01]: 🎉 Our paper has been accepted to the IEEE International Conference on Robotics and Automation (ICRA).
- Code and pretrained models will be released soon. (This repository will be updated.)
LiDAR semantic segmentation degrades in adverse weather because refraction, scattering, and point dropouts corrupt geometry. Prior work, including weather simulation, mixing-based augmentation, domain randomization, and uncertainty or boundary regularization, improves robustness but still overlooks structural vulnerabilities near boundaries, corners, and sparse regions. We present a Light Geometry-aware adapter. The module aligns azimuth and applies horizontal circular padding to preserve neighbor continuity across the 0◦-360◦ wrap-around boundary. A local-window K-Nearest Neighbors gathers nearby points and computes simple local statistics, which are compressed into compact geometry-aware cues. During training, these cues drive region-aware regularization that stabilizes predictions in structurally fragile areas. The adapter is plug-and-play, complements augmentation, and can be enabled only during training with negligible inference cost. We adopt a source-only cross-weather setup where models train on SemanticKITTI and are evaluated on SemanticSTF without target labels or fine-tuning. The adapter improves mIoU by +3.4% over the data-centric augmentation baseline and by +0.3% over the class-centric regularization baseline. These results indicate that geometry-driven regularization is a keydirection for all-weather LiDAR segmentation.
conda create -n lidar_weather python=3.8 -y && conda activate lidar_weather
conda install pytorch==1.10.0 torchvision==0.11.0 cudatoolkit=11.3 -c pytorch -y
pip install -U openmim && mim install mmengine && mim install 'mmcv>=2.0.0rc4, <2.1.0' && mim install 'mmdet>=3.0.0, <3.2.0'
git clone https://github.com/YoungJae1559/GA-PointDrop.git
cd GA-PointDrop && pip install -v -e .
pip install cumm-cu113 && pip install spconv-cu113
sudo apt-get install libsparsehash-dev
export PATH=/usr/local/cuda/bin:$PATH && pip install --upgrade git+https://github.com/mit-han-lab/torchsparse.git@v1.4.0
pip install nuscenes-devkit
pip install wandbPlease refer to DATA_PREPARE.md for the details to prepare the 1SemanticKITTI, 2SynLiDAR, 3SemanticSTF, and 4SemanticKITTI-C datasets.
- Training & evaluation code for SemanticKITTI to SemanticSTF
Training
./tools/dist_train.sh configs/lidarweather_minkunet/sj+lpd+minkunet_semantickitti_GMX.py 2Evaluation
python tools/test.py configs/lidarweather_minkunet/sj+lpd+minkunet_semanticstf_GMX.py sj+lpd+minkunet_semantickitti_GMX/best_miou_epoch_15.pth --task lidar_seg --show-dir /home/vip/harry/LiDARWeather/LiDARweather+GMX --show- Training & evaluation code for SynLiDAR to SemanticSTF
Training
./tools/dist_train.sh configs/lidarweather_minkunet/sj+lpd+minkunet_semantickitti_GMX.py 2Evaluation
python tools/test.py configs/lidarweather_minkunet/sj+lpd+minkunet_semanticstf_GMX.py sj+lpd+minkunet_synlidar_GMX/best_miou_epoch_15.pth --task lidar_seg --show-dir /home/vip/harry/LiDARWeather/LiDARweather+GMX --show- YoungJae Cheong —
bluebull777@gachon.ac.kr - Jhonhyun An —
jhonghyun@gachon.ac.kr
If you have questions, please open an issue or contact us via email.
We are deeply grateful for the following outstanding opensource work; without them, our work would not have been possible.