2024-04-21: The SemanticSpray++ dataset is accepted at IV2024. Here 2D camera boxes, 3D LiDAR boxes and radar semantic labels are additionally provided (Arxiv).
2023-07-01: The SematnicSpray dataset is released as part of our RA-L / ICRA-2024 paper, providing semantic labels for the LiDAR point cloud (Arxiv).
The SemanticSpray dataset contains scenes in wet surface conditions captured by Camera, LiDAR, and Radar.
The following label types are provided:
2D Boxes
3D Boxes
, Semantic Labels
Semantic Labels
An automatic download script is provided:
git clone https://github.com/uulm-mrm/semantic_spray_dataset.git
bash download.sh
For the manual download of the data, a guide is also provided here.
The sensor setup used for the recordings is the following:
First create a conda envirement and install the requirements:
conda create -n vis python=3.8
conda activate vis
pip3 install -r requirements.txt
To visualize the data in a 2D plot, use:
python3 demo.py --data data/SemanticSprayDataset/ --plot 2D
To visualize the data in a 3D plot, use:
python3 demo.py --data data/SemanticSprayDataset/ --plot 3D
Our approach for label efficient semantic segmentation can learn to segment point clouds in adverse weather using only few labeled scans (e.g., 1, 5, 10). For more information visit: Project Page / Arxiv / <a href="https://www.youtube.com/watch?v=DVdKOYepDmU"> Video
Our method can robustly detect adverse weather conditions like rain spray, rainfall, snow, and fog in LiDAR point clouds.
Additionally, it achieves state-of-the-art results in the detection of weather effects unseen during
training.
For more information visit:
Project
Page / Arxiv / <a
href="https://www.youtube.com/watch?v=pCS3zABdaAU&embeds_referring_euri=https%3A%2F%2Faldipiroli.github.io%2F&source_ve_path=MjM4NTE&feature=emb_title">
Video
If you find this dataset useful in your research, consider citing our work:
@article{10143263,
author = {Piroli, Aldi and Dallabetta, Vinzenz and Kopp, Johannes and Walessa, Marc and Meissner, Daniel and Dietmayer, Klaus},
journal = {IEEE Robotics and Automation Letters},
title = {Energy-Based Detection of Adverse Weather Effects in LiDAR Data},
year = {2023},
volume = {8},
number = {7},
pages = {4322-4329},
doi = {10.1109/LRA.2023.3282382}
}
Additionally, consider citing the original Road Spray dataset:
@misc{https://tudatalib.ulb.tu-darmstadt.de/handle/tudatalib/3537,
url = { https://tudatalib.ulb.tu-darmstadt.de/handle/tudatalib/3537 },
author = { Linnhoff, Clemens and Elster, Lukas and Rosenberger, Philipp and Winner, Hermann },
doi = { 10.48328/tudatalib-930 },
keywords = { Automated Driving, Lidar, Radar, Spray, Weather, Perception, Simulation, 407-04 Verkehrs- und Transportsysteme, Intelligenter und automatisierter Verkehr, 380 },
publisher = { Technical University of Darmstadt },
year = { 2022-04 },
copyright = { Creative Commons Attribution 4.0 },
title = { Road Spray in Lidar and Radar Data for Individual Moving Objects }
}