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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).
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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:
- Camera:
2D Boxes
- LiDAR:
3D Boxes
,Semantic Labels
- Radar:
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:
- 1 Front Camera
- 1 Velodyne VLP32C LiDAR (top-mounted high-resolution LiDAR)
- 2 Ibeo LUX 2010 LiDAR (front and rear mounted, l.- w-resolution LiDAR)
- 1 Aptiv ESR 2.5 Radar
- [Camera Image] in the folder "image_2"
- [VLP32C LiDAR] in the folder "velodyne"
- [VLIbeo LUX 2010 LiDAR front] in the folder "ibeo_front"
- [VLIbeo LUX 2010 LiDAR rear] in the folder "ibeo_rear"
- [Aptiv ESR 2.5 Radar] in the folder "delphi_radar"
- [Semantic Labels for VLP32C LiDAR] in the folder "labels"
- [Semantic Labels for Radar] in the folder "radar_labels"
- [3D Object Labels for VLP32C LiDAR] in the folder "object_labels/lidar"
- [2D Object Labels for Camera] in the folder "object_labels/camera"
- The ego vehicle poses are located in the file "poses.txt". The convention used by the SemanticKITTI dataset is followed.
- Additional information on the scene setup (e.g., ego_velocity) are given in the "metadata.txt" file.
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First create a conda envirement and install the requirements:
conda create -n vis python=3.8 conda activate vis pip3 install -r requirements.txt
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To visualize the data in a 2D plot, use:
python3 demo.py --data data/SemanticSprayDataset/ --plot 2D
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To visualize the data in a 3D plot, use:
python3 demo.py --data data/SemanticSprayDataset/ --plot 3D
Label-Efficient Semantic Segmentation of LiDAR Point Clouds in Adverse Weather Conditions [RA-L 2024]
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 / 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 /
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 }
}