SemanticSpray Dataset

News

• 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).

TL;DR

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

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Getting Started

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.

Exploring The Data

The sensor setup used for the recordings is the following:

Sensors

• 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

Raw Data

• [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"

Labels

• [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"

Misc

• 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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Visualizing The Data

• 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

  

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Related Work

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

Energy-based Detection of Adverse Weather Effects in LiDAR Data [RA-L 2023]

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

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Citation

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 }
}