sudo apt-get install npm
git clone https://github.com/walzimmer/3d-bat.git & cd 3d-bat
conda create -n 3d-bat python==3.11.3
conda activate 3d-bat
pip install -r requirements.txt
conda install -c conda-forge nodejs==10.13.0
npm install
npm run start-server
npm run start
The index.html
file should open now in the specified browser (chromium-browser by default).
The default browser can be changed in the package.json
file, line 32:
"start": "webpack serve --inline --open chromium-browser",
See Custom Data Annotation for more details.
Instructions for data annotation can be found here.
See Commands and Shortcuts for more details.
./tutorial_videos
folder.
A readthedocs documentation will be available soon.
If you use 3D Bounding Box Annotation Toolbox in your research, please cite the following papers:
@inproceedings{zimmermann20193d,
title={3D BAT: A Semi-Automatic, Web-based 3D Annotation Toolbox for Full-Surround, Multi-Modal Data Streams},
author={Zimmer, Walter and Rangesh, Akshay and Trivedi, Mohan M.},
booktitle={2019 IEEE Intelligent Vehicles Symposium (IV)},
pages={1--8},
year={2019},
organization={IEEE}
}
@inproceedings{cress2022a9,
author={CreΓ, Christian and Zimmer, Walter and Strand, Leah and Fortkord, Maximilian and Dai, Siyi and Lakshminarasimhan, Venkatnarayanan and Knoll, Alois},
booktitle={2022 IEEE Intelligent Vehicles Symposium (IV)},
title={A9-Dataset: Multi-Sensor Infrastructure-Based Dataset for Mobility Research},
year={2022},
volume={},
number={},
pages={965-970},
doi={10.1109/IV51971.2022.9827401}
}
@inproceedings{zimmer2023tumtraf,
title={TUMTraf Intersection Dataset: All You Need for Urban 3D Camera-LiDAR Roadside Perception [Best Student Paper Award]},
author={Zimmer, Walter and Cre{\ss}, Christian and Nguyen, Huu Tung and Knoll, Alois C},
publisher = {IEEE},
booktitle={2023 IEEE Intelligent Transportation Systems ITSC},
year={2023}
}
@inproceedings{zimmer2024tumtrafv2x,
title={TUMTraf V2X Cooperative Perception Dataset},
author={Zimmer, Walter and Wardana, Gerhard Arya and Sritharan, Suren and Zhou, Xingcheng and Song, Rui and Knoll, Alois C.},
publisher={IEEE/CVF},
booktitle={2024 IEEE/CVF International Conference on Computer Vision and Pattern Recognition (CVPR)},
year={2024}
}
Copyright Β© 2019 The Regents of the University of California
All Rights Reserved. Permission to copy, modify, and distribute this tool for educational, research and non-profit purposes, without fee, and without a written agreement is hereby granted, provided that the above copyright notice, this paragraph and the following three paragraphs appear in all copies. Permission to make commercial use of this software may be obtained by contacting:
Office of Innovation and Commercialization
9500 Gilman Drive, Mail Code 0910
University of California
La Jolla, CA 92093-0910
(858) 534-5815
innovation@ucsd.edu
This tool is copyrighted by The Regents of the University of California. The code is supplied βas isβ, without any accompanying services from The Regents. The Regents does not warrant that the operation of the tool will be uninterrupted or error-free. The end-user understands that the tool was developed for research purposes and is advised not to rely exclusively on the tool for any reason.
IN NO EVENT SHALL THE UNIVERSITY OF CALIFORNIA BE LIABLE TO ANY PARTY FOR DIRECT, INDIRECT, SPECIAL, INCIDENTAL, OR CONSEQUENTIAL DAMAGES, INCLUDING LOST PROFITS, ARISING OUT OF THE USE OF THIS TOOL, EVEN IF THE UNIVERSITY OF CALIFORNIA HAS BEEN ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. THE UNIVERSITY OF CALIFORNIA SPECIFICALLY DISCLAIMS ANY WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE. THE TOOL PROVIDED HEREUNDER IS ON AN βAS ISβ BASIS, AND THE UNIVERSITY OF CALIFORNIA HAS NO OBLIGATIONS TO PROVIDE MAINTENANCE, SUPPORT, UPDATES, ENHANCEMENTS, OR MODIFICATIONS.