Official model development kit for CODa. We strongly recommend using this repository to run our pretrained models and train on custom datasets. Thanks to the authors of ST3D++ and OpenPCDet from whom this repository was adapted from.
Please refer to INSTALL.md for the installation.
Please refer to GETTING_STARTED.md to learn more usage about this project.
Our code is released under the Apache 2.0 license.
If you find our work useful in your research, please consider citing our work:
@inproceedings{zhang2023utcoda,
title={Towards Robust 3D Robot Perception in Urban Environments: The UT Campus Object Dataset},
author={Arthur Zhang and Chaitanya Eranki and Christina Zhang and Raymond Hong and Pranav Kalyani and Lochana Kalyanaraman and Arsh Gamare and Maria Esteva and Joydeep Biswas },
booktitle={},
year={2023}
}
@data{T8/BBOQMV_2023,
author = {Zhang, Arthur and Eranki, Chaitanya and Zhang, Christina and Hong, Raymond and Kalyani, Pranav and Kalyanaraman, Lochana and Gamare, Arsh and Bagad, Arnav and Esteva, Maria and Biswas, Joydeep},
publisher = {Texas Data Repository},
title = {{UT Campus Object Dataset (CODa)}},
year = {2023},
version = {DRAFT VERSION},
doi = {10.18738/T8/BBOQMV},
url = {https://doi.org/10.18738/T8/BBOQMV}
}
Our code is heavily based on OpenPCDet v0.3. Thanks OpenPCDet Development Team for their awesome codebase.
Thank you to the authors of ST3D++ or OpenPCDet for an awesome codebase!
@article{yang2021st3d++,
title={ST3D++: Denoised Self-training for Unsupervised Domain Adaptation on 3D Object Detection},
author={Yang, Jihan and Shi, Shaoshuai and Wang, Zhe and Li, Hongsheng and Qi, Xiaojuan},
journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},
year={2022}
}
@misc{openpcdet2020,
title={OpenPCDet: An Open-source Toolbox for 3D Object Detection from Point Clouds},
author={OpenPCDet Development Team},
howpublished = {\url{https://github.com/open-mmlab/OpenPCDet}},
year={2020}
}