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UT Campus Object Dataset (CODa): Models for 3D Object Detection

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UT Campus Object Dataset (CODa) Object Detection Models

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.

Sequence 0 Clip

Installation

Please refer to INSTALL.md for the installation.

Getting Started

Quicksetup with Docker

Please refer to DOCKER.md to learn about how to pull our prebuilt docker images to deploy our pretrained 3D object detection models.

Full Setup

Please refer to GETTING_STARTED.md to learn more about how to use this project.

License

Our code is released under the Apache 2.0 license.

Paper Citation

If you find our work useful in your research, please consider citing our paper and dataset:

@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}
}

Dataset Citation

@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}
}

Acknowledgement

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}
}

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