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MD3D: Mixture-Density-based 3D Object Detection in Point Clouds (IEEE Access)

This is the official implementation of MD3D: Mixture-Density-based 3D Object Detection in Point Clouds (IEEE Access).

<p align="center"> <img src="docs/md3d_architecture.png" width="90%"> </p>

Installation & Training

README_OpenPCDet.md

Correction of typos

<p align="center"> <img src="docs/typo.png" width="90%"> </p>

Citation

If you find this code useful in your research, please consider citing our work:

@article{choi2022md3d,
  author={Choi, Jaeseok and Song, Yeji and Kim, Yerim and Yoo, Jaeyoung and Kwak, Nojun},
  journal={IEEE Access}, 
  title={MD3D: Mixture-Density-Based 3D Object Detection in Point Clouds}, 
  year={2022},
  volume={10},
  number={},
  pages={104011-104022},
  doi={10.1109/ACCESS.2022.3210108}
}

Acknowledgement

This work is built upon the OpenPCDet, an open source toolbox for LiDAR-based 3D scene perception. Please refer to the official github repository for more information.
Parts of our code refer to MDOD and CenterPoint.