Semi-supervised 3D Object Detection via Temporal Graph Neural Networks

This is the official repo of "Semi-supervised 3D Object Detection via Temporal Graph Neural Networks", 3DV 2022

Project page: https://www.jianrenw.com/SOD-TGNN/
Paper: https://arxiv.org/pdf/2202.00182.pdf

Setup environment

Follow CenterNet to setup environment and install required packages

Generate Pickle file

Follow SECOND to convert h3d/nuScenes dataset to pickle file

Run the training experiment

python run_pipeline_h3d.py CONFIG_PATH --save_dir=SAVE_PATH --work_dir=WORK_DIR --data_path=UNLABLED_DATASET --h3d_data=LABLED_DATASET

Run the testing

python -m torch.distributed.launch --nproc_per_node=4 ./tools/dist_test.py CONFIG_PATH --work_dir=work_dirs/CONFIG_NAME --checkpoint=work_dirs/CONFIG_NAME/latest.pth 

Acknowlegement

This project is not possible without multiple great opensourced codebases. We list some notable examples below.

• CenterNet
• SECOND

SOD-TGNN is deeply influenced by the following projects. Please consider citing the relevant papers.

@article{wang2021sodtgnn,
    title={Semi-supervised 3D Object Detection via Temporal Graph Neural Networks},
    author={Wang, Jianren and Gang, Haiming and Ancha, Siddharth and Chen, Yi-ting and Held, David},
    journal={International Conference on 3D Vision},
    year={2021}
}

@inproceedings{jianren20s3da,
    Author = {Wang, Jianren and Ancha, Siddharth and Chen, Yi-Ting and Held, David},
    Title = {Uncertainty-aware Self-supervised 3D Data Association},
    Booktitle = {IROS},
    Year = {2020}
}