MonoGround

Officail PyTorch implementation of the paper: "MonoGround: Detecting Monocular 3D Objects from the Ground".

Installation

Please see INSTALL.md.

Get started

To verify the results of the trained model, please run:

python tools/plain_train_net.py --batch_size 8 --config runs/monoground.yaml --ckpt /path/to/model --eval --output ./tmp

To train the model by yourself, please run:

python tools/plain_train_net.py --batch_size 8 --config runs/monoground.yaml --output ./tmp

Model and log

We provide the trained model on KITTI and corresponding logs.

Model	Log	AP easy	AP mod	AP hard
Google/Baidu	Google/Baidu	25.24	18.69	15.58

Exp on NuScenes

We also tested our method on the NuScenes dataset. Please see NuScenes.md for details.

Citation

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

@inproceedings{qin2022monoground,
  title={MonoGround: Detecting Monocular 3D Objects From the Ground},
  author={Qin, Zequn and Li, Xi},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={3793--3802},
  year={2022}
}

Acknowlegment

The code is heavily borrowed from MonoFlex and SMOKE and thanks for their contribution.