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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.

ModelLogAP easyAP modAP hard
Google/BaiduGoogle/Baidu25.2418.6915.58

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.