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MonoRCNN

MonoRCNN is a monocular 3D object detection method for autonomous driving, published at ICCV 2021 and WACV 2023. This project is an implementation of MonoRCNN.

Visualization

KITTI

WAYMO

Installation

Python 3.6
PyTorch 1.5.0
Detectron2 0.1.3

Please use the Detectron2 included in this project. To ignore fully occluded objects during training, build.py, rpn.py, and roi_heads.py have been modified.

Dataset Preparation

KITTI

Model & Log

KITTI val split

Organize the downloaded files as follows:

├── projects
│   ├── MonoRCNN
│   │   ├── output
│   │   │   ├── model
│   │   │   ├── log.txt
│   │   │   ├── ...

Waymo

Test

cd projects/MonoRCNN
./main.py --config-file config/MonoRCNN_KITTI.yaml --num-gpus 1 --resume --eval-only

Set VISUALIZE as True to visualize 3D object detection results (saved in output/evaluation/test/visualization).

Training

cd projects/MonoRCNN
./main.py --config-file config/MonoRCNN_KITTI.yaml --num-gpus 1

Citation

If you find this project useful in your research, please cite:

@inproceedings{MonoRCNN_ICCV21,
    title = {Geometry-based Distance Decomposition for Monocular 3D Object Detection},
    author = {Xuepeng Shi and Qi Ye and 
              Xiaozhi Chen and Chuangrong Chen and 
              Zhixiang Chen and Tae-Kyun Kim},
    booktitle = {ICCV},
    year = {2021},
}

@inproceedings{MonoRCNN_WACV23,
    title = {Multivariate Probabilistic Monocular 3D Object Detection},
    author = {Xuepeng Shi and Zhixiang Chen and Tae-Kyun Kim},
    booktitle = {WACV},
    year = {2023},
}