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.
- Video demo (KITTI)
- Paper (ICCV 2021)
- Paper (WACV 2023)
- KITTI benchmark (ICCV 2021)
- KITTI benchmark (WACV 2023)
- KITTI
- WAYMO
- 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.
Organize the downloaded files as follows:
├── projects │ ├── MonoRCNN │ │ ├── output │ │ │ ├── model │ │ │ ├── log.txt │ │ │ ├── ... 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).
cd projects/MonoRCNN ./main.py --config-file config/MonoRCNN_KITTI.yaml --num-gpus 1 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}, }