MonoRUn: Monocular 3D Object Detection by Reconstruction and Uncertainty Propagation. CVPR 2021. [paper] Hansheng Chen, Yuyao Huang, Wei Tian*, Zhong Gao, Lu Xiong. (*Corresponding author: Wei Tian.)

This repository is the PyTorch implementation for MonoRUn. The codes are based on MMDetection and MMDetection3D, although we use our own data formats. The PnP C++ codes are modified from PVNet.


Data preparation

Download the official KITTI 3D object dataset, including left color images, calibration files and training labels.

Download the train/val/test image lists [Google Drive | Baidu Pan, password: cj4u]. For training with LiDAR supervision, download the preprocessed object coordinate maps [Google Drive | Baidu Pan, password: fp3h].

Extract the downloaded archives according to the following folder structure. It is recommended to symlink the dataset root to $MonoRUn_ROOT/data. If your folder structure is different, you may need to change the corresponding paths in config files.

├── configs
├── monorun
├── tools
├── data
│   ├── kitti
│   │   ├── testing
│   │   │   ├── calib
│   │   │   ├── image_2
│   │   │   └── test_list.txt
│   │   └── training
│   │       ├── calib
│   │       ├── image_2
│   │       ├── label_2
│   │       ├── obj_crd
│   │       ├── mono3dsplit_train_list.txt
│   │       ├── mono3dsplit_val_list.txt
│   │       └── trainval_list.txt

Run the preparation script to generate image metas:

cd $MonoRUn_ROOT
python tools/


cd $MonoRUn_ROOT

To train without LiDAR supervision:

python configs/ --gpu-ids 0 1

where --gpu-ids 0 1 specifies the GPU IDs. In the paper we use two GPUs for distributed training. The number of GPUs affects the mini-batch size. You may change the samples_per_gpu option in the config file to vary the number of images per GPU. If you encounter out of memory issue, add the argument --seed 0 --deterministic to save GPU memory.

To train with LiDAR supervision:

python configs/ --gpu-ids 0 1

To view other training options:

python -h

By default, logs and checkpoints will be saved to $MonoRUn_ROOT/work_dirs. You can run TensorBoard to plot the logs:

tensorboard --logdir $MonoRUn_ROOT/work_dirs

The above configs use the 3712-image split for training and the other split for validating. If you want to train on the full training set (train-val), use the config files with _trainval postfix.


You can download the pretrained models:

  • kitti_multiclass.pth [Google Drive | Baidu Pan, password: 6bih] trained on KITTI training split
  • kitti_multiclass_lidar_supv.pth [Google Drive | Baidu Pan, password: nmdb] trained on KITTI training split
  • kitti_multiclass_lidar_supv_trainval.pth [Google Drive | Baidu Pan, password: hg2r] trained on KITTI train-val

To test and evaluate on the validation set using config at $CONFIG_PATH and checkpoint at $CPT_PATH:

python $CONFIG_PATH $CPT_PATH --val-set --gpu-ids 0

To test on the test set and save detection results to $RESULT_DIR:

python $CONFIG_PATH $CPT_PATH --result-dir $RESULT_DIR --gpu-ids 0

You can append the argument --show-dir $SHOW_DIR to save visualized results.

To view other testing options:

python -h

Note: the training and testing scripts in the root directory are wrappers for the original scripts taken from MMDetection, which can be found in $MonoRUn_ROOT/tools. For advanced usage, please refer to the official MMDetection docs.


We provide a demo script to perform inference on images in a directory and save the visualized results. Example:

python demo/ $KITTI_RAW_DIR/2011_09_30/2011_09_30_drive_0027_sync/image_02/data configs/ checkpoints/kitti_multiclass_lidar_supv_trainval.pth --calib demo/calib.csv --show-dir show/2011_09_30_drive_0027


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

  author = {Hansheng Chen and Yuyao Huang and Wei Tian and Zhong Gao and Lu Xiong}, 
  title = {MonoRUn: Monocular 3D Object Detection by Reconstruction and Uncertainty Propagation}, 
  booktitle = {IEEE Conference on Computer Vision and Pattern Recognition (CVPR)}, 
  year = {2021}