Awesome

GUPNet++

This is the official implementation of "GUPNet++: Geometry Uncertainty Propagation Network for Monocular 3D Object Detection".

citation

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

@article{lu2024gupnet++,
title={Gupnet++: geometry uncertainty propagation network for monocular 3D object detection},
author={Lu, Yan and Ma, Xinzhu and Yang, Lei and Zhang, Tianzhu and Liu, Yating and Chu, Qi and He, Tong and Li, Yonghui and Ouyang,  Wanli},
journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},
year={2024},
publisher={IEEE}
}
@article{lu2021geometry,
title={Geometry Uncertainty Projection Network for Monocular 3D Object Detection},
author={Lu, Yan and Ma, Xinzhu and Yang, Lei and Zhang, Tianzhu and Liu, Yating and Chu, Qi and Yan, Junjie and Ouyang, Wanli},
journal={arXiv preprint arXiv:2107.13774},year={2021}}

Usage

Installation

This project is based on mmdetection3d repository. You can refer to the original mmdetection3d README to install the requirements English | 简体中文. Here we provide our accurate steps corresponding to our experiment environments with specific version packages:

1. install mmcv

    pip install mmcv-full==1.6.0
   

2. install mmdetection

    git clone https://github.com/open-mmlab/mmdetection.git
    cd mmdetection
    git checkout v2.24.0  # switch to v2.24.0 branch (2.25.0 is also ok)
    pip install -r requirements/build.txt
    pip install -v -e .  #
    cd ..
   

3. install mmsegmentaion

    pip install mmsegmentation==0.26.0
   

4. install mmdetection3d (current repo)

    git clone https://github.com/SuperMHP/GUPNet_Plus.git
    cd GUPNet_Plus
    pip install -v -e .
   

5. Downloading datasets.

   KITTI, including left color images, camera calibration matrices and training labels.

   NuScenes, including Mini, Trainval, Test of Full dataset (v1.0).

6. Putting the datasets as following directory

   updating in recent days.

Train

KITTI training for evaluation set

# PyTorch DDP
CUDA_VISIBLE_DEVICES=0,1,2,3 bash tools/dist_train.sh configs/gupnet_plus/gupnet_plus_dla34_kitti.py

# Slurm
GPUS=4 GPUS_PER_NODE=4 bash tools/slurm_train.sh YOUR_PARTITION_NAME configs/gupnet_plus/gupnet_plus_dla34_kitti.py

KITTI training for test set

# PyTorch DDP
CUDA_VISIBLE_DEVICES=0,1,2,3 bash tools/dist_train.sh configs/gupnet_plus/gupnet_plus_dla34_kitti_trainval.py

# Slurm
GPUS=4 GPUS_PER_NODE=4 bash tools/slurm_train.sh YOUR_PARTITION_NAME configs/gupnet_plus/gupnet_plus_dla34_kitti_trainval.py

NuScenes training for evaluation set (DLA34)

# PyTorch DDP
CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 bash tools/dist_train.sh configs/gupnet_plus/gupnet_plus_dla34_nuscenes.py

# Slurm
GPUS=8 GPUS_PER_NODE=8 bash tools/dist_train.sh configs/gupnet_plus/gupnet_plus_dla34_nuscenes.py

NuScenes training for evaluation set (HGLS104)

# PyTorch DDP
## node 1
MASTER_ADDR=YOUR_MASTER_ADDR NNODES=2 NODE_RANK=0 CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 bash tools/dist_train.sh configs/gupnet_plus/gupnet_plus_hgls104_nuscenes.py
## node 2
MASTER_ADDR=YOUR_MASTER_ADDR NNODES=2 NODE_RANK=1 CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 bash tools/dist_train.sh configs/gupnet_plus/gupnet_plus_hgls104_nuscenes.py

# Slurm
GPUS=16 GPUS_PER_NODE=8 bash tools/dist_train.sh configs/gupnet_plus/gupnet_plus_hgls104_nuscenes.py

NuScenes training for test set (DLA34)

coming soon

Test

KITTI testing for evaluation set

# PyTorch DDP
CUDA_VISIBLE_DEVICES=0,1,2,3 bash tools/dist_test.sh configs/gupnet_plus/gupnet_plus_dla34_kitti.py XXXXXXX/your_model.pth --eval mAP

# Slurm
GPUS=4 GPUS_PER_NODE=4 bash tools/slurm_test.sh YOUR_PARTITION_NAME configs/gupnet_plus/gupnet_plus_dla34_kitti.py XXXXXXX/your_model.pth --eval mAP

KITTI testing for test set

# 1. PyTorch DDP
CUDA_VISIBLE_DEVICES=0,1,2,3 bash tools/dist_test.sh configs/gupnet_plus/gupnet_plus_dla34_kitti_trainval.py XXXXXXX/your_model.pth --format-only --eval-options 'pklfile_prefix=results/kitti_results/' 'submission_prefix=results/kitti_results/'

# 2. Slurm
GPUS=4 GPUS_PER_NODE=4 bash bash tools/slurn_test.sh configs/gupnet_plus/gupnet_plus_dla34_kitti_trainval.py XXXXXXX/your_model.pth --format-only --eval-options 'pklfile_prefix=results/kitti_results/' 'submission_prefix=results/kitti_results/'

# 3. zip files
zip -r -j submit.zip results/kitti_results/img_bbox

#4. submitting submit.zip on KITTI web

Checkpoints

coming soon

Contact

If you have any question about this project, please feel free to contact yan.lu1@sydney.edu.au or luyan@pjlab.org.cn.