Monocular 3D Object Detection: An Extrinsic Parameter Free Approach

This repository is the official implementation of our paper. For more details, please see our paper.

Introduction

MonoEF is a real-time monocular 3D object detector for autonomous driving.
Part of the code comes from SMOKE, CenterNet, maskrcnn-benchmark, and Detectron2.

Requirements

All codes are tested under the following environment:

• Ubuntu 16.04
• Python 3.7
• Pytorch 1.3.1
• CUDA 10.0

Dataset

We train and test our model on official KITTI 3D Object Dataset. Please first download the dataset and organize it as following structure:

kitti
│──training
│    ├──calib 
│    ├──label_2 
│    ├──image_2
│    └──ImageSets
└──testing
     ├──calib 
     ├──image_2
     └──ImageSets

Setup

1. We use conda to manage the environment:

conda create -n SMOKE python=3.7

2. Clone this repo:

git clone https://github.com/lzccccc/SMOKE

3. Build codes:

python setup.py build develop

4. Link to dataset directory:

mkdir datasets
ln -s /path_to_kitti_dataset datasets/kitti

Getting started

First check the config file under configs/.

We train the model on 4 GPUs with 32 batch size:

python tools/plain_train_net.py --num-gpus 4 --config-file "configs/smoke_gn_vector.yaml"

For single GPU training, simply run:

python tools/plain_train_net.py --config-file "configs/smoke_gn_vector.yaml"

We currently only support single GPU testing:

python tools/plain_train_net.py --eval-only --config-file "configs/smoke_gn_vector.yaml"

Acknowledgement

SMOKE

CenterNet

maskrcnn-benchmark

Detectron2

Citations

Please cite our paper if you find MonoEF is helpful for your research.

@inproceedings{zhou2021monocular,
  title={Monocular 3d object detection: An extrinsic parameter free approach},
  author={Zhou, Yunsong and He, Yuan and Zhu, Hongzi and Wang, Cheng and Li, Hongyang and Jiang, Qinhong},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={7556--7566},
  year={2021}
}