CenterFusion
This repository contains the implementation of CenterFusion: Center-based Radar and Camera Fusion for 3D Object Detection.
Citing CenterFusion
If you find CenterFusion useful in your research, please consider citing:
CenterFusion: Center-based Radar and Camera Fusion for 3D Object Detection
Ramin Nabati, Hairong Qi
@article{nabati2020centerfusion,
title={CenterFusion: Center-based Radar and Camera Fusion for 3D Object Detection},
author={Nabati, Ramin and Qi, Hairong},
journal={arXiv preprint arXiv:2011.04841},
year={2020}
}
Contents
- Introduction
- Results
- Installation
- Dataset Preparation
- Pretrained Models
- Training
- Testing
- References
- License
Introduction
We focus on the problem of radar and camera sensor fusion and propose a middle-fusion approach to exploit both radar and camera data for 3D object detection. Our method, called CenterFusion, first uses a center point detection network to detect objects by identifying their center points on the image. It then solves the key data association problem using a novel frustum-based method to associate the radar detections to their corresponding object's center point. The associated radar detections are used to generate radar-based feature maps to complement the image features, and regress to object properties such as depth, rotation and velocity. We evaluate CenterFusion on the challenging nuScenes dataset, where it improves the overall nuScenes Detection Score (NDS) of the state-of-the-art camera-based algorithm by more than 12%. We further show that CenterFusion significantly improves the velocity estimation accuracy without using any additional temporal information.
Results
-
Overall results:
Dataset NDS mAP mATE mASE mAOE mAVE mAAE nuScenes Test 0.449 0.326 0.631 0.261 0.516 0.614 0.115 nuScenes Val 0.453 0.332 0.649 0.263 0.535 0.540 0.142 -
Per-class mAP:
Dataset Car Truck Bus Trailer Const. Pedest. Motor. Bicycle Traff. Barrier nuScenes Test 0.509 0.258 0.234 0.235 0.077 0.370 0.314 0.201 0.575 0.484 nuScenes Val 0.524 0.265 0.362 0.154 0.055 0.389 0.305 0.229 0.563 0.470 -
Qualitative results:
Installation
The code has been tested on Ubuntu 16.04 and CentOS 7 with Python 3.7, CUDA 10.0 and PyTorch 1.2. For installation, follow these steps:
-
Create a new virtual environment (optional):
mkvirtualenv centerfusion
-
Install PyTorch:
pip install torch torchvision
-
Install COCOAPI:
pip install cython; pip install -U 'git+https://github.com/cocodataset/cocoapi.git#subdirectory=PythonAPI'
-
Clone the CenterFusion repository with the
--recursive
option. We'll call the directory that you cloned CenterFusion intoCF_ROOT
:CF_ROOT=/path/to/CenterFusion git clone --recursive https://github.com/mrnabati/CenterFusion.git $CF_ROOT
-
Install the requirements:
cd $CF_ROOT pip install -r requirements.txt
-
Build the deformable convolution library:
cd $CF_ROOT/src/lib/model/networks/DCNv2 ./make.sh
Dataset Preparation
-
Download the nuScenes dataset from nuScenes website.
-
Extract the downloaded files in the
${CF_ROOT}\data\nuscenes
directory. You should have the following directory structure after extraction:${CF_ROOT} `-- data `-- nuscenes |-- maps |-- samples | |-- CAM_BACK | | | -- xxx.jpg | | ` -- ... | |-- CAM_BACK_LEFT | |-- CAM_BACK_RIGHT | |-- CAM_FRONT | |-- CAM_FRONT_LEFT | |-- CAM_FRONT_RIGHT | |-- RADAR_BACK_LEFT | | | -- xxx.pcd | | ` -- ... | |-- RADAR_BACK_RIGHT | |-- RADAR_FRON | |-- RADAR_FRONT_LEFT | `-- RADAR_FRONT_RIGHT |-- sweeps |-- v1.0-mini `-- v1.0-trainval
-
Run the
convert_nuScenes.py
script to convet the nuScenes dataset to COCO format:cd $CF_ROOT/src/tools python convert_nuScenes.py
Pretrained Models
The pre-trained CenterFusion model and the baseline CenterNet model can be downloaded from the links below:
model | epochs | GPUs | Backbone | Val NDS | Val mAP | Test NDS | Test mAP |
---|---|---|---|---|---|---|---|
centerfusion_e60 | 60 | 2x Nvidia Quadro P5000 | DLA | 0.453 | 0.332 | 0.449 | 0.326 |
centernet_baseline_e170 | 170 | 2x Nvidia Quadro P5000 | DLA | 0.328 | 0.306 | - | - |
Notes: |
- The centernet_baseline_e170 model is obtained by starting from the original CenterNet 3D detection model (nuScenes_3Ddetection_e140) and training the velocity and attributes heads for 30 epochs.
Training
The $CF_ROOT/experiments/train.sh
script can be used to train the network:
cd $CF_ROOT
bash experiments/train.sh
The --train_split
parameter determines the training set, which could be mini_train
or train
. the --load_model
parameter can be set to continue training from a pretrained model, or removed to start training from scratch. You can modify the parameters in the script as needed, or add more supported parameters from $CF_ROOT/src/lib/opts.py
.
Testing
Download the pre-trained model into the $CF_ROOT/models
directory and use the $CF_ROOT/experiments/test.sh
script to run the evaluation:
cd $CF_ROOT
bash experiments/test.sh
Make sure the --load_model
parameter in the script provides the path to the downloaded pre-trained model. The --val_split
parameter determines the validation set, which could be mini_val
, val
or test
. You can adjust the other parameters as needed, or add more supported parameters from $CF_ROOT/src/lib/opts.py
.
References
The following works have been used by CenterFusion:
@inproceedings{zhou2019objects,
title={Objects as Points},
author={Zhou, Xingyi and Wang, Dequan and Kr{\"a}henb{\"u}hl, Philipp},
booktitle={arXiv preprint arXiv:1904.07850},
year={2019}
}
@article{zhou2020tracking,
title={Tracking Objects as Points},
author={Zhou, Xingyi and Koltun, Vladlen and Kr{\"a}henb{\"u}hl, Philipp},
journal={ECCV},
year={2020}
}
@inproceedings{nuscenes2019,
title={{nuScenes}: A multimodal dataset for autonomous driving},
author={Holger Caesar and Varun Bankiti and Alex H. Lang and Sourabh Vora and Venice Erin Liong and Qiang Xu and Anush Krishnan and Yu Pan and Giancarlo Baldan and Oscar Beijbom},
booktitle={CVPR},
year={2020}
}
License
CenterFusion is based on CenterNet and is released under the MIT License. See NOTICE for license information on other libraries used in this project.