CenterFusion

This repository contains the implementation of CenterFusion: Center-based Radar and Camera Fusion for 3D Object Detection.

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:

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:

  1. Create a new virtual environment (optional):

    mkvirtualenv centerfusion  
    
  2. Install PyTorch:

    pip install torch torchvision
    
  3. Install COCOAPI:

    pip install cython; pip install -U 'git+https://github.com/cocodataset/cocoapi.git#subdirectory=PythonAPI'
    
  4. Clone the CenterFusion repository with the --recursive option. We'll call the directory that you cloned CenterFusion into CF_ROOT:

    CF_ROOT=/path/to/CenterFusion
    git clone --recursive https://github.com/mrnabati/CenterFusion.git $CF_ROOT
    
  5. Install the requirements:

    cd $CF_ROOT
    pip install -r requirements.txt
    
  6. Build the deformable convolution library:

    cd $CF_ROOT/src/lib/model/networks/DCNv2
    ./make.sh
    

    Note: If the DCNv2 folder does not exist in the networks directory, it can be downloaded using this command:

    cd $CF_ROOT/src/lib/model/networks
    git clone https://github.com/CharlesShang/DCNv2/
    

Dataset Preparation

  1. Download the nuScenes dataset from nuScenes website.

  2. 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
    
  3. 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}
}

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