FPV_RCNN

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# FPV-RCNN: Keypoints Based Deep Feature Fusion for Cooperative Vehicle Detectionof  Autonomous  Driving
## Acknowledgement
This project is highly dependent on repo [OpenPCDet](https://github.com/open-mmlab/OpenPCDet). 
and [SpConv](https://github.com/traveller59/spconv).

## Download dataset
* Download the [COMAP](https://seafile.cloud.uni-hannover.de/f/cb3e9e25646b4119a5e6/?dl=1](https://data.uni-hannover.de:8080/dataset/upload/users/ikg/yuan/cosense3d/COMAP/) dataset and extract all folders
* Download the pre-trained [CIA-SSD checkpoint](https://seafile.cloud.uni-hannover.de/f/37986ccfc8ce46c2b4d2/?dl=1) and store it in a new logging-path
## Setup environment
Tested on ubuntu 16.04 and cuda 10.1
```bash
apt-get update -qq && apt-get install -y software-properties-common git nano

# for compiling spconv
apt-get install -y  libboost-all-dev build-essential libssl-dev

# build python venv and install python packages
cd FPV_RCNN && python -m venv venv && source venv/bin/activate
pip install -r requirements.txt

# build spconv and ops
cd spconv && python setup.py bdist_wheel
cd ./dist && pip install $(basename $./*.whl)
cd ../.. && python setup.py develop
```
## Training and test
Configurations for dataset pre-processing, model, training and testing can all be found in the python file
of folder __cfg__. To train the network with default settings, only the data path _Dataset.root_ 
and the output logging path _Optimization.PATHS['run']_  should be set. The logging path should contain 
the pre-trained CIA-SSD checkpoint. For example, if you want to train fpvrcnn, please configure the
__fusion_pvrcnn_comap.py__ as follows:
```python
# or
self.PATHS = {
        'run': '/path/to/experiments_output/fusion-pvrcnn'
}
```
and then put the cia-ssd checkpoint in the folder __experiments_output__.
### Training
Pass the cfg file name (ex. "fusion_pvrcnn_comap") to the function _cfg_from_py_ in the training 
or testing script, and run
```bash
tools/train_fusion_detector.py
# or
tools/test_fusion_detector.py
```
## Citation
If you find this work useful in your research, please consider cite:
```
@ARTICLE{9682601,
  author={Yuan, Yunshuang and Cheng, Hao and Sester, Monika},
  journal={IEEE Robotics and Automation Letters}, 
  title={Keypoints-Based Deep Feature Fusion for Cooperative Vehicle Detection of Autonomous Driving}, 
  year={2022},
  volume={7},
  number={2},
  pages={3054-3061},
  doi={10.1109/LRA.2022.3143299}}
```
or cite:
```
@crticle{comap,
AUTHOR = {Yunshuang Yuan, Monika Sester},
TITLE = {{COMAP}: A SYNTHETIC DATASET FOR COLLECTIVE MULTI-AGENT PERCEPTION OF AUTONOMOUS DRIVING},
JOURNAL = {The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences},
VOLUME = {XLIII-B2-2021},
YEAR = {2021},
PAGES = {255--263},
URL = {https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLIII-B2-2021/255/2021/},
DOI = {10.5194/isprs-archives-XLIII-B2-2021-255-2021}
}
```
if you want use the COMAP dataset.

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