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FPV_RCNN
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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.
