• ubuntu
• docker
• nvidia-docker

1. Get the dataset from https://github.com/kaist-avelab/K-Radar

2. Structure the dataset according to

   K-Radar Data Structure

   .
   |
   +---data/
   |   |
   |   +---kradar/
   |   |   |
   |   |   +---raw/
   |   |   |   |
   |   |   |   +---1/
   |   |   |   |   |
   |   |   |   |   +---cam-front/
   |   |   |   |   |
   |   |   |   |   +---cam-left/
   |   |   |   |   |
   |   |   |   |   +---cam-rear/
   |   |   |   |   |
   |   |   |   |   +---cam-right/
   |   |   |   |   |
   |   |   |   |   +---description.txt
   |   |   |   |   |
   |   |   |   |   +---info_calib/
   |   |   |   |   |
   |   |   |   |   +---info_frames/
   |   |   |   |   |
   |   |   |   |   +---info_label/
   |   |   |   |   |
   |   |   |   |   +---info_label_v2/
   |   |   |   |   |
   |   |   |   |   +---info_matching/
   |   |   |   |   |
   |   |   |   |   +---os1-128/
   |   |   |   |   |
   |   |   |   |   +---os2-64/
   |   |   |   |   |
   |   |   |   |   +---radar_tesseract/
   |   |   |   |   |
   |   |   |   |   +---...
   |   |   |   |
   |   |   |   +---2, 3... 
   
   

docker build -t dprt:0.0.1 .

docker run \
    --name dprt \
    -it \
    --gpus all \
    -e DISPLAY \
    -v /tmp/.X11-unix:/tmp/.X11-unix \
    -v <path to repository>:/app \
    -v <path to data>:/data \
    dprt:0.0.1 bash

python -m dprt.prepare --src /data/kradar/raw/ --cfg /app/config/kradar.json --dst /data/kradar/processed/

python -m dprt.train --src /data/kradar/processed/ --cfg /app/config/kradar.json

python -m dprt.evaluate --src /data/kradar/processed/ --cfg /app/config/kradar.json --checkpoint /app/log/<path to checkpoint>

If DPFT is useful or relevant to your research, please kindly recognize our contributions by citing our paper:

@article{fent2024dpft,
    title={DPFT: Dual Perspective Fusion Transformer for Camera-Radar-based Object Detection}, 
    author={Felix Fent and Andras Palffy and Holger Caesar},
    journal={arXiv preprint arXiv:2404.03015},
    year={2024}
}