Radar-Camera Fusion Dense Prediction Transformer
Official implementation of "RCDPT: Radar-Camera Fusion Dense Prediction Transformer" (https://arxiv.org/abs/2211.02432), an accepted paper of ICASSP2023.
Please check Dockerfile for environment settings and python packages
Or you could directly use the pre-built docker image with tag 'lochenchou/det:mde' from docker hub.
For generating Multi-channel Enhanced Radar (MER), which is the radar format used in the experiment, please follow the instructions of RC-PDA (https://github.com/longyunf/rc-pda).
Please follow the steps in gen_interpolation.py in DORN_radar repo (https://github.com/lochenchou/DORN_radar) to generate 5 frames sparse lidar as the golden ground truth to evaluate with during the evaluation step.
Directly call 'train.py' with dataset paths for training the baseline model and the proposed model on nuScenes.
If you find this work useful in your research, please consider citing:
@inproceedings{RCDPT,
author={Lo, Chen-Chou and Vandewalle, Patrick},
booktitle={Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2023)},
title={RCDPT: Radar-Camera fusion Dense Prediction Transformer},
year={2023},
}