The paper is under review: https://arxiv.org/abs/2407.09299
We recommend you to install the environment with environment.yaml.
conda env create --file=environment.yaml
Download KAIST dataset from https://github.com/SoonminHwang/rgbt-ped-detection.
Download FLIR dataset from https://www.flir.com/oem/adas/adas-dataset-form/.
VQGAN can be downloaded from https://github.com/CompVis/latent-diffusion.
Name | Note | Link |
---|---|---|
TeVNet | TeVNet checkpoint for KAIST, epoch=0.95k | TeVNet_KAIST.zip |
TeVNet | TeVNet checkpoint for FLIR, epoch=1k | TeVNet_FLIR.zip |
PID | PID checkpoint for KAIST, k1=50, k2=5 | PID_KAIST.zip |
PID | PID checkpoint for FLIR, k1=k2=50 | PID_FLIR.zip |
Use the shellscript to evaluate. indir
is the input directory of visible RGB images, outdir
is the output directory of translated infrared images, config
is the chosen config in configs/latent-diffusion/config.yaml
. We prepare some RGB images in dataset/KAIST
for quick evaluation.
bash run_test_kaist512_vqf8.sh
Prepare corresponding RGB and infrared images with same names in two directories.
cd TeVNet
bash shell/train.sh
Use the shellscript to train. It is recommended to use our pretrained model to accelerate the train process.
bash shell/run_train_kaist512_vqf8.sh
Our code is built upon LDM and HADAR. We thank the authors for their excellent work.
If you find this work helpful in your research, please consider citing our paper:
@inproceedings{Mao2024PIDPD,
title={PID: Physics-Informed Diffusion Model for Infrared Image Generation},
author={Fangyuan Mao and Jilin Mei and Shun Lu and Fuyang Liu and Liang Chen and Fangzhou Zhao and Yu Hu},
year={2024},
url={https://doi.org/10.48550/arXiv.2407.09299}
}
If you have any question, feel free to contact maofangyuan23s@ict.ac.cn .