Seung-Hwan Baek*,Noah Walsh*, Ilya Chugunov, Zheng Shi, Felix Heide
If you find our work useful in your research, please cite:
@article{baek2022centimeter,
title={Centimeter-Wave Free-Space Neural Time-of-Flight Imaging},
author={Baek, Seung-Hwan and Walsh, Noah and Chugunov, Ilya and Shi, Zheng and Heide, Felix},
journal={ACM Transactions on Graphics (TOG)},
year={2022},
publisher={ACM New York, NY}
}
This code is developed using Pytorch on Linux machine. Full frozen environment can be found in 'environment.yml', note some of these libraries are not necessary to run this code.
In the paper we use Hypersim RGB-D dataset as our training data. And they can be easily swtich to any other RGB-D datasets of your choice. See 'dataloader/' folder for more details.
To perform inference on real-world captures, please first download the pre-trained model from here to 'ckpts/' folder, then you can run the 'inference.ipynb' notebook in Jupyter Notebook. The notebook will load the checkpoint and process captured sensor measurements located in 'captures/'. The reconstructed depth will be displayed within the notebook.
We include 'train.sh' for training purpose.
Our code is licensed under BSL-1. By downloading the software, you agree to the terms of this License.
If there is anything unclear, please feel free to reach out to Seung-Hwan at shwbaek[at]postech[dot]ac[dot]kr.