STNet: An End-to-End Generative Framework for Synthesizing Spatiotemporal Super-Resolution Volumes

Pytorch implementation for STNet: An End-to-End Generative Framework for Synthesizing Spatiotemporal Super-Resolution Volumes.

Prerequisites

Linux
CUDA >= 10.0
Python >= 3.7
Numpy
Skimage
Pytorch >= 1.0

Data format

The volume at each time step is saved as a .dat file with the little-endian format. The data is stored in column-major order, that is, z-axis goes first, then y-axis, finally x-axis.

Training models

cd Code

training

python3 main.py --mode 'train' --dataset 'Vortex'

inference

python3 main.py --mode 'inf' --dataset 'Vortex'

Citation

@article{han2021stnet,
  title={STNet: An end-to-end generative framework for synthesizing spatiotemporal super-resolution volumes},
  author={Han, Jun and Zheng, Hao and Chen, Danny Z and Wang, Chaoli},
  journal={IEEE Transactions on Visualization and Computer Graphics},
  volume={28},
  number={1},
  pages={270--280},
  year={2021}
}

Acknowledgements