Introduction

This repository contains the source code to replicate the results in our paper AFN: Attentional Feedback Network based 3D Terrain Super-Resolution at ACCV 2020, Kyoto.

System/Software Requirements

1. PyTorch: 1.4
2. Nvidia-1080Ti with 11GB of VRAM

Inference

1. To get an inference with our pretrained model on your custom model, please add the test data pairs (aerial image and low-resolution DEM) in the datasets directory.
2. Download the pretrained model from this path.
3. Update the paths accordingly in options/test/test_options.json file.
4. Use following command to test.

python test.py -opt options/test/test_options.py

Train

1. To train the network on your own dataset, please add the training pairs into datasets directory.
2. Update the paths accordingly in options/train/train_options.json file. You can play with the hyperparameters like number of steps: T, number of groups: N in the same file.
3. For training, use follwing command:

python train.py -opt options/train/train_options.py

Citation

If you find this work useful in your research, please consider citing with:

@inproceedings{kubade2020afn,
  title={AFN: Attentional Feedback Network based 3D Terrain Super-Resolution},
  author={Kubade, Ashish and Patel, Diptiben and Sharma, Avinash and Rajan, KS},
  booktitle={Proceedings of the Asian Conference on Computer Vision},
  year={2020}
}