Official implementation for the paper "SAPE: Spatially-Adaptive Progressive Encoding for Neural Optimization".
Create an anaconda environment and install Pytorch. Install other dependencies:
conda env update --file environment.yml
Running examples:
python tasks_func_1d.py
python tasks_image_2d.py <path to an image file>
python tasks_silhouette_2d.py <path to a silhouette image file>
python tasks_occupancy_3d.py <path to a mesh file>
See ./assets directory for possible input files.
Models and other outputs (images, optimization animation, etc.) will be saved under ./assets/checkpoints/<task_name>/<file_name>
If you find this code useful for your research, please cite our paper.
@inproceedings{hertz2021sape,
title={SAPE: Spatially-Adaptive Progressive Encoding for Neural Optimization},
author={Hertz, Amir and Perel, Or and Giryes, Raja and Sorkine-Hornung, Olga and Cohen-Or, Daniel},
booktitle={Thirty-Fifth Conference on Neural Information Processing Systems},
year={2021}
}