/TAF

This is the code for siggrapha paper "An Implicit Neural Representation for the Image Stack: Depth, All in Focus, and High Dynamic Range"

Primary LanguagePythonMIT LicenseMIT

TAF: An Implicit Neural Representation for the Image Stack: Depth, All in Focus, and High Dynamic Range [Siggrapha Aisa 2023]

Installation

Use the following commands with Anaconda to create and activate your environment:

  • conda env create -f environment.yml
  • conda activate TAF

Training

 python train.py --save_path "./results" --base_path "./dataset/scene_1/"

please change the base_path to your own dataset path

Testing

python test.py 

please modify the wieghts path to your own for testing, here we include pre-trained weights in folder “weight” as an example.

BibTeX

@article{wang2023implicit,
  title={An Implicit Neural Representation for the Image Stack: Depth, All in Focus, and High Dynamic Range},
  author={Wang, Chao and Serrano, Ana and Pan, Xingang and Wolski, Krzysztof and Chen, Bin and Myszkowski, Karol and Seidel, Hans-Peter and Theobalt, Christian and Leimk{\"u}hler, Thomas},
  journal={ACM Transactions on Graphics (TOG)},
  volume={42},
  number={6},
  pages={1--11},
  year={2023},
  publisher={ACM New York, NY, USA}
}
}

Acknowledge

This source code is derived from the (https://shnnam.github.io/research/nir/). We really appreciate the contributions of the authors to that repository.