SIGNET: Efficient Neural Representations for Light Fields

This repository contains the demo code for SIGNET: Efficient Neural Representations for Light Fields, published at ICCV 2021. We provide the Python implementation of Gegenbauer embedding as well as the network used to encode the light fields.

Requirements

• CUDA
• PyTorch
• Numpy
• PIL

Demo

To decode an image at light field view point (u, v), please run

• python demo_decode.py -u [u] -v [v] --scene [scene_name]
• u and v are integers within the range [0, 16], specifying the viewpoint coordinates in the original light field We provide the pretrained weights for scenes "lego" and "tarot" in the encoded_weights folder.

Related Publication

Please refer to https://augmentariumlab.github.io/SIGNET for our paper published in ICCV 2021: "SIGNET: Efficient Neural Representations for Light Fields".

References

If you use this in your research, please reference it as:

@inproceedings{Feng2021SIGNET,
  author={Feng, Brandon Y. and Varshney, Amitabh},
  booktitle={Proceedings of the International Conference on Computer Vision (ICCV 2021)},
  title={SIGNET: Efficient Neural Representations for Light Fields},
  year={2021},
}

or

Brandon Y. Feng and Amitabh Varshney. 2021. SIGNET: Efficient Neural Representations for Light Fields. International Conference on Computer Vision (ICCV 2021).