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Tensor Holography

Towards Real-time Photorealistic 3D Holography with Deep Neural Networks (TensorHolo V1)
Nature 2021
Project Page | Paper | Data
Liang Shi, Beichen Li, Changil Kim, Petr Kellnhofer, Wojciech Matusik

End-to-end Learning of 3D Phase-only Holograms for Holographic Display (TensorHolo V2)
Light: Science and Applications 2022 [Impact factor: 20.26 (in 2022)]
Project Page | Paper | Data
Liang Shi, Beichen Li, Wojciech Matusik

This repository contains the code to reproduce the results presented in the above papers. Please read the License before using the software.

(New) Related Works

Ergonomic-Centric Holography: Optimizing Realism, Immersion, and Comfort for Holographic Display
Arxiv 2023
Project Page (in preparation) | Paper | Supplement | Code (in preparation)
Liang Shi*, DongHun Ryu*, Wojciech Matusik (* indicates equal contribution)

Getting Started

8/9/2022 Update: TensorHolo V2 code/dataset released.

This code runs with Python 3.8 and Tensorflow 1.15 (NVIDIA-maintained version to support training on the latest NVIDIA GPUS). You can set up a conda environment with the required dependencies using:

conda env create -f environment.yml
conda activate tensorholo
pip install nvidia-pyindex
pip install nvidia-tensorflow[horovod]

Alternatively, set up the following enviroment if you plan to export the model for TensorRT accelerated inference. The following instructions are tested on Ubuntu 20.04 with Python=3.8 CUDA=11.6 and TensorRT=8.4.

# Install CUDA 11.6 (Change to the correct link based on your own system)
wget https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2004/x86_64/cuda-ubuntu2004.pin
sudo mv cuda-ubuntu2004.pin /etc/apt/preferences.d/cuda-repository-pin-600
wget https://developer.download.nvidia.com/compute/cuda/11.6.1/local_installers/cuda-repo-ubuntu2004-11-6-local_11.6.1-510.47.03-1_amd64.deb
sudo dpkg -i cuda-repo-ubuntu2004-11-6-local_11.6.1-510.47.03-1_amd64.deb
sudo apt-key add /var/cuda-repo-ubuntu2004-11-6-local/7fa2af80.pub
sudo apt-get update
sudo apt-get -y install cuda

# Install TensorRT
# Download deb package from NVIDIA
# Replace xx in the os and tag with your package name
os="ubuntuxx04"
tag="cudax.x-trt8.x.x.x-yyyymmdd"
sudo dpkg -i nv-tensorrt-repo-${os}-${tag}_1-1_amd64.deb
sudo apt-key add /var/nv-tensorrt-repo-${os}-${tag}/7fa2af80.pub
sudo apt-get update
sudo apt-get install tensorrt

# Install other relevant packages
sudo apt-get install python3-libnvinfer-dev
sudo apt-get install onnx-graphsurgeon

# Add tensorrt bin path to use trtexec
export PATH=/usr/src/tensorrt/bin:$PATH

# Create a tensorrt environment
conda env create -f environment_trt.yml
conda activate trt
pip install nvidia-pyindex
pip install nvidia-tensorflow[horovod]
pip install nvidia-tensorrt

High-level structure

The code is organized as follows:

Reproducing the experiments of TensorHolo V1

Reproducing the experiments of TensorHolo V2

Citation

If you find our work useful in your research, please cite:

@article{Shi2021:TensorHolography,
    title   = "Towards real-time photorealistic {3D} holography with deep neural
                networks",
    author  = "Shi, Liang and Li, Beichen and Kim, Changil and Kellnhofer, Petr
                and Matusik, Wojciech",
    journal = "Nature",
    volume  =  591,
    number  =  7849,
    pages   = "234--239",
    year    =  2021
}

@article{Shi2022:TensorHolography-v2,
    title    = "End-to-end learning of {3D} phase-only holograms for holographic
                display",
    author   = "Shi, Liang and Li, Beichen and Matusik, Wojciech",
    journal  = "Light Sci Appl",
    volume   =  11,
    number   =  1,
    pages    = "247",
    month    =  aug,
    year     =  2022,
    language = "en"
} 

@misc{Shi2023:EC-H,
    title={Ergonomic-Centric Holography: Optimizing Realism,Immersion, and Comfort for Holographic Display}, 
    author={Liang Shi and Donghun Ryu and Wojciech Matusik},
    year={2023},
    eprint={2306.08138},
    archivePrefix={arXiv},
    primaryClass={cs.GR}
}

License

Our dataset and code, with the exception of the files in "data/example_image", are licensed under a custom license provided by the MIT Technology Licensing Office. By downloading the software, you agree to the terms of this License.