Focal Surface Holographic Light Transport using Learned Spatially Adaptive Convolutions

Chuanjun Zheng, Yicheng Zhan, Liang Shi, Ozan Cakmakci, Kaan Akşit

Project Site Arxiv Manuscript Supplementary

Getting started

(0) Requirments

Please make sure to have the right dependencies installed.

pip3 install -r requirements.txt

Install the latest version of Odak.

git clone git@github.com:kaanaksit/odak.git
cd odak
pip3 install -r requirements.txt
pip3 install -e .

(1) Testing

You can start testing using the following syntax:

(1.1) Default test

git clone git@github.com:complight/focal_surface_holographic_light_transport.git
cd focal_surface_holographic_light_transport
python test.py  

After running the script, you can find the output in the test_output directory. The primary result of the test will be the reconstructed image, which will be saved as reconstruction_image.png.

(1.2) Customizing the test

If you would like to test with a different focal surface file or change the output directory, you can specify these as arguments when running the script:

python test.py --focal_surface_filename ./path/to/your/focal_surface.png --hologram_phase_filename ./path/to/your/hologram.png --output_directory ./path/to/output

(2) Training

(2.1) Preparing your dataset

We strongly encourage you to refer to the previous work of our group, multicolor, to generate the dataset based on your own settings. Alternatively, you can directly use odak.learn.wave.multi_color_hologram_optimizer.

(2.2) Revising the settings

Please consult the settings file found in sample_zero.txt, where you will find a list of self descriptive variables that you can modify according to your needs. This way, you can create a new settings file or modify the existing one.

(2.3) Starting training

python main.py 

Support

For more support regarding the code base, please use the issues section of this repository to raise issues and questions.

Citation

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

@inproceedings{zheng2024focalholography,
  title={Focal Surface Holographic Light Transport using Learned Spatially Adaptive Convolutions},
  author={Chuanjun Zheng, Yicheng Zhan, Liang Shi, Ozan Cakmakci, and Kaan Ak{\c{s}}it},
  booktitle = {SIGGRAPH Asia 2024 Technical Communications (SA Technical Communications '24)},
  keywords = {Computer-Generated Holography, Light Transport, Optimization},
  location = {Tokyo, Japan},
  series = {SA '24},
  month={December},
  year={2024},
  doi={https://doi.org/10.1145/3681758.3697989}
}