This is the public repository for "Conditional Neural Holography: A Distance-Adaptive CGH Generator."
Although we plan to refactor the code in the near future, it is currently available for use. Training and evaluation can be performed using train.py and eval.py, respectively. We have employed the DIV2K and Flicker2K datasets, and their paths must be set appropriately for training.
We gratefully acknowledge Peng et al.'s neural-holography repository, which served as an important reference for this work. https://github.com/computational-imaging/neural-holography