  In the pursuit of advancing holographic display technology, we face a unique
yet persistent roadblock: the inflexibility of learned holography in adapting
to various hardware configurations. This is due to the variances in the complex
optical components and system settings in existing holographic displays.
Although the emerging learned approaches have enabled rapid and high-quality
hologram generation, any alteration in display hardware still requires a
retraining of the model. Our work introduces a configurable learned model that
interactively computes 3D holograms from RGB-only 2D images for a variety of
holographic displays. The model can be conditioned to predefined hardware
parameters of existing holographic displays such as working wavelengths, pixel
pitch, propagation distance, and peak brightness without having to retrain. In
addition, our model accommodates various hologram types, including conventional
single-color and emerging multi-color holograms that simultaneously use
multiple color primaries in holographic displays. Notably, we enabled our
hologram computations to rely on identifying the correlation between depth
estimation and 3D hologram synthesis tasks within the learning domain for the
first time in the literature. We employ knowledge distillation via a
student-teacher learning strategy to streamline our model for interactive
performance. Achieving up to a 2x speed improvement compared to
state-of-the-art models while consistently generating high-quality 3D holograms
with different hardware configurations.
