Configurable Learned Holography
arxiv(2024)
摘要
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.
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