Focal Surface Holographic Light Transport using Learned Spatially Adaptive Convolutions

TL;DR

This paper introduces a learned light transport model for computer-generated holography that simplifies the process by using a focal surface, reducing computation time and enabling faster hologram optimization.

Contribution

The work proposes a novel focal surface approach combined with spatially adaptive convolutions to efficiently model light transport in holography, replacing multiple plane simulations.

Findings

Reduces hologram optimization time by up to 1.5x

Enables single-inference light propagation to a focal surface

Facilitates faster hologram dataset generation

Abstract

Computer-Generated Holography (CGH) is a set of algorithmic methods for identifying holograms that reconstruct Three-Dimensional (3D) scenes in holographic displays. CGH algorithms decompose 3D scenes into multiplanes at different depth levels and rely on simulations of light that propagated from a source plane to a targeted plane. Thus, for n planes, CGH typically optimizes holograms using n plane-to-plane light transport simulations, leading to major time and computational demands. Our work replaces multiple planes with a focal surface and introduces a learned light transport model that could propagate a light field from a source plane to the focal surface in a single inference. Our learned light transport model leverages spatially adaptive convolution to achieve depth-varying propagation demanded by targeted focal surfaces. The proposed model reduces the hologram optimization process up to 1.5x, which contributes to hologram dataset generation and the training of future learned CGH models.