Learned holographic light transport

Abstract

Computer-Generated Holography (CGH) algorithms often fall short in matching simulations with results from a physical holographic display. Our work addresses this mismatch by learning the holographic light transport in holographic displays. Using a camera and a holographic display, we capture the image reconstructions of optimized holograms that rely on ideal simulations to generate a dataset. Inspired by the ideal simulations, we learn a complex-valued convolution kernel that can propagate given holograms to captured photographs in our dataset. Our method can dramatically improve simulation accuracy and image quality in holographic displays while paving the way for physically informed learning approaches.

Metameric Varifocal Holograms

“…A differentiable PyTorch implementation of this model is readily available in the odak library [3] . Using this library and work by Kavaklı et al [26], it is also possible to optimise this H operator in a camera-in-the-loop fashion to produce the best possible output on an actual holographic display. In our pipeline, we use an H operator optimised in this way to best suit our display hardware.…”

Perceptually guided computer-generated holography

“…14 We call this new pipeline a metameric varifocal CGH and built it using our holography simulation library 15 and learned light transport method. 16 There are three primary parts of our pipeline these are as follows:…”

Metameric Varifocal Holography

“…A differentiable PyTorch implementation of this model is readily available in the odak library [3] . Using this library and work by Kavaklı et al [27], it is also possible to optimise this H operator in a camerain-the-loop fashion to produce the best possible output on an actual holographic display. In our pipeline, we use an H operator optimised in this way to best suit our display hardware.…”