Journal Article10.1145/3355089.3356539
Wirtinger holography for near-eye displays
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TL;DR: The proposed Wirtinger Holography is flexible and facilitates the use of different loss functions, including learned perceptual losses parametrized by deep neural networks, as well as stochastic optimization methods, and extends the framework to render 3D volumetric scenes.
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Abstract: Near-eye displays using holographic projection are emerging as an exciting display approach for virtual and augmented reality at high-resolution without complex optical setups --- shifting optical complexity to computation. While precise phase modulation hardware is becoming available, phase retrieval algorithms are still in their infancy, and holographic display approaches resort to heuristic encoding methods or iterative methods relying on various relaxations. In this work, we depart from such existing approximations and solve the phase retrieval problem for a hologram of a scene at a single depth at a given time by revisiting complex Wirtinger derivatives, also extending our framework to render 3D volumetric scenes. Using Wirtinger derivatives allows us to pose the phase retrieval problem as a quadratic problem which can be minimized with first-order optimization methods. The proposed Wirtinger Holography is flexible and facilitates the use of different loss functions, including learned perceptual losses parametrized by deep neural networks, as well as stochastic optimization methods. We validate this framework by demonstrating holographic reconstructions with an order of magnitude lower error, both in simulation and on an experimental hardware prototype.
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Citations
Augmented reality and virtual reality displays: emerging technologies and future perspectives.
TL;DR: In this article, the basic structures of AR and VR headsets and operation principles of various holographic optical elements (HOEs) and lithography-enabled devices are described, with detailed description and analysis of some state-of-the-art architectures.
Neural holography with camera-in-the-loop training
TL;DR: In this paper, a camera-in-the-loop optimization strategy is used to optimize a hologram directly or train an interpretable model of the optical wave propagation and a neural network architecture that represents the first CGH algorithm capable of generating full-color high quality holographic images at 1080p resolution in real time.
383
Toward the next-generation VR/AR optics: a review of holographic near-eye displays from a human-centric perspective.
Chenliang Chang,Kiseung Bang,Gordon Wetzstein,Byoungho Lee,Liang Gao +4 more
- 20 Nov 2020
TL;DR: Compared with other 3D displays, the holographic display has unique advantages in providing natural depth cues and correcting eye aberrations and holds great promise to be the enabling technology for next-generation VR/AR devices.
350
DeepCGH: 3D computer-generated holography using deep learning.
TL;DR: DeepCGH is introduced, a non-iterative algorithm that relies on a convolutional neural network with unsupervised learning to compute accurate holograms with fixed computational complexity that substantially enhances two-photon absorption and improves performance in photostimulation tasks without requiring additional laser power.
207
Relaxed averaged alternating reflections for diffraction imaging
TL;DR: A relaxation of averaged alternating reflectors and determine the fixed-point set of the related operator in the convex case is proposed and the effectiveness of the algorithm compared to the current state of the art is demonstrated.
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