End-to-end learning of 3D phase-only holograms for holographic display

Liang Shi; Beichen Li; Wojciech Matusik

doi:10.1038/s41377-022-00894-6

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End-to-end learning of 3D phase-only holograms for holographic display

Original Articles

- End-to-end learning of 3D phase-only holograms for holographic display
- End-to-end learning of 3D phase-only holograms for holographic display
- Light: Science & Applications 2022年11卷第9期页码：2176-2193
- Affiliations：
  
  1.Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, 32 Vassar St, Cambridge, MA 02139, USA
- Author bio：
  
  Liang Shi (liangs@mit.edu)
  Wojciech Matusik (wojciech@mit.edu)
- Funds：
- DOI：10.1038/s41377-022-00894-6
  中图分类号：
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Liang Shi, Beichen Li, Wojciech Matusik. End-to-end learning of 3D phase-only holograms for holographic display[J]. Light: Science & Applications, 2022,11(9):2176-2193.

Shi, L. et al. End-to-end learning of 3D phase-only holograms for holographic display. Light: Science & Applications, 11, 2176-2193 (2022).
Liang Shi, Beichen Li, Wojciech Matusik. End-to-end learning of 3D phase-only holograms for holographic display[J]. Light: Science & Applications, 2022,11(9):2176-2193. DOI： 10.1038/s41377-022-00894-6.

Shi, L. et al. End-to-end learning of 3D phase-only holograms for holographic display. Light: Science & Applications, 11, 2176-2193 (2022). DOI： 10.1038/s41377-022-00894-6.

摘要

Abstract

Computer-generated holography (CGH) provides volumetric control of coherent wavefront and is fundamental to applications such as volumetric 3D displays, lithography, neural photostimulation, and optical/acoustic trapping. Recently, deep learning-based methods emerged as promising computational paradigms for CGH synthesis that overcome the quality-runtime tradeoff in conventional simulation/optimization-based methods. Yet, the quality of the predicted hologram is intrinsically bounded by the dataset's quality. Here we introduce a new hologram dataset, MIT-CGH-4K-V2, that uses a layered depth image as a data-efficient volumetric 3D input and a two-stage supervised+unsupervised training protocol for direct synthesis of high-quality 3D phase-only holograms. The proposed system also corrects vision aberration, allowing customization for end-users. We experimentally show photorealistic 3D holographic projections and discuss relevant spatial light modulator calibration procedures. Our method runs in real-time on a consumer GPU and 5 FPS on an iPhone 13 Pro, promising drastically enhanced performance for the applications above.

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Keywords

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