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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