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arXiv · 2401.12537

Motion Hologram: Jointly optimized hologram generation and motion planning for photorealistic and speckle-free 3D displays via reinforcement learning

Zhenxing Dong·Yuye Ling·Yan Li·Yikai Su
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Abstract

Holography is capable of rendering three-dimensional scenes with full-depth control, and delivering transformative experiences across numerous domains, including virtual and augmented reality, education, and communication. However, traditional holography presents 3D scenes with unnatural defocus and severe speckles due to the limited space bandwidth product of the spatial light modulator (SLM). Here, we introduce Motion Hologram, a novel holographic technique to accurately portray photorealistic and speckle-free 3D scenes, by leveraging a single hologram and learnable motion trajectory, which are jointly optimized within the deep reinforcement learning framework. Specifically, we experimentally demonstrated the proposed technique could achieve a 4~5 dB PSNR improvement of focal stacks in comparison with traditional holography and could successfully depict speckle-free, high-fidelity, and full-color 3D displays using only a commercial SLM for the first time. We believe the proposed method promises a new form of holographic displays that will offer immersive viewing experiences for audiences.

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Zhenxing Dong, Yuye Ling, Yan Li, Yikai Su. 2024-01-23. Motion Hologram: Jointly optimized hologram generation and motion planning for photorealistic and speckle-free 3D displays via reinforcement learning. https://arxiv.org/abs/2401.12537

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