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<p>Holographic displays deliver realistic visual experiences by reconstructing three-dimensional (3D) light fields. However, widely used RGB-D data offers reliable geometry only in focused regions, lacking light field information in defocused areas, particularly with transparent or reflective surfaces. Existing methods typically utilize coherent diffraction to approximate defocus effects in incoherent perception, introducing ringing artifacts and unnatural depth transitions. We propose a method generating physically consistent complex-valued light fields from multi-layer RGB-D data. Our approach integrates neural rendering and multi-branch phase prediction networks to compute amplitude and phase, while explicitly enforcing wave propagation to preserve coherence and physical consistency. Our simulations achieve an average peak signal-to-noise ratio of 32.35 dB and an average structural similarity index of 0.938. Experiments demonstrate correct defocus relationships and natural defocus blur in scenes with glasses and mirrors. Our approach bridges coherent computation and incoherent perception, addressing physical consistency in 3D holography for next-generation human-computer interfaces.</p>

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Photorealistic 3D Holographic Display with Natural Defocus Effect

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年5月6日

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  • Mi Zhou,
  • Mu Ku Chen,
  • Fei Liu,
  • Mei Shen,
  • Lei Lei,
  • Chaoqun Ma,
  • Xueqian Wang,
  • Jian Song,
  • Haoqian Wang,
  • Kaichen Dong,
  • Chao Zuo,
  • Zihan Geng

摘要

Holographic displays deliver realistic visual experiences by reconstructing three-dimensional (3D) light fields. However, widely used RGB-D data offers reliable geometry only in focused regions, lacking light field information in defocused areas, particularly with transparent or reflective surfaces. Existing methods typically utilize coherent diffraction to approximate defocus effects in incoherent perception, introducing ringing artifacts and unnatural depth transitions. We propose a method generating physically consistent complex-valued light fields from multi-layer RGB-D data. Our approach integrates neural rendering and multi-branch phase prediction networks to compute amplitude and phase, while explicitly enforcing wave propagation to preserve coherence and physical consistency. Our simulations achieve an average peak signal-to-noise ratio of 32.35 dB and an average structural similarity index of 0.938. Experiments demonstrate correct defocus relationships and natural defocus blur in scenes with glasses and mirrors. Our approach bridges coherent computation and incoherent perception, addressing physical consistency in 3D holography for next-generation human-computer interfaces.