• Infrared and Laser Engineering
  • Vol. 51, Issue 1, 20210935 (2022)
Liangcai Cao, Zehao He*, Kexuan Liu, and Xiaomeng Sui
Author Affiliations
  • State Key Laboratory of Precision Measurement Technology and Instrument, Department of Precision Instrument, Tsinghua University, Beijing 100084, China
  • show less
    DOI: 10.3788/IRLA20210935 Cite this Article
    Liangcai Cao, Zehao He, Kexuan Liu, Xiaomeng Sui. Progress and challenges in dynamic holographic 3D display for the metaverse (Invited)[J]. Infrared and Laser Engineering, 2022, 51(1): 20210935 Copy Citation Text show less
    Various 3D depth cues in 3D display
    Fig. 1. Various 3D depth cues in 3D display
    Depth cues provided by different 3D display technologies
    Fig. 2. Depth cues provided by different 3D display technologies
    Recording and reconstruction of (a) optical holography, (b) computer-generated holography and (c) digital holography
    Fig. 3. Recording and reconstruction of (a) optical holography, (b) computer-generated holography and (c) digital holography
    (a) Holographic 3D display system; (b) Holographic 3D reconstructions [59]
    Fig. 4. (a) Holographic 3D display system; (b) Holographic 3D reconstructions [59]
    (a) Holographic video display system based on coarse integration; (b) Full-color 3D holographic reconstructions [68]
    Fig. 5. (a) Holographic video display system based on coarse integration; (b) Full-color 3D holographic reconstructions [68]
    (a) Slim-panel holographic 3D display system; (b) Full-color 3D holographic reconstruction69]
    Fig. 6. (a) Slim-panel holographic 3D display system; (b) Full-color 3D holographic reconstruction69]
    The brief history of computer-generated holography
    Fig. 7. The brief history of computer-generated holography
    Computer-generated holographic algorithm based on auto-encoder deep learning network[90]
    Fig. 8. Computer-generated holographic algorithm based on auto-encoder deep learning network[90]
    Holographic reconstructions of 3D models generated by 2D-to-3D rendering method [112]
    Fig. 9. Holographic reconstructions of 3D models generated by 2D-to-3D rendering method [112]
    SpeedQualityTraining timeGeneralization
    Random-phase-based methodsMediumMediumNAMedium
    Iteration-based methodsSlowHighNAHigh
    Learning-based methodsFastHighLongHigh
    Table 1. Comparison of optimization methods for holographic
    Hardware complexitySoftware complexity
    Wavefront-compensation methods[91-92]HighHigh
    Conventional superposition methods[96-98]LowHigh
    Phase-shifting-based superposition methods [99]LowLow
    Table 2. Comparison of distortion calibration methods in holography
    ResolutionDepth rangeProcessing timeCompatibility
    Hardware-based methodsCamera arrayHighLargeLongMedium
    TOF cameraLowLargeShortLow
    Lens-array cameraMediumMediumMediumLow
    Structured illumination cameraHighMediumLongLow
    Software-based methods3D modeling methodsHighLargeLongLow
    2D-to-3D methodsHighLargeMediumHigh
    Table 3. Comparison of generation methods of 3D content
    Liangcai Cao, Zehao He, Kexuan Liu, Xiaomeng Sui. Progress and challenges in dynamic holographic 3D display for the metaverse (Invited)[J]. Infrared and Laser Engineering, 2022, 51(1): 20210935
    Download Citation