The ability to present three-dimensional (3D) scenes with continuous depth sensation has a profound impact on virtual and augmented reality, human–computer interaction, education and training. Computer-generated holography (CGH) enables high-spatio-angular-resolution 3D projection via numerical simulation of diffraction and interference1. Yet, existing physically based methods fail to produce holograms with both per-pixel focal control and accurate occlusion2,3. The computationally taxing Fresnel diffraction simulation further places an explicit trade-off between image quality and runtime, making dynamic holography impractical4. Here we demonstrate a deep-learning-based CGH pipeline capable of synthesizing a photorealistic colour 3D hologram from a single RGB-depth image in real time. Our convolutional neural network (CNN) is extremely memory efficient (below 620 kilobytes) and runs at 60 hertz for a resolution of 1,920 × 1,080 pixels on a single consumer-grade graphics processing unit. Leveraging low-power on-device artificial intelligence acceleration chips, our CNN also runs interactively on mobile (iPhone 11 Pro at 1.1 hertz) and edge (Google Edge TPU at 2.0 hertz) devices, promising real-time performance in future-generation virtual and augmented-reality mobile headsets. We enable this pipeline by introducing a large-scale CGH dataset (MIT-CGH-4K) with 4,000 pairs of RGB-depth images and corresponding 3D holograms. Our CNN is trained with differentiable wave-based loss functions5 and physically approximates Fresnel diffraction. With an anti-aliasing phase-only encoding method, we experimentally demonstrate speckle-free, natural-looking, high-resolution 3D holograms. Our learning-based approach and the Fresnel hologram dataset will help to unlock the full potential of holography and enable applications in metasurface design6,7, optical and acoustic tweezer-based microscopic manipulation8,9,10, holographic microscopy11 and single-exposure volumetric 3D printing12,13.
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Our hologram dataset (MIT-CGH-4K) and the trained CNN model will be made publicly available (on GitHub) along with the paper.
The code to evaluate the trained CNN model will be made publicly available (on GitHub) along with the paper. Additional codes are available from the corresponding authors upon reasonable request.
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We thank K. Aoyama and S. Wen (from Sony) for discussions; J. Minor, T. Du, M. Foshey, L. Makatura, W. Shou and T. Erps from MIT for improving/editing the manuscript; R. White for the administration of the project; X. Ju for the design of iPhone demo; and P. Ma for providing an iPhone 11 Pro for the mobile demo. We acknowledge funding from Sony Research Award Program.
The authors declare no competing interests.
Peer review information Nature thanks Tomoyoshi Shimobaba and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Extended data figures and tables
Extended Data Fig. 1 Visualization of masked Fresnel zone plates computed by OA-PBM and performance comparison of foreground occlusion.
a, A depth image cropped from a frame of Big Buck Bunny. Three regions with different depth landscapes are highlighted in different colours. b, Masked Fresnel zone plates computed for the centre pixel of each highlighted region. Three pixels are propagated for the same distance for ease of comparison. The flat depth landscape around the green pixel results in a non-occluded Fresnel zone plate. The masked Fresnel zone plates of red and blue pixels contain sharp cutoffs at their long-distance separated occlusion boundaries, and freeform shapes at occlusion boundaries with moderate distance separation and varying depth distribution. c, Comparison of foreground reconstruction by the PBM, OA-PBM and Fresnel diffraction. The scene is a cropped modulation transfer function bar target with a step depth profile. The PBM leaks a considerable portion of the background into the foreground due to a lack of occlusion handling. The artefacts are clearly visible in the original unmagnified view. The OA-PBM removes a considerable portion of the artefacts and the remaining artefacts are visually inconsequential in the unmagnified view. d, Comparison of focal stacks reconstructed by the PBM and OA-PBM for the Big Buck Bunny. The orange bounding boxes mark the background leakage in the PBM reconstructions. a, d, Images reproduced from www.bigbuckbunny.org (© 2008, Blender Foundation) under a Creative Commons licence (https://creativecommons.org/licenses/by/3.0/).
Extended Data Fig. 2 Samples of the MIT-CGH-4K dataset and comparison with the DeepFocus dataset.
a, The RGB-D image, amplitude and phase of two samples from the MIT-CGH-4K dataset. The RGB image records the amplitude of the scene (directly visualized in sRGB space) and consists of large variations in colour, texture, shading and occlusion. The pixel depth has a statistically uniform distribution throughout the view frustum. The phase presents high-frequency features at both occlusion boundaries and texture edges to accommodate rapid depth and colour changes. b, A sample RGB-D image from the DeepFocus dataset51. c, Histograms of pixel depth distribution computed for the MIT-CGH-4K dataset and the DeepFocus dataset. b, Image reproduced from ‘3D Scans from Louvre Museum’ by Benjamin Bardou under a Creative Commons licence (https://creativecommons.org/licenses/by-nc/4.0/).
Extended Data Fig. 3 Schematic of the midpoint hologram calculation.
a, A holographic display magnified through a diverging point light source. b, A holographic display unmagnified through the thin-lens formula. c, The target hologram in this example is propagated to the centre of the unmagnified view frustum to produce the midpoint hologram. The width of the maximum subhologram is considerably reduced.
Extended Data Fig. 4 Evaluation of tensor holography CNN on model architecture and test patterns.
a, Performance comparison of different CNN architectures. b, Performance comparison of different CNN miniaturization methods. c, CNN prediction of two standard test pattern (USAF-1951 and RCA Indian-head) variants made by the authors.
Extended Data Fig. 5 Evaluation of tensor holography CNN on additional computer-rendered scenes.
a, b, CNN prediction of amplitude and phase along with focused reconstructions for holograms of a living room scene from the DeepFocus dataset51 (a) and a night landscape scene from the Stanford light field dataset29 (b). a, Certain still images from ‘ArchVizPRO Vol. 2’ were used to render new images for inclusion in this publication with the permission of the copyright holder (© Corridori Ruggero 2018), under a Creative Commons licence (https://creativecommons.org/licenses/by-nc/4.0/). Panel b reproduced with permission from ref. 29, ACM.
Extended Data Fig. 6 Evaluation of tensor holography CNN on real-world captured scenes.
a, b, CNN prediction of amplitude and phase along with focused reconstructions for holograms of a statue scene (a) and a mansion scene (b). Both scenes are from the ETH light field dataset46.
Extended Data Fig. 7 Comparison of the original DPM and the AA-DPM.
Reconstruction of two real-world scenes from the encoded phase-only holograms. The couch scene is focused on the mouse toy and the statue scene is focused on the black statue. Orange bounding boxes highlight regions with strong high-frequency artefacts. Left: DPM. Right: AA-DPM.
Extended Data Fig. 8 Holographic display prototype used for the experimental results shown in this paper.
The control box of the laser, Labjack DAQ and camera are not visualized in the figure.
Extended Data Fig. 9 Additional experimental demonstration of 3D holographic projection (part 1).
The RGB-D input can be found in Extended Data Fig. 6.
Extended Data Fig. 10 Additional experimental demonstration of 3D holographic projection (part 2).
This video demonstrates a simulated focal sweep of a CNN predicted hologram computed for a real-world captured 3D couch scene. The image resolution is 1080p.
This video demonstrates a simulated focal sweep of a CNN predicted hologram computed for a computer-rendered 3D living room scene. The image resolution is 1024*1024.
This video demonstrates a photographed focal sweep of a CNN predicted hologram computed for a real-world captured 3D couch scene. The video is captured by a Sony A7 Mark III mirrorless camera paired with a Sony GM 16-35mm/f2.8 camera lens at 4K/30 Hz and downsampled to 1080p. Only green channel is visualized for temporal stability.
This video demonstrates real-time 3D hologram computation on a NVIDIA TITAN RTX GPU. The video is captured by a Panasonic GH5 mirrorless camera with a Lumix 10-25 mm f/1.7 lens at 4K/60 Hz (a colour frame rate of 20 Hz) and downsampled to 1080P. The color is obtained field sequentially.
This video demonstrates interactive hologram computation on an iPhone 11 Pro using a mini version of tensor holography CNN (see Fig. 2 caption for network architecture details).
This video demonstrates a simulated focal sweep of a CNN predicted hologram computed for a 3D Star test pattern. The image resolution is 1550*1462.
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Shi, L., Li, B., Kim, C. et al. Towards real-time photorealistic 3D holography with deep neural networks. Nature 591, 234–239 (2021). https://doi.org/10.1038/s41586-020-03152-0
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