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Holographic near-eye displays promise unprecedented capabilities for virtual and augmented reality (VR/AR) systems. The image quality achieved by current holographic displays, however, is limited by the wave propagation models used to simulate the physical optics. We propose a neural network-parameterized plane-to-multiplane wave propagation model that closes the gap between physics and simulation. Our model is automatically trained using camera feedback and it outperforms related techniques in 2D plane-to-plane settings by a large margin. Moreover, it is the first network-parameterized model to naturally extend to 3D settings, enabling high-quality 3D computer-generated holography using a novel phase regularization strategy of the complex-valued wave field. The efficacy of our approach is demonstrated through extensive experimental evaluation with both VR and optical see-through AR display prototypes.
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On the Statistical Analysis of Space-Time Wave Physics in Complex Enclosures
The paper proposes a physics-oriented, mathematically tractable statistical wave model, named as the space-time stochastic Green’s function, for analyzing the wave physics of high frequency reverberation within complex confined electromagnetic environments. The model characterizes both spatial and temporal variations and correlations of wave fields without the need for a detailed knowledge of the complex environment. Experimental results are supplied to validate the proposed work.
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- Award ID(s):
- 1953000
- PAR ID:
- 10315270
- Date Published:
- Journal Name:
- 2021 IEEE 30th Conference on Electrical Performance of Electronic Packaging and Systems (EPEPS)
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/EPEPS51341.2021.9609212
