期刊
ACM TRANSACTIONS ON GRAPHICS
卷 40, 期 6, 页码 -出版社
ASSOC COMPUTING MACHINERY
DOI: 10.1145/3478513.3480542
关键词
computational displays; holography; virtual reality; augmented reality
资金
- Kwanjeong Scholarship
- Korea Government Scholarship
- Stanford Graduate Fellowship
- Ford (Alliance Project)
- NSF [1839974]
- PECASE by the ARO
- Div Of Civil, Mechanical, & Manufact Inn
- Directorate For Engineering [1839974] Funding Source: National Science Foundation
The article introduces a method to improve holographic display image quality using a neural network parameterized wave propagation model, trained through camera feedback, and demonstrates its effectiveness through experiments.
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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