Neural 3D holography
-
- Suyeon Choi
- Stanford University
-
- Manu Gopakumar
- Stanford University
-
- Yifan Peng
- Stanford University
-
- Jonghyun Kim
- NVIDIA and Stanford University
-
- Gordon Wetzstein
- Stanford University
Bibliographic Information
- Other Title
-
- learning accurate wave propagation models for 3D holographic virtual and augmented reality displays
Description
<jats:p>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.</jats:p>
Journal
-
- ACM Transactions on Graphics
-
ACM Transactions on Graphics 40 (6), 1-12, 2021-12
Association for Computing Machinery (ACM)
- Tweet
Details 詳細情報について
-
- CRID
- 1360298762025526656
-
- ISSN
- 15577368
- 07300301
-
- Data Source
-
- Crossref