DGE-CNN: 2D-to-3D holographic display based on a depth gradient extracting module and ZCNN network

GAEA Spatial Light Modulators
AR/VR/MR / Holographic Display Deep Learning / Neuronal Network
Published on:
Authors: Ninghe Liu, Zhengzhong Huang, Zehao He, and Liangcai Cao
Abstract:

Holography is a crucial technique for the ultimate three-dimensional (3D) display, because it renders all optical cues from the human visual system. However, the shortage of 3D contents strictly restricts the extensive application of holographic 3D displays. In this paper, a 2D-to-3D-display system by deep learning-based monocular depth estimation is proposed. By feeding a single RGB image of a 3D scene into our designed DGE-CNN network, a corresponding display-oriented 3D depth map can be accurately generated for layer-based computer-generated holography. With simple parameter adjustment, our system can adapt the distance range of holographic display according to specific requirements. The high-quality and flexible holographic 3D display can be achieved based on a single RGB image without 3D rendering devices, permitting potential human-display interactive applications such as remote education, navigation, and medical treatment.

Open Access

Publication: Optics Express
Issue/Year: Opt. Express 31, 23867-23876 (2023)
DOI: 10.1364/OE.489639
Link: https://doi.org/10.1364/OE.489639

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