From picture to 3D hologram: end-to-end learning of real-time 3D photorealistic hologram generation from 2D image input
Chenliang Chang, Bo Dai, Dongchen Zhu, Jiamao Li, Jun Xia, Dawei Zhang, Lianping Hou, Songlin Zhuang
Abstract
In this Letter, we demonstrate a deep-learning-based method capable of synthesizing a photorealistic 3D hologram in real-time directly from the input of a single 2D image. We design a fully automatic pipeline to create large-scale datasets by converting any collection of real-life images into pairs of 2D images and corresponding 3D holograms and train our convolutional neural network (CNN) end-to-end in a supervised way. Our method is extremely computation-efficient and memory-efficient for 3D hologram generation merely from the knowledge of on-hand 2D image content. We experimentally demonstrate speckle-free and photorealistic holographic 3D displays from a variety of scene images, opening up a way of creating real-time 3D holography from everyday pictures.