A team of researchers from Chiba University propose a novel approach based on deep learning that further streamlines hologram generation by producing 3D images directly from regular 2D color images captured using ordinary cameras. The proposed approach employs three deep neural networks (DNNs) to transform a regular 2D color image into data that can be used to display a 3D scene or object as a hologram. The first DNN makes use of a color image captured using a regular camera as the input and then predicts the associated depth map, providing information about the 3D structure of the image. Both the original RGB image and the depth map created by the first DNN are then utilized by the second DNN to generate a hologram.
The third DNN refines the hologram generated by the second DNN, making it suitable for display on different devices. The researchers found that the time taken by the proposed approach to process data and generate a hologram was superior to that of a state-of-the-art graphics processing unit. Soon, this approach can find potential applications in heads-up and head-mounted displays for generating high-fidelity 3D displays. Likewise, it can revolutionize the generation of an in-vehicle holographic head-up display, which may be able to present the necessary information on people, roads, and signs to passengers in 3D. The proposed approach is thus expected to pave the way for augmenting the development of ubiquitous holographic technology.
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