Feature: Displays
Simplifying the creation of three- dimensional holographic displays By a research team of
the Graduate School of Engineering, Chiba University, Japan
H
olograms that offer a 3D view of objects provide a level of detail unattainable with typical 2D images. Due to their ability to provide a realistic and
immersive experience of 3D objects, holograms hold enormous potential for use in various fields, including medical imaging, manufacturing, gaming, entertainment and virtual reality. Tey have long held the promise of immersive 3D experiences, but generating them is difficult, hence not conducive to commercialisation. Researchers from Chiba University in
Japan are now turning to deep learning to generate three-dimensional holograms from coloured two-dimensional images: “We use neural networks to transform ordinary 2D colour images into 3D holograms, simplifying hologram creation.”
Details Holograms are traditionally constructed by recording the three-dimensional data of an object and the interactions of light with it. However, this technique is very computationally-intensive and requires special cameras to capture the 3D images. Deep-learning methods can create
holograms directly from the gathered 3D data using RGB-D cameras that capture an object’s colour and depth information. Tis approach circumvents the many computational challenges associated with the conventional method, making it an easier method of generating holograms. Professor Tomoyoshi Shimobaba’s
team at the Chiba Graduate School of Engineering further streamlines hologram generation by producing 3D images
directly from regular 2D colour images captured from ordinary cameras. “Tere are several problems in realising
holographic displays, including the acquisition of 3D data, the computational cost of holograms, and the transformation of hologram images to match the characteristics of a holographic display device. We undertook this study because we believe that deep learning has developed rapidly in recent years and has the potential to solve these problems,” said Shimobaba.
Neural-network-based Te proposed approach uses three deep neural networks (DNNs) to transform a regular 2D colour image into data that can be used to display a 3D scene or object as a hologram. Te first DNN uses the colour image captured with a regular camera as input to predict the associated depth map, and 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 fed to the second DNN to generate the hologram. Finally,
32 February 2024
www.electronicsworld.co.uk
the third DNN refines the hologram generated by the second DNN, making it suitable for display on different devices. In addition to generating the image from
ordinary cameras, the researchers found that this approach to process data and generate a hologram is much faster than current state-of-the-art graphics processing unit. “Another noteworthy benefit of our
approach is that the reproduced image of the final hologram can represent a natural 3D reproduced image. Moreover, since depth information is not used during hologram generation, this approach is cheaper and does not require 3D imaging devices such as RGB-D cameras aſter training,” said Shimobaba. Te approach can find potential
applications in high-resolution 3D head-up and head-mounted displays. Likewise, it can revolutionise the generation of in- vehicle holographic displays that will show information in 3D. Te proposed approach is thus expected to pave the way for the development of ubiquitous holographic technology.
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