Simplifying the production of 3D holographic displays

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Holograms, which provide a three-dimensional (3D) view of objects, provide a level of detail that cannot be achieved by conventional two-dimensional (2D) images. Due to their ability to provide a realistic and immersive experience of 3D objects, holograms have great potential for use in various fields including medical imaging, construction, and virtual reality.

Holograms are traditionally made by recording three-dimensional data of an object and the interactions of light with the object. However, this technique is very computationally intensive because it requires the use of a special camera to capture 3D images. This makes the production of holograms challenging and limits their widespread use.

In recent times, many deep learning methods have been proposed to generate holograms. They can create holograms directly from 3D data captured using RGB-D cameras that capture both the color and depth information of an object. This approach bypasses many of the computational challenges associated with the conventional method and presents a simpler approach to hologram generation.

Now, a team of researchers led by Professor Tomoyoshi Shimobaba of Chiba University’s Graduate School of Engineering is proposing a new deep learning-based approach to generate holograms by generating 3D images directly from conventional 2D color images captured using conventional cameras. It makes it easier.

Yoshiyuki Ishii and Tomoyoshi Ito from Chiba University Graduate School of Engineering were also part of the study, published in Optics and laser in engineering.

Explaining the rationale behind the study, Professor Shimobaba says, “There are several challenges in realizing holographic displays, including obtaining 3D data, the computational cost of holograms, and converting hologram images to match the characteristics of a holographic display device. We We conducted this study because we believe that deep learning has developed rapidly in recent years and has the potential to solve these problems.”

The proposed approach uses three deep neural networks (DNN) to convert a typical 2D color image into data that can be used to display a 3D scene or object as a hologram.

The first DNN uses a color image captured using a conventional camera as 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 used by the second DNN to generate the hologram. Finally, the third DNN modifies the hologram produced by the second DNN and makes it suitable for display on different devices.

The researchers found that the time taken by the proposed approach to process the data and generate the hologram was superior to that of an advanced graphics processing unit.

Another significant advantage of our approach is that the reproduced image of the final hologram can represent a natural 3D reproduced image. Furthermore, since depth information is not used during hologram generation, this approach is inexpensive and does not require 3D imaging devices such as RGB. Professor Shimobaba adds: D cameras after training.

In the near future, this approach can find potential applications in head-up and head-mounted displays to produce high-quality 3D displays. Likewise, it could revolutionize the generation of an in-car holographic head-up display that might be able to provide passengers with information about people, roads and signs in 3D.

Therefore, the proposed approach is expected to pave the way to enhance the development of pervasive holographic technology.

more information:
Yoshiyuki Ishii et al., Multi-depth hologram generation from 2D images with deep learning, Optics and laser in engineering (2023). DOI: 10.1016/j.optlaseng.2023.107758

Magazine information:
Optics and laser in engineering

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