Des hologrammes facilement créés avec un appareil photo


Holograms are traditionally constructed by recording the three-dimensional data of an object and the interactions of light with the object. But, this technique is very demanding on computer resources, because it requires the use of a special camera to capture 3D images. Researchers have developed a solution allowing the use of a traditional device.

Holograms provide a three-dimensional (3D) view of objects, offering a level of detail that two-dimensional (2D) images cannot match. The realistic and immersive display of objects in 3D makes holograms incredibly valuable in a variety of industries, including medical imaging, manufacturing, and virtual reality.

Result, the global hologram market is growing. Valued at approximately $44 billion in 2022, it is expected to reach $67 billion by 2030, with an estimated CAGR of 21.4%.

But their generation is difficult and complex, which limits their large-scale use. For several years, deep learning (or deep learning) has been exploited to produce three-dimensional holograms. Studies carried out by the Massachusetts Institute of Technology (MIT) and supported by Sony had produced interesting results.

Of the researchers from the Graduate School of Engineering from Chiba University appear to have reached a major milestone. They developed a deep learning method that simplifies creating holograms directly from 2D photos captured with standard cameras.

No expensive equipment

This technique, which uses a sequence of three neural networks (or Deep Neural Network), not only streamlines the hologram generation process, but also outperforms current high-end graphics processing units in speed.

The first deep neural network takes as input a color image captured using a regular camera and then predicts the associated depth map, thereby providing information about the 3D structure of the image. The original RGB image and the depth map created by the first DNN are then used by the second DNN to generate a hologram. Finally, the third DNN refines the hologram generated by the second DNN, allowing it to be displayed on different devices.

The researchers found that the time required for the proposed approach to process data and generate a hologram was higher than that of a state-of-the-art graphics processing unit. Another advantage is that it does not require expensive equipment such as RGB-D cameras which capture both the color and depth information of an object. But, above all, the reproduced image of the final hologram can represent a natural 3D image.

This novel and interesting option could find applications in heads-up displays and head-mounted displays to generate high-fidelity 3D displays.

It could also be used to generate an on-board holographic head-up display. This interface (or holographic HUD) would provide diagnostic and repair instructions to professionals without needing to look at a document or screen.


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