AI Aids in Real

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Jun 01, 2023

AI Aids in Real

“People previously thought that with existing consumer-grade hardware it was impossible to do real-time 3D holography computations,” said lead author Liang Shi, a Ph.D. student in MIT’s Department of

“People previously thought that with existing consumer-grade hardware it was impossible to do real-time 3D holography computations,” said lead author Liang Shi, a Ph.D. student in MIT’s Department of Electrical Engineering and Computer Science. “It’s often been said that commercially available holographic displays will be around in 10 years, yet this statement has been around for decades.”Shi believes the new approach, “tensor holography,” will bring the goal within reach.Ultimately, the difference between a photograph and a hologram lies in the hologram’s encoding of the brightness and phase of each lightwave. This allows a hologram to portray a more life-like representation of a scene’s parallax and depth. To optically capture a hologram, a laser beam is split, with half used to illuminate the subject and the other half used as a reference for the lightwaves’ phase. The reference generates a sense of depth. These holograms, however, which were developed in the mid-20th century, were static and therefore unable to capture motion. And the method only produced one hard copy.

To address occlusion, they also provided a new set of physics-based calculations.The algorithm, with a photorealistic training data set, optimized its own calculations, successfully enhancing its ability to generate holograms. The network operated orders of magnitude faster than physics-based calculations.The method is able to generate holograms in milliseconds from images with depth information — provided by typical computer generated images and can be calculated with a multicamera setup or a lidar sensor. The compact tensor network requires less than 1 MB of memory.“It’s negligible, considering the tens and hundreds of gigabytes available on the latest cellphone,” researcher Wojciech Matusik said.In VR, the team believes the technology could provide more realistic scenery and eliminate eyestrain and other side effects of long-term VR use. The technology could also see use in displays capable of modulating the phase of lightwaves.“It’s a considerable leap that could completely change people’s attitudes toward holography,” Matusik said. “We feel like neural networks were born for this task.”The work was published in Nature (www.doi.org/10.1038/s41586-020-03152-0).