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'Time camera' generates 3D images from echoes of light - Science Magazine

'Time camera' generates 3D images from echoes of light - Science Magazine

'Time camera' generates 3D images from echoes of light - Science Magazine
Aug 04, 2020 2 mins, 11 secs

A team of scientists has pulled off the photographic equivalent of that trick: They tease out a 3D image of a scene by timing the reflections of light onto a simple detector.

“It’s really surprising to me that they can get anything out of this system because there’s absolutely not enough information coming out of it,” says Laura Waller, a computer scientist and electrical engineer at the University of California, Berkeley, who was not involved in the work.

The image is the pattern of brighter and dark spots the reflected light creates.

A so-called time-of-flight camera can even add depth and make a 3D image by timing exactly when a flash of light reflected from an object arrives at the various pixels.

In recent decades, researchers have invented subtler ways to capture an image using just a single pixel detector.

By tracking those variations, researchers can reconstruct the image of the object.

Now, data scientist Alex Turpin and physicist Daniele Faccio of the University of Glasgow and colleagues have invented a way to generate a 3D image with a single pixel—but without the patterned flashes.

From that information alone, the researchers reconstruct an image of the scene.

That’s surprising, Waller says, because in principle there’s no one-to-one relationship between the arrangement of objects in the scene and the timing information.

To get past that problem, Turpin and colleagues employed a machine learning program called a neural network that can be trained to detect subtle correlations between inputs and outputs.

After using the two data sets to train the neural network, the program was able to image people moving in the scene by itself, the researchers reported last week in Optica.

The neural network can decipher the ambiguous signals because, thanks to its training, it will try to conjure up only scenes and objects similar to those it has already seen.

But that means the system is also limited: It must train on the precise scene that it’s going to observe.

In theory, a technophile might be able to surveil a room using an ordinary laptop and the radio antenna from a wireless router, Turpin says.

Still, Waller says it’s not clear how useful the system will be, given that actual cameras are already fairly inexpensive.

Instead, she says, the experiment raises an interesting conceptual question: Precisely how does the neural network learn to make reasonable images.

“What’s the physics that it’s picking up on?” The challenge, Waller says, is to go beyond using the neural network as a black box and actually figure out what it’s doing.

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