The unaided human eye will never be able to see around corners, but anything is possible with enough fancy imaging technology. So-called non-line-of-sight (or NLOS) tech is an increasingly common area of study in the age of self-driving cars, which would benefit hugely from being able to see what’s around the bend. Now, a team from the Stanford Computational Imaging Lab has taken the idea a step further by spying on objects inside a locked room. All they need is a laser and a keyhole.
NLOS systems don’t yet have any applications in the real world, but they could eventually help first-responders locate people following a disaster and improve medical imaging. The idea has been validated on numerous occasions, but the current methods are too slow to be useful. In the past, NLOS experiments have relied on flat, reflective surfaces which can bound light around a barrier. The light (usually laser pulses) reflects off the target and back to the experimenters. With some computation, we can get a rough idea of what’s on the other side of the barrier without looking.
The Stanford study doesn’t do anything about the speed issue, but it does work in a wider variety of conditions. The researchers simply shined a laser through the keyhole, projecting the point on the far wall. The photons from that beam are scattered, bouncing around the room and all the objects inside it. Over time, a small number of those photons will end up coming back through the keyhole, but this by itself is not enough to tell what those photons “saw” inside.
The researchers have found that a moving object changes the laser pulse data in such a way that there is some salvageable data. With the help of an AI algorithm, it is possible to extract a passable representation of what’s inside the room.
These “estimates” of hidden object shapes won’t give you a crystal clear image. As you can see above, people end up looking a bit like birds. The letters are almost legible, though. The important thing is these estimates were generated without ever looking directly at the subject. Under the right conditions with a refined algorithm, it might be possible to tell if someone is walking around behind a closed door. Maybe it’s time to put some tape over your keyholes.
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