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takyon writes:

Stanford researchers develop technique to see objects hidden around corners

A driverless car is making its way through a winding neighborhood street, about to make a sharp turn onto a road where a child's ball has just rolled. Although no person in the car can see that ball, the car stops to avoid it. This is because the car is outfitted with extremely sensitive laser technology that reflects off nearby objects to see around corners.

This scenario is one of many that researchers at Stanford University are imagining for a system that can produce images of objects hidden from view. They are focused on applications for autonomous vehicles, some of which already have similar laser-based systems for detecting objects around the car, but other uses could include seeing through foliage from aerial vehicles or giving rescue teams the ability to find people blocked from view by walls and rubble.

Confocal non-line-of-sight imaging based on the light-cone transform (DOI: 10.1038/nature25489) (DX)

Whereas light detection and ranging (LIDAR) systems use such measurements to recover the shape of visible objects from direct reflections, NLOS [(Non Line Of Sight)] imaging reconstructs the shape and albedo of hidden objects from multiply scattered light. Despite recent advances, NLOS imaging has remained impractical owing to the prohibitive memory and processing requirements of existing reconstruction algorithms, and the extremely weak signal of multiply scattered light. Here we show that a confocal scanning procedure can address these challenges by facilitating the derivation of the light-cone transform to solve the NLOS reconstruction problem. This method requires much smaller computational and memory resources than previous reconstruction methods do and images hidden objects at unprecedented resolution. Confocal scanning also provides a sizeable increase in signal and range when imaging retroreflective objects. We quantify the resolution bounds of NLOS imaging, demonstrate its potential for real-time tracking and derive efficient algorithms that incorporate image priors and a physically accurate noise model. Additionally, we describe successful outdoor experiments of NLOS imaging under indirect sunlight.

Original Submission