Making 3-D Imaging 1,000 Times Better

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The new paper also offers the tantalizing prospect that polarization systems could aid the development of self-driving cars. Today’s experimental self-driving cars are, in fact, highly reliable under normal illumination conditions, but their vision algorithms go haywire in rain, snow, or fog. That’s because water particles in the air scatter light in unpredictable ways, making it much harder to interpret.

The MIT researchers show that in some very simple test cases — which have nonetheless bedeviled conventional computer vision algorithms — their system can exploit information contained in interfering waves of light to handle scattering. “Mitigating scattering in controlled scenes is a small step,” Kadambi says. “But that’s something that I think will be a cool open problem.”

“The work fuses two 3-D sensing principles, each having pros and cons,” says Yoav Schechner, an associate professor of electrical engineering at Technion — Israel Institute of Technology in Haifa, Israel. “One principle provides the range for each scene pixel: This is the state of the art of most 3-D imaging systems. The second principle does not provide range. On the other hand, it derives the object slope, locally. In other words, per scene pixel, it tells how flat or oblique the object is.”

“The work uses each principle to solve problems associated with the other principle,” Schechner explains. “Because this approach practically overcomes ambiguities in polarization-based shape sensing, it can lead to wider adoption of polarization in the toolkit of machine-vision engineers.”