By Adam Zewe
As a car travels along a narrow city street, reflections off the glossy paint or side mirrors of parked vehicles can help the driver glimpse things that would otherwise be hidden from view, like a child playing on the sidewalk behind the parked cars.
Drawing on this idea, researchers from MIT and Rice University have created a computer vision technique that leverages reflections to image the world. Their method uses reflections to turn glossy objects into “cameras,” enabling a user to see the world as if they were looking through the “lenses” of everyday objects like a ceramic coffee mug or a metallic paper weight.
Using images of an object taken from different angles, the technique converts the surface of that object into a virtual sensor which captures reflections. The AI system maps these reflections in a way that enables it to estimate depth in the scene and capture novel views that would only be visible from the object’s perspective. One could use this technique to see around corners or beyond objects that block the observer’s view.
This method could be especially useful in autonomous vehicles. For instance, it could enable a self-driving car to use reflections from objects it passes, like lamp posts or buildings, to see around a parked truck.
“We have shown that any surface can be converted into a sensor with this formulation that converts objects into virtual pixels and virtual sensors. This can be applied in many different areas,” says Kushagra Tiwary, a graduate student in the Camera Culture Group at the Media Lab and co-lead author of a paper on this research.
Tiwary is joined on the paper by co-lead author Akshat Dave, a graduate student at Rice University; Nikhil Behari, an MIT research support associate; Tzofi Klinghoffer, an MIT graduate student; Ashok Veeraraghavan, professor of electrical and computer engineering at Rice University; and senior author Ramesh Raskar, associate professor of media arts and sciences and leader of the Camera Culture Group at MIT. The research will be presented at the Conference on Computer Vision and Pattern Recognition.