Generating a 3 dimensional model from a 2 dimensional picture could ultimately find application in vision systems used to guide robotic vehicles, monitor security cameras and archive photos.
Using machine learning techniques, Robotics Institute researchers Alexei Efros and Martial Hebert, along with graduate student Derek Hoiem, have taught computers how to spot the visual cues that differentiate between vertical surfaces and horizontal surfaces in photographs of outdoor scenes. They've even developed a program that allows the computer to automatically generate 3-D reconstructions of scenes based on a single image.
Using 300 images gleaned from a Google search, Hoiem showed the computer numerous examples of vertical and horizontal surfaces, allowing a machine learning program to develop statistical associations between certain shapes, shadings and other characteristics typical of each orientation.
The program also takes advantage of the constraints of the real world -- skies are blue, horizons are horizontal and most objects sit on the ground.
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