- Nov 11, 2004
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http://tinyurl.com/orehf
Researchers at Carnegie Mellon University appear to have solved a problem long thought impossible, teaching computers to turn static 2D images into 3D models. It was apparently a hot area for research in the 1970s but was virtually abandoned in the 80s after attempts to devise the machine learning necessary proved too demanding for the computers of the time. The key to Carnegie Mellon's research, apart from better machines, is the ability for computers to detect visual cues (such as a car) that can be used to differentiate between vertical and horizontal surfaces -- easy for us humans, but enough to turn even the most powerful computers into an incoherent mess. Apart from turning your vacation snapshots into a whole new experience, one of the big applications for this technology is obviously robotics, where it could boost their vision systems, improve navigation, and basically endow them with one more skill necessary to keep us in line after the uprising.
Researchers at Carnegie Mellon University appear to have solved a problem long thought impossible, teaching computers to turn static 2D images into 3D models. It was apparently a hot area for research in the 1970s but was virtually abandoned in the 80s after attempts to devise the machine learning necessary proved too demanding for the computers of the time. The key to Carnegie Mellon's research, apart from better machines, is the ability for computers to detect visual cues (such as a car) that can be used to differentiate between vertical and horizontal surfaces -- easy for us humans, but enough to turn even the most powerful computers into an incoherent mess. Apart from turning your vacation snapshots into a whole new experience, one of the big applications for this technology is obviously robotics, where it could boost their vision systems, improve navigation, and basically endow them with one more skill necessary to keep us in line after the uprising.
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