Researchers Teach Computers To Perceive 3D from 2D
hamilton76 writes to tell us that researchers at Carnegie Mellon have found a way to allow computers to extrapolate 3 dimensional models from 2 dimensional pictures. From the article: "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. [...] Identifying vertical and horizontal surfaces and the orientation of those surfaces provides much of the information necessary for understanding the geometric context of an entire scene. Only about three percent of surfaces in a typical photo are at an angle, they have found."
They've even developed a program that allows the computer to automatically generate 3-D reconstructions of scenes based on a single image
This is so not new. These researchers may have advanced techniques is some areas, but shape from shading inversion problems like this have been worked successfully since the 1970's and earlier. The theory is well established. Horn's Robot Vision is a classic.
an ill wind that blows no good
This is only for outdoor scenes and only extracts planar information. It isn't designed for objects at all. It provides general geometric context, ie this area is ground, this area is a left facing wall, etc. That's not to say that a similar technique couldn't be used for identifying round objects, but that isn't what this is for.
Shape from shading works only on a very narrow set of objects. If you are trying to recover the shape of a marble statue, use shape from shading. If your object has color forget about it.
What you are saying amounts to "People have done research into computer vision in the past, therfore any new research into computer vision is soooo not new."