Wow tough crowd, this is actually extremely impressive if you're interested in the subject. The fineness and complexity of the reconstructed depth field is unparalleled.
I work in the field of Computer Vision based 3D Reconstruction. Although this development is "nice", it is hardly capable of the potential applications cited. Notice any 3D reconstructions used anywhere other than novelty? That's because they are mostly useless. In order to create useful 3D Reconstrucions, the underlying reconstruction system needs to recognize the object and use prior acquired references to supply information obscured from view, and/or identify transformations (damage, wear) to the "original" objects and scene being analyzed. The goal is to supply one image of an object, and receive back a correctly formed, rigged for correct motion, 3d geometry of nearly any common object. And it's coming.
I have worked in the field before and I know 3D reconstructions get used for serious work - albeit rarely for the purposes of redisplay. Besides an overworked applications section of a paper? I'm shocked and horrified :)
The work you're describing sounds impressive though.
What if you can't separate the geometry you want from the geometry next to it, or the geometry it sets upon? That's the current public state of the art. Such systems become much more useful after they can identify objects so they can tell when two items are separate things, and when two sections of the same thing are in fact one object.
If you are making a robot that has to manipulate objects in it's environment, fair enough. If you are making a vehicle that just needs to know about the free "drivable" space in it's vicinity, the barrier is lower.
Looks very nice - and of great interest to me, since I was already playing with epipolar images for structure - from - motion albeit for a very different application.
