Although image data storage isn't exactly small or cheap in terms of memory required, I think the basic idea here is analogous to that of machine vision image libraries, where the machine vision user builds up a database of images of objects to be inspected on the line, such as PC boards and components on the boards.
The idea is to create 3D scans of various objects to help teach robots about their environment and the objects in it, so they can navigate the environment and manipulate those objects, including, for example, refrigerators and people. An example given in this IEEE Spectrum article http://spectrum.ieee.org/automaton/robotics/robotics-hardware/kinecthome-wants-to-start-3d-scanning-the-world?utm_source=feedburner&utm_medium=feed&utm_campaign=Feed:+IeeeSpectrum+(IEEE+Spectrum) is teaching a robot to open a fridge door. First, the robot has to have a map of a fridge door and how it operates. If the robot is Kinect-equipped, as many now are (in R&D, anyway), it can use 3D images for those maps. But fridges aren't all the same size, don't have the same kind of door, and doors aren't always located on the same side of the box. So it needs an image library for each object: lots and lots of images.
This is an interesting project. The Kinect is an interesting device, and has many uses. Building up a database in this way is an outstanding way to get a large mass of information in a short time. In AI it is very beneficial to have a large training set. Frankly, this is true of us humans as well.
Looks pretty cool and I like the crowdsourcing angle a ton, but I'm not really sure what kinds of scans are being collected with the Kinect. It is scans of people, physical objects, movements? I'm also curious how this data is being fed back to robotics designers for future use? My guess is through the site community, but just wanted to confirm.
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