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  Messages 7-5 deleted by author between 05-17-2008 10:16 AM and 07-22-2006 09:30 AM
Brendan Morris
01:34 PM ET (US)
The results are quite impressive visually. I'm curious to see the results as applied to a non-outdoor scene. I'm wondering if this could be used as an initial guess to a more sophisticated 3d reconstruction engine to create very accurate surveillance tracks. I think the power of this method is the image view constraints and simple model. Can adaptive learning also be applied in a real time application.
Erik Murphy-Chutorian
12:22 PM ET (US)
I'm impressed at how good a 3D rendition can look when everything is simply a set of texture mapped vertical planes. In the example video of the train image, it seems like the front of the engine possesses a much more complex 3D model. To give the results and authors credit, if I hadn't read the paper, I would have believed they were employing a much more complex algorithm.

I had some difficulty in the sections describing how the Geometrical features are used to define the pop-up regions. I'm hoping the presentation will provide more insight.
Edited 10-13-2005 12:23 PM
Robin Hewitt
11:02 AM ET (US)
I'd be interested to see if this could be combined with structure from motion. For example, one could imagine a robot learning geometric categories as it moves, then applying that when it's stationary.
Ben Laxton
11:09 PM ET (US)
I found these papers to be very interesting. I first read the sigraph paper, and the algorithm seemed interesting, but the application presented was not groundbreaking, at least in my opinion. The discussion of the same algorithm from a recognition point of view in the ICCV paper however was compelling. This method makes steps toward integrating many aspects of visual knowledge into a recognition framework. The drawback is that the current implementation is quite slow. However, the recognition steps could possibly be made simpler within this framework. I also wonder how this method might be improved by range (stereo) data.
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