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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.

Edited by author 10-13-2005 12:23 PM
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.

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.