We wish to determine the epipolar geometry of a stereo camera pair from image measurements alone. This paper describes a solution to this problem which does not require a parametric model of the camera system, and consequently applies equally well to a wide class of stereo con\ufb01gurations. Examples in the paper range from a standard pinhole stereo con\ufb01guration to more exotic systems combining curved mirrors and wide-angle lenses. The method described here allows epipolar curves to be learned from multiple image pairs presented to the stereo cameras. By aggregating information over the multiple images, a dense map of the epipolar curves can be determined on the images. The algorithm requires a large number of images, but has the distinct bene\ufb01t that the correspondence problem does not have to be explicitly solved. We show that for standard stereo con\ufb01gurations the results are comparable to those obtained from a state of the art parametric model method, despite the signi\ufb01cantly weaker constraints on the non-parametric model. The new algorithm is simple to implement, so it may easily be employed on a new and possibly complex camera system<\/p>\n","protected":false},"excerpt":{"rendered":"
We wish to determine the epipolar geometry of a stereo camera pair from image measurements alone. This paper describes a solution to this problem which does not require a parametric model of the camera system, and consequently applies equally well to a wide class of stereo con\ufb01gurations. 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