We present a new method for generating large numbers of accurate point correspondences between two wide baseline images. This is important for structure-from-motion algorithms, which rely on many correct matches to reduce error in the derived geometric structure. Given a small initial correspondence set we iteratively expand the set with nearby points exhibiting strong affine correlation, and then we constrain the set to an epipolar geometry using RANSAC. A key point to our algorithm is to allow a high error tolerance in the constraint, allowing the correspondence set to expand into many areas of an image before applying a lower error tolerance constraint. We show that this method successfully expands a small set of initial matches, and we demonstrate it on a variety of image pairs.
Correspondence expansion for wide baseline stereo
2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) ; 1 ; 1055-1062 vol. 1
2005-01-01
926116 byte
Conference paper
Electronic Resource
English
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