Keywords

point correspondences, structure-from-motion algorithms

Abstract

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.

Original Publication Citation

Kevin Steele and Parris K. Egbert, "Correspondence Expansion for Wide Baseline Stereo." In IEEE Conference on Computer Vision and Pattern Recognition (CVPR 25), San Diego, CA, Vol 1, pp.155-161, (June 2-26, 25).