Correspondence problems are of great importance in
computer vision. They appear as subtasks in many applications
such as object recognition, merging partial 3D reconstructions
and image alignment. Automatically matching
features from appearance only is difficult and errors are
frequent. Thus, it is necessary to use geometric consistency
to remove incorrect correspondences. Typically heuristic
methods like RANSAC or EM-like algorithms are used, but
they risk getting trapped in local optima and are in no way
guaranteed to find the best solution.
This paper illustrates how pairwise constraints in combination
with graph methods can be used to efficiently find
optimal correspondences. These ideas are implemented on
two basic geometric problems, 3D-3D registration and 2D-
3D registration. The developed scheme can handle large
rates of outliers and cope with multiple hypotheses. Despite
the combinatorial explosion, the resulting algorithm
which has been extensively evaluated ...