Abstract
We propose a novel method for the geometric registration of semantically labeled regions. We approximate semantic regions by ellipsoids, and leverage their convexity to
formulate the correspondence search effectively as a constrained optimization problem that maximizes the number
of matched regions, and which we solve globally optimal
in a Branch-and-Bound fashion. To this end, we derive
suitable linear matrix inequality constraints which describe
ellipsoid-to-ellipsoid assignment conditions. Our approach
is robust to large percentages of outliers and thus applicable to difficult correspondence search problems. In multiple
experiments we demonstrate the flexibility and robustness of
our approach on a number of challenging vision problems.