  Correspondence is a ubiquitous problem in computer vision and graph matching
has been a natural way to formalize correspondence as an optimization problem.
Recently, graph matching solvers have included higher-order terms representing
affinities beyond the unary and pairwise level. Such higher-order terms have a
particular appeal for geometric constraints that include three or more
correspondences like the PnP 2D-3D pose problems. In this paper, we address the
problem of finding correspondences in the absence of unary or pairwise
constraints as it emerges in problems where unary appearance similarity like
SIFT matches is not available. Current higher order matching approaches have
targeted problems where higher order affinity can simply be formulated as a
difference of invariances such as lengths, angles, or cross-ratios. In this
paper, we present a method of how to apply geometric constraints modeled as
polynomial equation systems. As opposed to RANSAC where such systems have to be
solved and then tested for inlier hypotheses, our constraints are derived as a
single affinity weight based on $n>2$ hypothesized correspondences without
solving the polynomial system. Since the result is directly a correspondence
without a transformation model, our approach supports correspondence matching
in the presence of multiple geometric transforms like articulated motions.
