  In this paper we propose a fully automatic method for shape correspondence
that is widely applicable, and especially effective for non isometric shapes
and shapes of different topology. We observe that fully-automatic shape
correspondence can be decomposed as a hybrid discrete/continuous optimization
problem, and we find the best sparse landmark correspondence, whose
sparse-to-dense extension minimizes a local metric distortion. To tackle the
combinatorial task of landmark correspondence we use an evolutionary genetic
algorithm, where the local distortion of the sparse-to-dense extension is used
as the objective function. We design novel geometrically guided genetic
operators, which, when combined with our objective, are highly effective for
non isometric shape matching. Our method outperforms state of the art methods
for automatic shape correspondence both quantitatively and qualitatively on
challenging datasets.
