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Graph."],"published-print":{"date-parts":[[2016,7,11]]},"abstract":"<jats:p>Many shape and image processing tools rely on computation of correspondences between geometric domains. Efficient methods that stably extract \"soft\" matches in the presence of diverse geometric structures have proven to be valuable for shape retrieval and transfer of labels or semantic information. With these applications in mind, we present an algorithm for probabilistic correspondence that optimizes an entropy-regularized Gromov-Wasserstein (GW) objective. Built upon recent developments in numerical optimal transportation, our algorithm is compact, provably convergent, and applicable to any geometric domain expressible as a metric measure matrix. We provide comprehensive experiments illustrating the convergence and applicability of our algorithm to a variety of graphics tasks. 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