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Despite being vastly ignored in the literature, coping with topological noiseis an issue of increasing importance, especially as a consequence of theincreasing number and diversity of 3D polygonal models that are captured bydevices of different qualities or synthesized by algorithms of differentstabilities. One approach for matching 3D shapes under topological noise is toreplace the topology-sensitive geodesic distance with distances that are lesssensitive to topological changes. We propose an alternative approach utilisinggradual deflation (or inflation) of the shape volume, of which purpose is tobring the pair of shapes to be matched to a \emph{comparable} topology beforethe search for correspondences. Illustrative experiments using differentdatasets demonstrate that as the level of topological noise increases, ourapproach outperforms the other methods in the literature.

Topologically Robust 3D Shape Matching via Gradual Deflation and Inflation