  We propose a novel 3D shape correspondence method based on the iterative
alignment of so-called smooth shells. Smooth shells define a series of
coarse-to-fine shape approximations designed to work well with multiscale
algorithms. The main idea is to first align rough approximations of the
geometry and then add more and more details to refine the correspondence. We
fuse classical shape registration with Functional Maps by embedding the input
shapes into an intrinsic-extrinsic product space. Moreover, we disambiguate
intrinsic symmetries by applying a surrogate based Markov chain Monte Carlo
initialization. Our method naturally handles various types of noise that
commonly occur in real scans, like non-isometry or incompatible meshing.
Finally, we demonstrate state-of-the-art quantitative results on several
datasets and show that our pipeline produces smoother, more realistic results
than other automatic matching methods in real world applications.
