Non-Rigid Shape Correspondence through Deformation

Solving for 3D correspondences beyond isometries has made tremendous progress in recent years, much of it due to (deep) learning. However, not all applications provide the necessary training data. This talk will focus on how far we can take the results without learning. I will present a line of work that poses the non-rigid shape registration problem in terms of physical and non-physical deformation energies. Our work aims to combine extrinsic and intrinsic measures to overcome typical shortcomings of both. We use Functional Maps and Markov Chain Monte Carlo initialization to handle all kinds of pose variations and smooth deformation fields to achieve high accuracy alignments, even in the case of serve non-isometries.