  Many algorithms for the computation of correspondences between deformable
shapes rely on some variant of nearest neighbor matching in a descriptor space.
Such are, for example, various point-wise correspondence recovery algorithms
used as a postprocessing stage in the functional correspondence framework. In
this paper, we show that such frequently used techniques in practice suffer
from lack of accuracy and result in poor surjectivity. We propose an
alternative recovery technique guaranteeing a bijective correspondence and
producing significantly higher accuracy. We derive the proposed method from a
statistical framework of Bayesian inference and demonstrate its performance on
several challenging deformable 3D shape matching datasets.
