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 post-processing stage in the functional correspondence framework.
Such frequently used techniques implicitly make restrictive assumptions (e.g.,
near-isometry) on the considered shapes and in practice suffer from lack of
accuracy and result in poor surjectivity. We propose an alternative recovery
technique capable of guaranteeing a bijective correspondence and producing
significantly higher accuracy and smoothness. Unlike other methods our approach
does not depend on the assumption that the analyzed shapes are isometric. We
derive the proposed method from the statistical framework of kernel density
estimation and demonstrate its performance on several challenging deformable 3D
shape matching datasets.

通过核密度估计的统计框架，我们提出了一种不依赖于形状同构的替代恢复技术，能够保证双向对应并产生更高的精度和平滑度，并在多个具有挑战性的可变形 3D 形状匹配数据集上展示其性能。