Partial correspondence of 3D shapes using properties of the nearest-neighbor field

This paper proposes an algorithm for finding correspondences between shapes in 3D. The method is designed to address three challenging cases: large deformations, partiality of the shapes, and topological noise. At the core of the method lies a novel, yet simple, similarity measure that analyzes statistical properties of the nearest-neighbor field from the source surface to the target. This information is shown to be powerful, compared to minimizing some function of distances. In particular, the proposed similarity function analyzes the diversity of the nearest-neighbor field and its preservation of distances. Empirical evaluation on partial matching benchmarks shows that our method outperforms state-of-the-art techniques, both quantitatively and qualitatively.