A low-dimensional representation for robust partial isometric correspondences computation

• We characterize partial isometries of shapes by single-point maps up to first order.
• A novel matching algorithm for partial intrinsic matching.
• We use redundancy in our representation for approximate partial iso-metric matching.
• Robustness to strong topological noise, geometric noise and missing data.

Intrinsic shape matching has become the standard approach for pose invariant correspondence estimation among deformable shapes. Most existing approaches assume global consistency. While global isometric matching is well understood, only a few heuristic solutions are known for partial matching. Partial matching is particularly important for robustness to topological noise, which is a common problem in real-world scanner data. We introduce a new approach to partial isometric matching based on the observation that isometries are fully determined by local information: a map of a single point and its tangent space fixes an isometry. We develop a new representation for partial isometric maps based on equivalence classes of correspondences between pairs of points and their tangent-spaces. We apply our approach to register partial point clouds and compare it to the state-of-the-art methods, where we obtain significant improvements over global methods for real-world data and stronger guarantees than previous partial matching algorithms.