Scale-Invariant Global Sparse Shape Matching with Provable Optimality Guarantees

We propose a novel mixed-integer programming formulation for generating precise sparse correspondences for highly non-rigid shapes, which is provably invariant to rigid transformations and global scaling, can often be solved to global optimality, and scales linearly with mesh resolution.