Automatic anatomical shape correspondence and alignment using mesh features

In this work, we propose a fully automatic and computationally efficient group registration approach for sets of three-dimensional models represented as mesh objects. Our approach is based on agglomerating the set of pairwise model-to-model rigid registrations by a robust spectral synchronization scheme. The pairwise registration is computed using spectral graph matching applied to meshes via the LD-SIFT local mesh features. We applied the proposed scheme to sets of subcortical surfaces, and it was shown to provide accurate and robust registration results.