Extended coherent point drift algorithm with correspondence priors and optimal subsampling

The problem of dense point set registration, given a sparse set of prior correspondences, often arises in computer vision tasks. Unlike in the rigid case, integrating prior knowledge into a registration algorithm is especially demanding in the non-rigid case due to the high variability of motion and deformation. In this paper we present the Extended Coherent Point Drift registration algorithm. It enables, on the one hand, to couple correspondence priors into the dense registration procedure in a closed form and, on the other hand, to process large point sets in reasonable time through adopting an optimal coarse-to-fine strategy. Combined with a suitable keypoint extractor during the preprocessing step, our method allows for non-rigid registrations with increased accuracy for point sets with structured outliers. We demonstrate advantages of our approach against other non-rigid point set registration methods in synthetic and real-world scenarios.