Coherent Point Drift Networks: Unsupervised Learning of Non-Rigid Point Set Registration

Understand

Given new pairs of source and target point sets, standard point set registration methods often repeatedly conduct the independent iterative search of desired geometric transformation to align the source point set with the target one.

• This limits their use in applications to handle the real-time point set registration with large volume dataset.
• This paper presents a novel method, named coherent point drift networks (CPD-Net), for the unsupervised learning of geometric transformation towards real-time non-rigid point set registration.
• In contrast to previous efforts (e.g.