Point-Set Registration: Coherent Point Drift

TL;DR

The paper introduces the Coherent Point Drift (CPD) algorithm, a probabilistic method for rigid and non-rigid point set registration that effectively handles noise, outliers, and missing data, outperforming existing methods.

Contribution

It presents a novel probabilistic framework for point set registration that models alignment as a density estimation problem with coherent movement constraints.

Findings

CPD accurately registers point sets under noise and outliers.

The method outperforms current state-of-the-art registration techniques.

It offers a fast, linear-complexity algorithm for practical use.

Abstract

Point set registration is a key component in many computer vision tasks. The goal of point set registration is to assign correspondences between two sets of points and to recover the transformation that maps one point set to the other. Multiple factors, including an unknown non-rigid spatial transformation, large dimensionality of point set, noise and outliers, make the point set registration a challenging problem. We introduce a probabilistic method, called the Coherent Point Drift (CPD) algorithm, for both rigid and non-rigid point set registration. We consider the alignment of two point sets as a probability density estimation problem. We fit the GMM centroids (representing the first point set) to the data (the second point set) by maximizing the likelihood. We force the GMM centroids to move coherently as a group to preserve the topological structure of the point sets. In the rigid case, we impose the coherence constraint by re-parametrization of GMM centroid locations with rigid parameters and derive a closed form solution of the maximization step of the EM algorithm in arbitrary dimensions. In the non-rigid case, we impose the coherence constraint by regularizing the displacement field and using the variational calculus to derive the optimal transformation. We also introduce a fast algorithm that reduces the method computation complexity to linear. We test the CPD algorithm for both rigid and non-rigid transformations in the presence of noise, outliers and missing points, where CPD shows accurate results and outperforms current state-of-the-art methods.