Division-based large point set registration using coherent point drift (CPD) with automatic parameter tuning 1

Abstract

Abstract

Large point sets consists of unordered sets of usually 3D coordinates representing a surface (e.g., face) or a volume. With the advent of laser scanners the surface can be captured with high resolution generating a large amount of data. Processing this amount of data for point set registration efficiently, poses the type of challenges being addressed by the big data community. Coherent Point Drift (CPD) is a state-of-the-art point set registration method, that is able to handle large point cloud registration in $\mathcal{O}n$ time with the incorporation of the Fast Gauss Transform (FGT) and low-rank matrix approximation (LRA). However, its registration accuracy degrades rapidly for large point sets. To overcome this, we present a strategy that divides a large point set into several smaller overlapping subsets. These subsets are then independently registered using CPD that are then merged for final registration. To improve registration accuracy, we also propose a method to tune the width parameter of the Gaussian kernel in CPD. The proposed method has been tested on four large datasets, including the USF 3D face dataset. The results show that the proposed method is able to register large datasets with greater speed and accuracy than the state-of-the-art CPD method.