Toward Meta-Shape-Based Multi-View 3D Point Cloud Registration: An Evaluation

Abstract

Reducing cumulative registration error is critical to accurate 3D multi-view registration. Meta-shape based methods optimize rigid transformations of point clouds by iteratively registering each point cloud with a meta-shape, which remain popular solutions to 3D multi-view registration. However, the merits and demerits of existing meta-shape based methods remain unclear. Moreover, we argue that simpler meta-shape based solutions can achieve even better performance. To this end, we evaluate seven representative meta-shape based methods in this work, including four existing ones and three modified ones, in order to investigate the problem of defining a good meta-shape. In particular, we first abstract the main steps of considered methods. Then, experiments on both object and scene datasets with real and synthetic cumulative registration errors are deployed for an in-depth evaluation. Finally, based on the experimental outcomes, we give a discussion on the advantages and limitations of meta-shape based methods. We demonstrate prior works have used unnecessarily complicated techniques for cumulative error elimination and our slightly modified simpler solutions can achieve competitive performance on experimental datasets.