Single Voter Spreading for Efficient Correspondence Grouping and 3D Registration.

Abstract

Obtaining highly consistent correspondences between point clouds is crucial for computer vision tasks such as 3D registration and recognition. Due to nuisances such as limited overlap and noise, initial correspondences often contain a large number of outliers, imposing a great challenge to downstream tasks. In this paper, we present a novel single voter spreading (SVOS) method for efficient 3D correspondence grouping and 3D registration. Our core insight is to leverage low-order graph constraints only in a single voter spreading voting scheme to achieve comparable constrain-ability as complex constraints without searching them. First, a simple first-order graph is constructed for the initial correspondence set. Second, a two-stage voting method is proposed, including single voter voting and spread voters voting. Each voting stage involves both local and global voting via edge constraints only. This promises good selectivity while making the voting process time- and storage-efficient. Finally, top-scored correspondences are opted for robust transformation estimation. Experiments on U3M, 3DMatch/3DLoMatch, ETH, and KITTI-LC datasets verify that SVOS achieves new state-of-the-art correspondence grouping and registration performance, while being light-weight and robust to graph construction parameters.