Correspondence Selection With Loose–Tight Geometric Voting for 3-D Point Cloud Registration

Abstract

This article presents a simple yet effective method for 3-D correspondence selection and point cloud registration. It first models the initial correspondence set as a graph with nodes representing correspondences and edges connecting geometrically compatible nodes. Such graphs offer either loose or tight geometric constraints for judging the correctness of correspondence, e.g., edges, loops, and cliques. Then, we render these constraints dynamic voters to judge the correctness of a node. More specifically, we develop a loose–tight geometric voting (LT-GV) method that employs both loose and tight geometric constraints in the graph to score 3-D feature correspondences. The motivation behind this is to strike a balanced performance in terms of precision and recall because loose and tight constraints are complementary to each other. Under the dynamic voting scheme with both loose and tight voters, consistent correspondences can be retrieved based on the voting score. Both feature-matching and 3-D point cloud registration experiments on datasets with different modalities, challenges, application scenarios, and comparisons with state-of-the-art methods (including deep learned methods) verify that our LT-GV is effective for correspondence selection, robust to a number of nuisances, and able to dramatically boost 3-D point cloud registration performance.