IRAv3+: Hierarchical Incremental Rotation Averaging via Multiple Connected Dominating Sets

Focusing on the difficulty of absolute rotation globalization of large-scale rotation averaging problem, a novel hierarchical pipeline, termed as IRAv3+, based on multiple Connected Dominating Sets (CDSs) is proposed in this paper. Specifically, the proposed method not only obtains the graph clusters for local rotation averaging like other cluster-based methods, but also generate a subset via connected dominating set extraction, which is served as a reference for rotation globalization. To facilitate the rotation globalization, two key techniques are proposed: 1) to provide a more reliable global reference, instead of a single CDS, multiple CDSs are randomly selected and united; 2) to give a more accurate local-to-global alignment estimation, instead of using the relative rotation measurements of the sharing edges between local clusters and global reference, the absolute rotations of common vertices between them are involved. Experiments on the 1DSfM dataset demonstrate the effectiveness of the proposed IRAv3+ and its advantages over the existing cluster-based rotation averaging methods and other state of the arts.