Hierarchical RANSAC-Based Rotation Averaging

In this letter, we present a novel rotation averaging pipeline, which is performed in a hierarchical manner. Unlike the traditional rotation averaging methods which focus on designing robust loss function to get rid of the impacts of the relative rotation outliers, here the outliers are detected and filtered by leveraging the well-known robust model estimation procedure, RANdom SAmple Consensus (RANSAC). During the RANSAC process, the minimal set is randomly sampled by random tree spanning on the Epipolar-geometry Graph (EG). As the RANSAC estimation result is sensitive to the size of minimal set, the EG is clustered into several sub-graphs, and the inner- and inter-cluster RANSAC-based rotation averaging are performed hierarchically. In addition, both random generation and optimal selection of the minimal set are performed in a weighted manner to make the rotation averaging pipeline more robust. Ablation studies and comparison experiments on the 1DSfM and San Francisco (SNF) datasets demonstrate the effectiveness of our proposed method.