IRAv3: Hierarchical Incremental Rotation Averaging on the Fly

We present IRAv3, which is built upon the state-of-the-art rotation averaging method, IRA++, to push this fundamental task in 3D computer vision one step further. The key observation of this letter lies in that during IRA++, the community detection-based Epipolar-geometry Graph (EG) clustering is preemptive and permanent, which is not relevant to the follow-up rotation averaging task and limits the upper bound of absolute rotation estimation accuracy. In this letter, however, the EG clustering is performed along with the cluster-wise absolute rotation estimation, i.e. instead of pre-determination, the affiliation of each vertex to which EG cluster is determined on the fly, and the EG clustering finishes until all the vertices find the clusters they belong to, together with their absolute rotations estimated (in the local coordinate systems of the clusters they attached). By this way, a rotation averaging-targeted and -friendly EG clustering is obtained, which facilitates the rotation averaging task in turn. Experiments on both 1DSfM and KITTI odometry datasets demonstrate the effectiveness of our proposed IRAv3 on large-scale rotation averaging problems and its advantages over its previous works (IRA and IRA++) and other state of the arts.

Automated summary

We propose IRAv3, which is built upon the state-of-the-art rotation averaging method IRA++, to advance the fundamental task in 3D computer vision. The key observation is that the community detection-based Epipolar-geometry Graph (EG) clustering in IRA++ is fixed and does not allow changes, limiting the accuracy of absolute rotation estimation. However, in IRAv3, the EG clustering is performed together with the estimation of absolute rotation for each cluster, allowing for dynamic determination of vertex affiliation. Experimental results on 1DSfM and KITTI odometry datasets demonstrate the effectiveness of IRAv3 in large-scale rotation averaging problems and its advantages over previous works and other state-of-the-art methods.