Incremental Rotation Averaging Revisited
This work addresses incremental improvements in rotation averaging for 3D reconstruction tasks, primarily benefiting computer vision researchers and practitioners.
The paper tackles the problem of improving accuracy and robustness in incremental rotation averaging methods by introducing IRAv4, which uses a task-specific connected dominating set as a reference for alignment. The method is evaluated on the 1DSfM dataset, demonstrating effectiveness in both reference construction and the overall pipeline.
In order to further advance the accuracy and robustness of the incremental parameter estimation-based rotation averaging methods, in this paper, a new member of the Incremental Rotation Averaging (IRA) family is introduced, which is termed as IRAv4. As its most significant feature, a task-specific connected dominating set is extracted in IRAv4 to serve as a more reliable and accurate reference for rotation local-to-global alignment. This alignment reference is incrementally constructed, together with the absolute rotations of the vertices belong to it simultaneously estimated. Comprehensive evaluations are performed on the 1DSfM dataset, by which the effectiveness of both the reference construction method and the entire rotation averaging pipeline proposed in this paper is demonstrated.