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 the most significant feature of the IRAv4, a
task-specific connected dominating set is extracted to serve as a more reliable
and accurate reference for rotation global alignment. In addition, to further
address the limitations of the existing rotation averaging benchmark of relying
on the slightly outdated Bundler camera calibration results as ground truths
and focusing solely on rotation estimation accuracy, this paper presents a new
COLMAP-based rotation averaging benchmark that incorporates a cross check
between COLMAP and Bundler, and employ the accuracy of both rotation and
downstream location estimation as evaluation metrics, which is desired to
provide a more reliable and comprehensive evaluation tool for the rotation
averaging research. Comprehensive comparisons between the proposed IRAv4 and
other mainstream rotation averaging methods on this new benchmark demonstrate
the effectiveness of our proposed approach.

本文介绍了一种名为 IRAv4 的增量旋转平均方法，其中使用了特定任务的连接支配集作为旋转全局对齐的可靠参考，并提出了一种基于 COLMAP 的旋转平均基准，将旋转和下游位置估计精度作为评估指标，与其他主流旋转平均方法进行了全面的比较。