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.

中文翻译：

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基于分层 RANSAC 的旋转平均

在这封信中，我们提出了一种新颖的旋转平均管道，它以分层方式执行。与传统的旋转平均方法侧重于设计稳健的损失函数以消除相对旋转异常值的影响不同，这里的异常值是通过利用众所周知的稳健模型估计程序 RANdom SAmple Consensus (RANSAC) 来检测和过滤的。在 RANSAC 过程中，最小集由对极几何图 (EG) 上的随机树生成随机采样。由于 RANSAC 估计结果对最小集的大小敏感，因此将 EG 聚类为几个子图，并分层执行基于簇内和簇间 RANSAC 的旋转平均。此外，最小集的随机生成和最优选择均以加权方式执行，以使旋转平均流水线更加稳健。对 1DSfM 和旧金山 (SNF) 数据集的消融研究和比较实验证明了我们提出的方法的有效性。