We propose a novel hierarchical approach for multiple rotation averaging, dubbed HARA. Our method incrementally initializes the rotation graph based on a hierarchy of triplet support. The key idea is to build a spanning tree by prioritizing the edges with many strong triplet supports and gradually adding those with weaker and fewer supports. This reduces the risk of adding outliers in the spanning tree. As a result, we obtain a robust initial solution that enables us to filter outliers prior to nonlinear optimization. With minimal modification, our approach can also integrate the knowledge of the number of valid 2D-2D correspondences. We perform extensive evaluations on both synthetic and real datasets, demonstrating state-of-the-art results.

翻译：我们建议了一种新型的等级方法,用于多轮用平均,称为HARA。我们的方法根据三重支持的等级逐步初始化了旋转图。 关键的想法是,通过优先选择具有许多强力三重支持的边缘并逐步增加支持较弱和较少的边缘来构建一个横贯的树。 这降低了在横贯的树上增加外部线的风险。 因此, 我们获得了一个强大的初始解决方案, 使我们能够在非线性优化之前过滤外部线。 只要稍作修改, 我们的方法还可以整合对有效的 2D-2D 通信数量的知识。 我们对合成和真实数据集进行广泛的评估, 展示最新的结果 。