In this paper, we present a simple yet effective rotation averaging pipeline, termed Incremental Rotation Averaging (IRA), which is inspired by the well-developed incremental Structure from Motion (SfM) techniques. Unlike the traditional rotation averaging methods which estimate all the absolute rotations simultaneously and focus on designing either robust loss function or outlier filtering strategy, here the absolute rotations are estimated in an incremental way. Similar to the incremental SfM, our IRA is robust to relative rotation outliers and could achieve accurate rotation averaging results. In addition, we propose several key techniques, such as initial triplet and Next-Best-View selection, Weighted Local/Global Optimization, and Re-Rotation Averaging, to push the rotation averaging results one step further. Ablation studies and comparison experiments on the 1DSfM, Campus, and San Francisco datasets demonstrate the effectiveness of our IRA and its advantages over the state-of-the-art rotation averaging methods in accuracy and robustness.

在本文中，我们提出了一个简单而有效的旋转平均流水线，称为增量旋转平均（IRA），其灵感来自发达的运动增量结构（SfM）技术。与传统的旋转平均方法不同，传统的旋转平均方法会同时估计所有绝对旋转并专注于设计鲁棒损失函数或离群值滤波策略，而绝对旋转则以增量方式进行估计。与增量SfM相似，我们的IRA对相对旋转离群值具有鲁棒性，并且可以实现精确的旋转平均结果。此外，我们提出了几种关键技术，例如初始三元组和下一个最佳视图选择，加权局部/全局优化和平均旋转平均，将旋转平均结果进一步推进了一步。