We address rotation averaging (RA) and its application to real-world 3D
reconstruction. Local optimisation based approaches are the de facto choice,
though they only guarantee a local optimum. Global optimisers ensure global
optimality in low noise conditions, but they are inefficient and may easily
deviate under the influence of outliers or elevated noise levels. We push the
envelope of rotation averaging by leveraging the advantages of a global RA
method and a local RA method. Combined with a fast view graph filtering as
preprocessing, the proposed hybrid approach is robust to outliers. We further
apply the proposed hybrid rotation averaging approach to incremental Structure
from Motion (SfM), the accuracy and robustness of SfM are both improved by
adding the resulting global rotations as regularisers to bundle adjustment.
Overall, we demonstrate high practicality of the proposed method as bad camera
poses are effectively corrected and drift is reduced.

本文提出一种采用局部和全局旋转平均法的混合方法，结合快速的视图图滤波预处理，解决 3D 重建中的精度和准确性问题，并将此方法应用于增量式运动结构，从而将此方法的实用性提高到很高的程度，实验表明，此方法可以有效地纠正糟糕的相机姿态和减小漂移。

混合旋转均值：一种快速且鲁棒的旋转均值方法

Hybrid Rotation Averaging: A Fast and Robust Rotation Averaging Approach

Accuracy and efficiency are two key problems in large scale incremental
Structure from Motion (SfM). In this paper, we propose a unified framework to
divide the image set into clusters suitable for reconstruction as well as find
multiple reliable and stable starting points. Image partitioning performs in
two steps. First, some small image groups are selected at places with high
image density, and then all the images are clustered according to their optimal
reconstruction paths to these image groups. This promises that the scene is
always reconstructed from dense places to sparse areas, which can reduce error
accumulation when images have weak overlap. To enable faster speed, images
outside the selected group in each cluster are further divided to achieve a
greater degree of parallelism. Experiments show that our method achieves
significant speedup, higher accuracy and better completeness.

本文提出一个统一的框架，将图像集分成适合重建的簇，并找到多个可靠和稳定的起始点；图像分区分两步进行，首先在密集图像的位置选择一些小的图像组，然后将所有图像按照它们到这些图像组的最优重建路径进行聚类。实验结果表明，我们的方法实现了显著的加速、更高的精度和更好的完整性。