In this paper, we tackle the accurate and consistent Structure from Motion
(SfM) problem, in particular camera registration, far exceeding the memory of a
single computer in parallel. Different from the previous methods which
drastically simplify the parameters of SfM and sacrifice the accuracy of the
final reconstruction, we try to preserve the connectivities among cameras by
proposing a camera clustering algorithm to divide a large SfM problem into
smaller sub-problems in terms of camera clusters with overlapping. We then
exploit a hybrid formulation that applies the relative poses from local
incremental SfM into a global motion averaging framework and produce accurate
and consistent global camera poses. Our scalable formulation in terms of camera
clusters is highly applicable to the whole SfM pipeline including track
generation, local SfM, 3D point triangulation and bundle adjustment. We are
even able to reconstruct the camera poses of a city-scale data-set containing
more than one million high-resolution images with superior accuracy and
robustness evaluated on benchmark, Internet, and sequential data-sets.

本文提出一种可扩展的结构运动问题（SfM）求解方法，通过引入相机聚类算法将大型 SfM 问题划分为具有重叠的相机簇的子问题，并将局部增量 SfM 的相对位置应用到全局运动平均框架中以产生准确且一致的全局相机位姿。我们能够重建含有超过一百万张高分辨率图像的城市级数据集的相机位姿，在基准、Internet 和序列数据集上表现出卓越的准确性和鲁棒性。