  Recovering 3D structure and camera motion from images has been a
long-standing focus of computer vision research and is known as
Structure-from-Motion (SfM). Solutions to this problem are categorized into
incremental and global approaches. Until now, the most popular systems follow
the incremental paradigm due to its superior accuracy and robustness, while
global approaches are drastically more scalable and efficient. With this work,
we revisit the problem of global SfM and propose GLOMAP as a new
general-purpose system that outperforms the state of the art in global SfM. In
terms of accuracy and robustness, we achieve results on-par or superior to
COLMAP, the most widely used incremental SfM, while being orders of magnitude
faster. We share our system as an open-source implementation at
{https://github.com/colmap/glomap}.
