Global Structure-from-Motion Revisited

Reconstructing 3D structure and camera motion from images has been a long-standing focus of computer vision research and is commonly referred to as Structure-from-Motion (SfM). Solutions to this problem are categorized into two main approaches: incremental and global. While the most popular systems follow the incremental paradigm due to superior accuracy and robustness, global approaches are drastically more scalable and efficient. We revisit the problem of global SfM and propose GLOMAP as a new general-purpose system. In terms of accuracy and robustness, we achieve results comparable to COLMAP, the most widely used incremental SfM, while being orders of magnitude faster. GLOMAP significantly outperforms state-of-the-art global SfM (Theia, OpenMVG). The code will be made available as open-source.