  While initial approaches to Structure-from-Motion (SfM) revolved around both
global and incremental methods, most recent applications rely on incremental
systems to estimate camera poses due to their superior robustness. Though there
has been tremendous progress in SfM `front-ends' powered by deep models learned
from data, the state-of-the-art (incremental) SfM pipelines still rely on
classical SIFT features, developed in 2004. In this work, we investigate
whether leveraging the developments in feature extraction and matching helps
global SfM perform on par with the SOTA incremental SfM approach (COLMAP). To
do so, we design a modular SfM framework that allows us to easily combine
developments in different stages of the SfM pipeline. Our experiments show that
while developments in deep-learning based two-view correspondence estimation do
translate to improvements in point density for scenes reconstructed with global
SfM, none of them outperform SIFT when comparing with incremental SfM results
on a range of datasets. Our SfM system is designed from the ground up to
leverage distributed computation, enabling us to parallelize computation on
multiple machines and scale to large scenes.
