  Bundle adjustment is an important global optimization step in many structure
from motion pipelines. Performance is dependent on the speed of the linear
solver used to compute steps towards the optimum. For large problems, the
current state of the art scales superlinearly with the number of cameras in the
problem. We investigate the conditioning of global bundle adjustment problems
as the number of images increases in different regimes and fundamental
consequences in terms of superlinear scaling of the current state of the art
methods. We present an unsmoothed aggregation multigrid preconditioner that
accurately represents the global modes that underlie poor scaling of existing
methods and demonstrate solves of up to 13 times faster than the state of the
art on large, challenging problem sets.
