Abstract
Global Structure-from-Motion (SfM) techniques have
demonstrated superior efficiency and accuracy than the
conventional incremental approach in many recent studies. This work proposes a divide-and-conquer framework
to solve very large global SfM at the scale of millions of images. Specifically, we first divide all images into multiple
partitions that preserve strong data association for wellposed and parallel local motion averaging. Then, we solve
a global motion averaging that determines cameras at partition boundaries and a similarity transformation per partition to register all cameras in a single coordinate frame.
Finally, local and global motion averaging are iterated until convergence. Since local camera poses are fixed during
the global motion average, we can avoid caching the whole
reconstruction in memory at once. This distributed framework significantly enhances the efficiency and robustness of
large-scale motion averaging