  View-graph is an essential input to large-scale structure from motion (SfM)
pipelines. Accuracy and efficiency of large-scale SfM is crucially dependent on
the input view-graph. Inconsistent or inaccurate edges can lead to inferior or
wrong reconstruction. Most SfM methods remove `undesirable' images and pairs
using several, fixed heuristic criteria, and propose tailor-made solutions to
achieve specific reconstruction objectives such as efficiency, accuracy, or
disambiguation. In contrast to these disparate solutions, we propose a single
optimization framework that can be used to achieve these different
reconstruction objectives with task-specific cost modeling. We also construct a
very efficient network-flow based formulation for its approximate solution. The
abstraction brought on by this selection mechanism separates the challenges
specific to datasets and reconstruction objectives from the standard SfM
pipeline and improves its generalization. This paper demonstrates the
application of the proposed view-graph framework with standard SfM pipeline for
two particular use-cases, (i) accurate and ghost-free reconstructions of highly
ambiguous datasets using costs based on disambiguation priors, and (ii)
accurate and efficient reconstruction of large-scale Internet datasets using
costs based on commonly used priors.
