Abstract. View-graph selection is a crucial step for accurate and effi-
cient large-scale structure from motion (sfm). 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 an optimization based formulation that
can be used to achieve these different reconstruction objectives with taskspecific cost modeling and 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 mainly focuses on application of
this framework with standard sfm pipeline for accurate and ghost-free
reconstructions of highly ambiguous datasets. To model selection costs
for this task, we introduce new disambiguation priors based on local geometry. We further demonstrate versatility of the method by using it
for the general objective of accurate and efficient reconstruction of largescale Internet datasets using costs based on well-known sfm priors