  Image datasets have been steadily growing in size, harming the feasibility
and efficiency of large-scale 3D reconstruction methods. In this paper, a novel
approach for scaling Multi-View Stereo (MVS) algorithms up to arbitrarily large
collections of images is proposed. Specifically, the problem of reconstructing
the 3D model of an entire city is targeted, starting from a set of videos
acquired by a moving vehicle equipped with several high-resolution cameras.
Initially, the presented method exploits an approximately uniform distribution
of poses and geometry and builds a set of overlapping clusters. Then, an
Integer Linear Programming (ILP) problem is formulated for each cluster to
select an optimal subset of views that guarantees both visibility and
matchability. Finally, local point clouds for each cluster are separately
computed and merged. Since clustering is independent from pairwise visibility
information, the proposed algorithm runs faster than existing literature and
allows for a massive parallelization. Extensive testing on urban data are
discussed to show the effectiveness and the scalability of this approach.
