  We introduce a new method that efficiently computes a set of viewpoints and
trajectories for high-quality 3D reconstructions in outdoor environments. Our
goal is to automatically explore an unknown area, and obtain a complete 3D scan
of a region of interest (e.g., a large building). Images from a commodity RGB
camera, mounted on an autonomously navigated quadcopter, are fed into a
multi-view stereo reconstruction pipeline that produces high-quality results
but is computationally expensive. In this setting, the scanning result is
constrained by the restricted flight time of quadcopters. To this end, we
introduce a novel optimization strategy that respects these constraints by
maximizing the information gain from sparsely-sampled view points while
limiting the total travel distance of the quadcopter. At the core of our method
lies a hierarchical volumetric representation that allows the algorithm to
distinguish between unknown, free, and occupied space. Furthermore, our
information gain based formulation leverages this representation to handle
occlusions in an efficient manner. In addition to the surface geometry, we
utilize the free-space information to avoid obstacles and determine
collision-free flight paths. Our tool can be used to specify the region of
interest and to plan trajectories. We demonstrate our method by obtaining a
number of compelling 3D reconstructions, and provide a thorough quantitative
evaluation showing improvement over previous state-of-the-art and regular
patterns.
