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We introduce a new method that efficiently computes a set of viewpoints andtrajectories for high-quality 3D reconstructions in outdoor environments. Ourgoal is to automatically explore an unknown area, and obtain a complete 3D scanof a region of interest (e.g., a large building). Images from a commodity RGBcamera, mounted on an autonomously navigated quadcopter, are fed into amulti-view stereo reconstruction pipeline that produces high-quality resultsbut is computationally expensive. In this setting, the scanning result isconstrained by the restricted flight time of quadcopters. To this end, weintroduce a novel optimization strategy that respects these constraints bymaximizing the information gain from sparsely-sampled view points whilelimiting the total travel distance of the quadcopter. At the core of our methodlies a hierarchical volumetric representation that allows the algorithm todistinguish between unknown, free, and occupied space. Furthermore, ourinformation gain based formulation leverages this representation to handleocclusions in an efficient manner. In addition to the surface geometry, weutilize the free-space information to avoid obstacles and determinecollision-free flight paths. Our tool can be used to specify the region ofinterest and to plan trajectories. We demonstrate our method by obtaining anumber of compelling 3D reconstructions, and provide a thorough quantitativeevaluation showing improvement over previous state-of-the-art and regularpatterns.

Plan3D: Viewpoint and Trajectory Optimization for Aerial Multi-View Stereo Reconstruction