Plan3D introduces a new method that effciently computes a set of viewpoints and trajectories for high-quality 3D reconstructions in outdoor environments. The 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 fight time of quadcopters. To this end, Plan3D introduces 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 this method lies a hierarchical volumetric representation that allows the algorithm to distinguish between unknown, free, and occupied space. Furthermore, the information gain based formulation leverages this representation to handle occlusions in an effcient manner. In addition to the surface geometry, Plan3D utilizes the free-space information to avoid obstacles and determine collision-free fight paths. This tool can be used to specify the region of interest and to plan trajectories.

Authors: BENJAMIN HEPP, ETH Zurich, MATTHIAS NIESSNER, Technical University of Munich, Stanford University, OTMAR HILLIGES, ETH Zurich