  Camera equipped drones are nowadays being used to explore large scenes and
reconstruct detailed 3D maps. When free space in the scene is approximately
known, an offline planner can generate optimal plans to efficiently explore the
scene. However, for exploring unknown scenes, the planner must predict and
maximize usefulness of where to go on the fly. Traditionally, this has been
achieved using handcrafted utility functions. We propose to learn a better
utility function that predicts the usefulness of future viewpoints. Our learned
utility function is based on a 3D convolutional neural network. This network
takes as input a novel volumetric scene representation that implicitly captures
previously visited viewpoints and generalizes to new scenes. We evaluate our
method on several large 3D models of urban scenes using simulated depth
cameras. We show that our method outperforms existing utility measures in terms
of reconstruction performance and is robust to sensor noise.
