  In this paper we present an autonomous system for acquiring close-range
high-resolution images that maximize the quality of a later-on 3D
reconstruction with respect to coverage, ground resolution and 3D uncertainty.
In contrast to previous work, our system uses the already acquired images to
predict the confidence in the output of a dense multi-view stereo approach
without executing it. This confidence encodes the likelihood of a successful
reconstruction with respect to the observed scene and potential camera
constellations. Our prediction module runs in real-time and can be trained
without any externally recorded ground truth. We use the confidence prediction
for on-site quality assurance and for planning further views that are tailored
for a specific multi-view stereo approach with respect to the given scene. We
demonstrate the capabilities of our approach with an autonomous Unmanned Aerial
Vehicle (UAV) in a challenging outdoor scenario.
