  Real-time 3D reconstruction enables fast dense mapping of the environment
which benefits numerous applications, such as navigation or live evaluation of
an emergency. In contrast to most real-time capable approaches, our approach
does not need an explicit depth sensor. Instead, we only rely on a video stream
from a camera and its intrinsic calibration. By exploiting the self-motion of
the unmanned aerial vehicle (UAV) flying with oblique view around buildings, we
estimate both camera trajectory and depth for selected images with enough novel
content. To create a 3D model of the scene, we rely on a three-stage processing
chain. First, we estimate the rough camera trajectory using a simultaneous
localization and mapping (SLAM) algorithm. Once a suitable constellation is
found, we estimate depth for local bundles of images using a Multi-View Stereo
(MVS) approach and then fuse this depth into a global surfel-based model. For
our evaluation, we use 55 video sequences with diverse settings, consisting of
both synthetic and real scenes. We evaluate not only the generated
reconstruction but also the intermediate products and achieve competitive
results both qualitatively and quantitatively. At the same time, our method can
keep up with a 30 fps video for a resolution of 768x448 pixels.
