Small unmanned aerial vehicles (UAVs) are ideal capturing devices for
high-resolution urban 3D reconstructions using multi-view stereo. Nevertheless, practical
considerations such as safety usually mean that access to the scan
target is often only available for a short amount of time, especially
in urban environments. It therefore becomes crucial to perform both
view and path planning to minimize flight time while ensuring complete
and accurate reconstructions.
In this work, we address the challenge of automatic view and path
planning for UAV-based aerial imaging with the goal of urban
reconstruction from multi-view stereo. To this end, we develop a novel
continuous optimization approach using heuristics for multi-view
stereo reconstruction quality and apply it to the problem of path
planning. Even for large scan areas, our method generates paths in
only a few minutes, and is therefore ideally suited for deployment in
the field.
To evaluate our method, we introduce and describe a detailed
benchmark dataset for UAV path planning in urban environments which
can also be used to evaluate future research efforts on this
topic. Using this dataset and both synthetic and real data, we
demonstrate survey-grade urban reconstructions with ground resolutions
of 1 cm or better on large areas (30,000 m^2).