  We introduce a new outdoor urban 3D pointcloud dataset, covering a total area
of 2.7 $km^2$, sampled from three Swiss cities with different characteristics.
The dataset is manually annotated for semantic segmentation with per-point
labels, and is built using photogrammetry from images acquired by multirotors
equipped with high-resolution cameras. In contrast to datasets acquired with
ground LiDAR sensors, the resulting point clouds are uniformly dense and
complete, and are useful to disparate applications, including autonomous
driving, gaming and smart city planning. As a benchmark, we report quantitative
results of PointNet++, an established point-based deep 3D semantic segmentation
model; on this model, we additionally study the impact of using different
cities for model generalization.
