  With the recent availability and affordability of commercial depth sensors
and 3D scanners, an increasing number of 3D (i.e., RGBD, point cloud) datasets
have been publicized to facilitate research in 3D computer vision. However,
existing datasets either cover relatively small areas or have limited semantic
annotations. Fine-grained understanding of urban-scale 3D scenes is still in
its infancy. In this paper, we introduce SensatUrban, an urban-scale UAV
photogrammetry point cloud dataset consisting of nearly three billion points
collected from three UK cities, covering 7.6 km^2. Each point in the dataset
has been labelled with fine-grained semantic annotations, resulting in a
dataset that is three times the size of the previous existing largest
photogrammetric point cloud dataset. In addition to the more commonly
encountered categories such as road and vegetation, urban-level categories
including rail, bridge, and river are also included in our dataset. Based on
this dataset, we further build a benchmark to evaluate the performance of
state-of-the-art segmentation algorithms. In particular, we provide a
comprehensive analysis and identify several key challenges limiting urban-scale
point cloud understanding. The dataset is available at
http://point-cloud-analysis.cs.ox.ac.uk.
