  Research on supervised learning algorithms in 3D scene understanding has
risen in prominence and witness great increases in performance across several
datasets. The leading force of this research is the problem of autonomous
driving followed by indoor scene segmentation. However, openly available 3D
data on these tasks mainly focuses on urban scenarios. In this paper, we
propose TS40K, a 3D point cloud dataset that encompasses more than 40,000 Km on
electrical transmission systems situated in European rural terrain. This is not
only a novel problem for the research community that can aid in the high-risk
mission of power-grid inspection, but it also offers 3D point clouds with
distinct characteristics from those in self-driving and indoor 3D data, such as
high point-density and no occlusion. In our dataset, each 3D point is labeled
with 1 out of 22 annotated classes. We evaluate the performance of
state-of-the-art methods on our dataset concerning 3D semantic segmentation and
3D object detection. Finally, we provide a comprehensive analysis of the
results along with key challenges such as using labels that were not originally
intended for learning tasks.
