  Extracting high-level structural information from 3D point clouds is
challenging but essential for tasks like urban planning or autonomous driving
requiring an advanced understanding of the scene at hand. Existing approaches
are still not able to produce high-quality results consistently while being
fast enough to be deployed in scenarios requiring interactivity. We propose to
utilize a novel set of features describing the local neighborhood on a
per-point basis via first and second order statistics as input for a simple and
compact classification network to distinguish between non-edge, sharp-edge, and
boundary points in the given data. Leveraging this feature embedding enables
our algorithm to outperform the state-of-the-art techniques in terms of quality
and processing time.
