  We propose a new supervized learning framework for oversegmenting 3D point
clouds into superpoints. We cast this problem as learning deep embeddings of
the local geometry and radiometry of 3D points, such that the border of objects
presents high contrasts. The embeddings are computed using a lightweight neural
network operating on the points' local neighborhood. Finally, we formulate
point cloud oversegmentation as a graph partition problem with respect to the
learned embeddings.
  This new approach allows us to set a new state-of-the-art in point cloud
oversegmentation by a significant margin, on a dense indoor dataset (S3DIS) and
a sparse outdoor one (vKITTI). Our best solution requires over five times fewer
superpoints to reach similar performance than previously published methods on
S3DIS. Furthermore, we show that our framework can be used to improve
superpoint-based semantic segmentation algorithms, setting a new
state-of-the-art for this task as well.
