Abstract
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-ofthe-art for this task as well.