Abstract
We propose a novel deep learning-based framework to
tackle the challenge of semantic segmentation of largescale point clouds of millions of points. We argue that the
organization of 3D point clouds can be efficiently captured
by a structure called superpoint graph (SPG), derived
from a partition of the scanned scene into geometrically
homogeneous elements. SPGs offer a compact yet rich
representation of contextual relationships between object
parts, which is then exploited by a graph convolutional
network. Our framework sets a new state of the art for
segmenting outdoor LiDAR scans (+11.9 and +8.8 mIoU
points for both Semantic3D test sets), as well as indoor
scans (+12.4 mIoU points for the S3DIS dataset).