  We propose a novel deep learning-based framework to tackle the challenge of
semantic segmentation of large-scale 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).

Computer Vision and Pattern Recognition;Machine Learning;Neural and Evolutionary Computing