  An efficient solution to semantic segmentation of large-scale indoor scene
point clouds is proposed in this work. It is named GSIP (Green Segmentation of
Indoor Point clouds) and its performance is evaluated on a representative
large-scale benchmark -- the Stanford 3D Indoor Segmentation (S3DIS) dataset.
GSIP has two novel components: 1) a room-style data pre-processing method that
selects a proper subset of points for further processing, and 2) a new feature
extractor which is extended from PointHop. For the former, sampled points of
each room form an input unit. For the latter, the weaknesses of PointHop's
feature extraction when extending it to large-scale point clouds are identified
and fixed with a simpler processing pipeline. As compared with PointNet, which
is a pioneering deep-learning-based solution, GSIP is green since it has
significantly lower computational complexity and a much smaller model size.
Furthermore, experiments show that GSIP outperforms PointNet in segmentation
performance for the S3DIS dataset.
