  Besides local features, global information plays an essential role in
semantic segmentation, while recent works usually fail to explicitly extract
the meaningful global information and make full use of it. In this paper, we
propose a SceneEncoder module to impose a scene-aware guidance to enhance the
effect of global information. The module predicts a scene descriptor, which
learns to represent the categories of objects existing in the scene and
directly guides the point-level semantic segmentation through filtering out
categories not belonging to this scene. Additionally, to alleviate segmentation
noise in local region, we design a region similarity loss to propagate
distinguishing features to their own neighboring points with the same label,
leading to the enhancement of the distinguishing ability of point-wise
features. We integrate our methods into several prevailing networks and conduct
extensive experiments on benchmark datasets ScanNet and ShapeNet. Results show
that our methods greatly improve the performance of baselines and achieve
state-of-the-art performance.
