  While point cloud semantic segmentation is a significant task in 3D scene
understanding, this task demands a time-consuming process of fully annotating
labels. To address this problem, recent studies adopt a weakly supervised
learning approach under the sparse annotation. Different from the existing
studies, this study aims to reduce the epistemic uncertainty measured by the
entropy for a precise semantic segmentation. We propose the graphical
information gain based attention network called GaIA, which alleviates the
entropy of each point based on the reliable information. The graphical
information gain discriminates the reliable point by employing relative entropy
between target point and its neighborhoods. We further introduce anchor-based
additive angular margin loss, ArcPoint. The ArcPoint optimizes the unlabeled
points containing high entropy towards semantically similar classes of the
labeled points on hypersphere space. Experimental results on S3DIS and
ScanNet-v2 datasets demonstrate our framework outperforms the existing weakly
supervised methods. We have released GaIA at https://github.com/Karel911/GaIA.
