GaIA: Graphical Information Gain based Attention Network for Weakly Supervised Point Cloud Semantic Segmentation
This work addresses the problem of reducing annotation costs for 3D scene understanding, presenting an incremental improvement over existing weakly supervised methods.
The paper tackles weakly supervised point cloud semantic segmentation by reducing epistemic uncertainty via entropy minimization, achieving state-of-the-art performance on S3DIS and ScanNet-v2 datasets.
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.