论文标题
盖亚：针对弱监督点云语义分割的图形信息增益的注意网络
GaIA: Graphical Information Gain based Attention Network for Weakly Supervised Point Cloud Semantic Segmentation
论文作者
论文摘要
虽然点云语义分割是3D场景理解中的重要任务，但此任务需要一个耗时的全面注释标签的过程。为了解决这个问题，最近的研究在稀疏注释下采用了弱监督的学习方法。与现有研究不同，本研究旨在减少熵测量的认知不确定性，以进行精确的语义分割。我们提出了名为GAIA的图形信息增益的注意力网络，该网络基于可靠的信息来减轻每个点的熵。图形信息获得通过在目标点及其邻居之间采用相对熵来区分可靠点。我们进一步引入了基于锚固的添加角缘损失Arcpoint。 ArcPoint优化了未标记的点，该点包含高熵，朝着透明空间上标记的点的语义相似类别。 S3DIS和Scannet-V2数据集的实验结果表明，我们的框架优于现有的弱监督方法。我们已经在https://github.com/karel911/gaia上发布了Gaia。
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.
