In this paper, we propose one novel model for point cloud semantic segmentation, which exploits both the local and global structures within the point cloud based on the contextual point representations. Specifically, we enrich each point representation by performing one novel gated fusion on the point itself and its contextual points. Afterwards, based on the enriched representation, we propose one novel graph pointnet module, relying on the graph attention block to dynamically compose and update each point representation within the local point cloud structure. Finally, we resort to the spatial-wise and channel-wise attention strategies to exploit the point cloud global structure and thereby yield the resulting semantic label for each point. Extensive results on the public point cloud databases, namely the S3DIS and ScanNet datasets, demonstrate the effectiveness of our proposed model, outperforming the state-of-the-art approaches. Our code for this paper is available at https://github.com/fly519/ELGS.

本篇论文使用上下文点表示方法，提出了一种基于点云局部和全局结构的创新模型，使用门控融合和图形点网络模块进行语义分割，借助空间性和通道性的注意力策略，以S3DIS和ScanNet数据集为基础，效果超过现有最先进的方法。

利用局部和全局结构进行点云语义分割，使用上下文点表示