Exploiting Local and Global Structure for Point Cloud Semantic Segmentation with Contextual Point Representations

Part of Advances in Neural Information Processing Systems 32 (NeurIPS 2019)

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Xu Wang, Jingming He, Lin Ma


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 onthe contextual point representations. Specifically, we enrich each point represen-tation by performing one novel gated fusion on the point itself and its contextualpoints. Afterwards, based on the enriched representation, we propose one novelgraph pointnet module, relying on the graph attention block to dynamically com-pose and update each point representation within the local point cloud structure.Finally, we resort to the spatial-wise and channel-wise attention strategies to exploitthe point cloud global structure and thereby yield the resulting semantic label foreach point. Extensive results on the public point cloud databases, namely theS3DIS 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.