Exploiting Local and Global Structure for Point Cloud Semantic Segmentation with Contextual Point Representations
This work addresses semantic segmentation in 3D point clouds for applications like robotics and autonomous driving, presenting an incremental improvement over existing methods.
The paper tackles point cloud semantic segmentation by exploiting local and global structures with contextual point representations, achieving state-of-the-art performance on S3DIS and ScanNet datasets.
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.