SGSLNet: stratified contextual graph pooling for point cloud segmentation with graph structural learning

Graph Convolutional Neural Networks (GCNs) have demonstrated significant efficiency and flexibility in processing irregular data. And since point clouds are essentially irregular points discretely distributed in space, GCNs have great potential for application in point cloud segmentation tasks. However, current GCNs struggle to learn global structure and local details efficiently. Furthermore, their excessive use of Max pooling results in the substantial loss of contextual structural information. To address the above problems, we present a novel hierarchical graph structure learning network (SGSLNet), which mainly consists of structure-aware Adaptive Graph Convolution (GAdaptive Conv) and Stratified Contextual Graph Pooling (SCGP). GAdaptive Conv is used to dynamically learn local geometric structures, while SCGP is employed to aggregate features and model global contextual structures. Our method not only learns global structure and local details concurrently but also reduces the loss of contextual structure information. We conduct extensive experiments on various datasets, including ShapeNetPart, S3DIS, and ScanNet v2. The results show that SGSLNet achieves state-of-the-art segmentation performance.