GAGAT: Global Aware Graph Attention Network for 3D Classification and Segmentation

Graph Neural Networks have brought many breakthroughs in graph representation learning and boosted the state of the art in many data processing domains including point cloud learning. Graph structures provide the relative neighborhood information to unordered point clouds and help the exploration of topological structures for the understanding of complex scenes. Therefore, in this paper, we propose novel neural network architectures leveraging graph attention for point cloud-based classification and segmentation. The proposed architectures construct a connected graph from point cloud scans and employ a global aware attention module using global, local, and self-feature information for point cloud segmentation and classification. Various experiments on different datasets (ModelNet40 [1], ShapeNet [2], S3DIC [3], and Semantic3D [4]) show that our methods yield comparable results for classification, part segmentation, and semantic segmentation.