  Exploiting fine-grained semantic features on point cloud is still challenging
due to its irregular and sparse structure in a non-Euclidean space. Among
existing studies, PointNet provides an efficient and promising approach to
learn shape features directly on unordered 3D point cloud and has achieved
competitive performance. However, local feature that is helpful towards better
contextual learning is not considered. Meanwhile, attention mechanism shows
efficiency in capturing node representation on graph-based data by attending
over neighboring nodes. In this paper, we propose a novel neural network for
point cloud, dubbed GAPNet, to learn local geometric representations by
embedding graph attention mechanism within stacked Multi-Layer-Perceptron (MLP)
layers. Firstly, we introduce a GAPLayer to learn attention features for each
point by highlighting different attention weights on neighborhood. Secondly, in
order to exploit sufficient features, a multi-head mechanism is employed to
allow GAPLayer to aggregate different features from independent heads. Thirdly,
we propose an attention pooling layer over neighbors to capture local signature
aimed at enhancing network robustness. Finally, GAPNet applies stacked MLP
layers to attention features and local signature to fully extract local
geometric structures. The proposed GAPNet architecture is tested on the
ModelNet40 and ShapeNet part datasets, and achieves state-of-the-art
performance in both shape classification and part segmentation tasks.
