Graph Attention Neural Networks For Point Cloud Recognition

With the rapid development of automatic driving, point cloud recognition is becoming an important problem for 3D understanding. Among existing works, they don't take frill advantage of local structural information and the relationship between points is difficult to be represented. In this paper, we propose graph attention neural network (GANN) to enhance the structural representation of point clouds by relational degree between points. We construct graph structure on point cloud and use the attention mechanism to compute the relationship of different nodes in a neighborhood and then embedding them into the aggregation operation designed. Finally, node's features can be updated by multiple graph attention layer over time, so that we can obtain more plentiful node feature representation and local structural feature. Extensive experiments on popular benchmarks demonstrate that our methods are effective for point cloud recognition.