  The application of deep learning to 3D point clouds is challenging due to its
lack of order. Inspired by the point embeddings of PointNet and the edge
embeddings of DGCNNs, we propose three improvements to the task of point cloud
analysis. First, we introduce a novel feature-attentive neural network layer, a
FAT layer, that combines both global point-based features and local edge-based
features in order to generate better embeddings. Second, we find that applying
the same attention mechanism across two different forms of feature map
aggregation, max pooling and average pooling, gives better performance than
either alone. Third, we observe that residual feature reuse in this setting
propagates information more effectively between the layers, and makes the
network easier to train. Our architecture achieves state-of-the-art results on
the task of point cloud classification, as demonstrated on the ModelNet40
dataset, and an extremely competitive performance on the ShapeNet part
segmentation challenge.
