  With the tide of artificial intelligence, we try to apply deep learning to
understand 3D data. Point cloud is an important 3D data structure, which can
accurately and directly reflect the real world. In this paper, we propose a
simple and effective network, which is named PyramNet, suites for point cloud
object classification and semantic segmentation in 3D scene. We design two new
operators: Graph Embedding Module(GEM) and Pyramid Attention Network(PAN).
Specifically, GEM projects point cloud onto the graph and practices the
covariance matrix to explore the relationship between points, so as to improve
the local feature expression ability of the model. PAN assigns some strong
semantic features to each point to retain fine geometric features as much as
possible. Furthermore, we provide extensive evaluation and analysis for the
effectiveness of PyramNet. Empirically, we evaluate our model on ModelNet40,
ShapeNet and S3DIS.
