A Geometry Feature Aggregation Method for Point Cloud Classification and Segmentation

Abstract

In spite of the good performance of convolutional neural network (CNN) and graph neural network (GNN) in 3D point cloud classification and segmentation at present, to aggregate local information of point clouds and improve the robustness of geometric transformation are still challenging problems. In order to tackle the problems, we propose Geometry Feature Aggregation Network (GFA-Net), which can effectively learn the context information of each point to aggregate local information, so as to enhance the robustness of rotation and translation. Compared with the current popular method GNN that convolves on nearby points in Euclidean space, GFA-Net can better aggregate the geometric features around the points. GFA-Net uses the Laplacian feature mapping to reduce dimensions, and aggregates the nearest neighbor features in the space after dimensionality reduction, and fuses them with the nearest neighbor features of Euclidean space, so as to better obtain the geometric features of each point. Then, points are grouped with geometric features, so that nearby points are insensitive to geometric transformations such as rotation and translation. This method allows GFA-Net to better obtain holistic geometry features, such as symmetry. In addition, we use attention mechanism instead of pooling, so that important neighborhood information can be learned automatically and information loss can be reduced. We conduct extensive experiments on public datasets ModelNet40 and ShapeNet Part. The experimental results show that GFA-Net achieves very good performance, which is very close to the current state-of-the-art methods, and GFA-Net has better robustness.