A Graph-CNN for 3D Point Cloud Classification

ABSTRACT Graph convolutional neural networks (Graph-CNNs) extend traditional CNNs to handle data that is supported on a graph. Major challenges when working with data on graphs are that the support set (the vertices of the graph) do not typically have a natural ordering, and in general, the topology of the graph is not regular (i.e., vertices do not all have the same number of neighbors). Thus, Graph-CNNs have huge potential to deal with 3D point cloud data which has been obtained from sampling a manifold. By the effective exploration of the point cloud local structure using the Graph-CNN, the proposed architecture achieves competitive performance on the 3D object classification benchmark ModelNet, and our architecture is more stable than competing schemes. Index Terms-- Graph convolutional neural networks, graph signal processing, 3D point cloud data, supervised learning 1. INTRODUCTION With the advent of very large datasets and improved computational capabilities, methods using convolutional neural networks (CNNs) now achieve state-of-the-art performance on a variety of tasks, including speech recognition and image classification. Many emerging applications give rise to data that may be viewed as being supported on the vertices of a graph, and field of graph signal processing (GSP) has developed filtering and other operations on graph signals [1, 2].