Graph-PBN: Graph-based parallel branch network for efficient point cloud learning

In recent years, approaches based on graph convolutional networks (GCNs) have achieved state-of-the-art performance in point cloud learning. The typical pipeline of GCNs is modeled as a two-stage learning process: graph construction and feature learning. We argue that such process exhibits low efficiency because a high percentage of the total time is consumed during the graph construction process when a large amount of sparse data are required to be accessed rather than on actual feature learning. To alleviate this problem, we propose a graph-based parallel branch network (Graph-PBN) that introduces a parallel branch structure to point cloud learning in this study. In particular, Graph-PBN is composed of two branches: the PointNet branch and the GCN branch. PointNet exhibits advantages in memory access and computational cost, while GCN behaves better in local context modeling. The two branches are combined in our architecture to utilize the potential of PointNet and GCN fully, facilitating the achievement of efficient and accurate recognition results. To better aggregate the features of each node in GCN, we investigate a novel operator, called EAGConv, to augment their local context by fully utilizing geometric and semantic features in a local graph. We conduct experiments on several benchmark datasets, and experiment results validate the significant performance of our method compared with other state-of-the-art approaches. Our code will be made publicly available at https://github.com/zhangcheng828/Graph-PBN.