EANet: A Point Cloud Registration Network Based on EdgeConv with Dual Attention Mechanism

Abstract

In machine vision, point cloud registration is one of the core elements, which has been applied to many fields such as robot localisation, medical image processing, and autonomous driving. The main problem solved by point cloud registration is to solve the rotation matrix and translation vectors from one point cloud to another. This paper proposes a point cloud registration network based on EdgeConv with spatial attention mechanism. EdgeConv can dynamically construct graph structure and build topological relationships within the point cloud, so that each point can obtain multi-level feature representation; the attention mechanism can capture contextual information and improve the accuracy of the registration. Experimental results show that EANet has higher registration accuracy, stronger generalisation ability and robustness compared to ICP, Go ICP, FGR, PCRNet and PointNetLK.