Extract Descriptors for Point Cloud Registration by Graph Clustering Attention Network

Abstract

Extracting geometric descriptors in 3D vision is the first step. It plays an important role in 3D registration, 3D reconstruction, and other applications. The success of many 3D tasks is closely related to whether the geometric descriptor has accurate characteristics. Today, the main methods are divided into manual production and neural network learning. The applicability of descriptors is limited to a low-level point, corner, edge, and fixed neighborhood features. For this, we use the class attention of the point cloud. In order to extract class attention, the graph clustering approach is utilized. It can collect points with similar structures and divide regions dynamically. While maintaining rotation invariance, features can enhance their fit to the original data. Point attention and edge attention are used to describe the structural characteristics of point clouds. We combine the three attentions indicated before to improve the features obtained by the PointNet decoder. This feature can dynamically reflect the structure of the point cloud, which includes both soft shape information and rich detail information. Finally, the 3D descriptors are extracted with the FoldingNet decoder. Our method is validated on both indoor and outdoor datasets. The accuracy of the final result is improved by two percentage points.