CPCRNet: Capture for Point Cloud Registration

In a point cloud registration task, the feature extraction ability of the network directly affects the final registration effect. Previous work has tended to ignore the local information of point clouds and the context information between two point clouds in the feature extraction. To overcome the poor feature extraction ability and lack of robustness of existing registration algorithms, this paper, inspired by the graph attention network, proposes a learning-based point cloud registration framework that uses the existing network to extract point cloud features. We call it CPCRNet. Furthermore, a Capture module based on graph attention (GAT) is proposed to re-integrate the point cloud context information. Experimental results indicate that Capture, the core feature extraction module in the network, has a significant impact on the registration effect, with a mean square error, root mean square error, and mean absolute error between the ground truth and predicted values lower than those of competing methods. Moreover, experimental results for ModelNet40 and ShapeNet Part datasets show that the errors are not significantly changed by noise, indicating that CPCRNet has higher registration accuracy and better robustness compared to existing algorithms.