LAUSR

The point cloud registration is important and necessary for the applications of changing detection, deformation monitoring, etc., which is also challenging due to the vast clustered points, irregular and complex structures of spatial objects, and quality effects of the labeled corresponding points. Aiming at this problem, we design an end-to-end 3-dimensional (3D) graph deep learning framework of point cloud registration which can simultaneously learn the detector (graph attention expression) and the descriptor (graph deep feature) for point cloud registration in a weakly supervised way, so that the learned detector and descriptor promote each other in the process of model optimization. Then, the detector is used to automatically extract the keypoints and the descriptor describes the deep feature of each keypoint. In the framework, we innovatively propose a new module (named as MLP_GCN), which fuses multi-layer perceptron (MLP) and graph convolutional network (GCN). The MLP_GCN module is further integrated into the detector branch and descriptor branch to fully express the detector and descriptor of point cloud. In the training process of the framework, we rotate and translate the point cloud randomly to form the training data by a weakly supervised way, which can save plenty of manually labeling time of corresponding points. In the experiments, our method can achieve better results of point cloud registration in comparison with other methods, which verities the advantages of the proposed method.