# MPCR-Net: Multiple Partial Point Clouds Registration Network Using a Global Template
###### Abstract  
With advancements in photoelectric technology and computer image processing technology, the visual measurement method based on point clouds is gradually being applied to the 3D measurement of large workpieces. Point cloud registration is a key step in 3D measurement, and its registration accuracy directly affects the accuracy of 3D measurements. In this study, we designed a novel MPCR-Net for multiple partial point cloud registration networks. First, an ideal point cloud was extracted from the CAD model of the workpiece and used as the global template. Next, a deep neural network was used to search for the corresponding point groups between each partial point cloud and the global template point cloud. Then, the rigid body transformation matrix was learned according to these correspondence point groups to realize the registration of each partial point cloud. Finally, the iterative closest point algorithm was used to optimize the registration results to obtain the final point cloud model of the workpiece. We conducted point cloud registration experiments on untrained models and actual workpieces, and by comparing them with existing point cloud registration methods, we verified that the MPCR-Net could improve the accuracy and robustness of the 3D point cloud registration. point cloud registration; template point cloud; multiple partial point cloud; deep learning 2021 2021 11 10535. 11.  
registration error between partial point clouds under multiple poses, thereby improving the reconstruction accuracy. However, traditional point cloud registration methods, such as the turntable method [7] and the labeling method [8], are handcrafted methods with disadvantages such as low efficiency, high requirements for equipment accuracy, and missing point clouds in labeled areas. With the development of deep learning technology and a greater number of publicly available datasets for point cloud models, many scholars have attempted to use deep learning methods to achieve point cloud registration and verify its effectiveness through experiments. However, to reduce computational complexity and improve the efficiency of 3D reconstruction, the existing deep learning-based point cloud registration methods often degenerate the multiple partial point cloud registration problem into a pairwise registration problem [9]. Then, by optimizing the relative spatial pose between the pairwise partial point clouds, the final reconstruction model of the workpiece is constructed based on the pairwise registration results. However, due to the camera pose and environmental limitations, it is often impossible to ensure a sufficient overlap area between the pairwise point clouds. If the relationship between the partial point clouds and the overall structure of the workpiece is ignored, only the registration of partial point clouds is performed. This will increase the difficulty of point cloud registration, reduce the registration accuracy, and increase the final 3D reconstruction error. The following are the remaining gaps in point cloud registration algorithms that need to be addressed:  
1. Some algorithms require the structures of pairwise point clouds to be the same. If the geometric structures of the pairwise point clouds are quite different, the registration accuracy will decrease;
2. Some algorithms can complete the registration of two partially overlapping point clouds through partial-to-partial point-cloud registration methods. However, these methods rely on the individual training of specific partial data of the point cloud to establish the correspondence point relationship between the pairwise point clouds. Moreover, the registration accuracy is very sensitive to changes in the points. From the above analysis, it can be concluded that existing deep learning-based point cloud registration methods can only be used for scene reconstruction and other occasions with low accuracy requirements. They are not suitable for the 3D reconstruction of large workpieces with high accuracy requirements.