Point Cloud Registration Based on Learning Gaussian Mixture Models With Global-Weighted Local Representations

In the field of point cloud registration, the ability to characterize the point cloud is core to improve the registration performance. Previous methods either convert point clouds as probability density models but ignore the rich feature of point clouds or only extract the local feature of point clouds without considering the global information. They did not fully utilize the point cloud information, so the characterization abilities of these methods are limited. To solve the above problems, we propose a point cloud registration based on learning Gaussian mixture models (GMMs) with global-weighted local representations. On the one hand, the point cloud is converted to GMM for registration. Unlike discrete point cloud data, GMM is a compact and lightweight representation. On the other hand, we generate GMM by extracting unique local features and global information from the point cloud. The global information is used to weigh the local features. Thus, the resulting GMM is a distribution with global-weighted local feature information representation ability, fully exploring the point cloud's local and global information. At the same time, we design a learning guide module to directly solve the transformation without following the expectation maximization (EM)-solving paradigm. Benefiting from the combination of GMM and learning deep information, this formulation greatly improves the ability to characterize point clouds. Our method shows superiority in registration accuracy and generalization performance on synthetic and real-world datasets. The source code will be made public.