  Registration is a basic yet crucial task in point cloud processing. In
correspondence-based point cloud registration, matching correspondences by
point feature techniques may lead to an extremely high outlier ratio. Current
methods still suffer from low efficiency, accuracy, and recall rate. We use a
simple and intuitive method to describe the 6-DOF (degree of freedom)
curtailment process in point cloud registration and propose an outlier removal
strategy based on the reliability of the correspondence graph. The method
constructs the corresponding graph according to the given correspondences and
designs the concept of the reliability degree of the graph node for optimal
candidate selection and the reliability degree of the graph edge to obtain the
global maximum consensus set. The presented method could achieve fast and
accurate outliers removal along with gradual aligning parameters estimation.
Extensive experiments on simulations and challenging real-world datasets
demonstrate that the proposed method can still perform effective point cloud
registration even the correspondence outlier ratio is over 99%, and the
efficiency is better than the state-of-the-art. Code is available at
https://github.com/WPC-WHU/GROR.
