A New Outlier Removal Strategy Based on Reliability of Correspondence Graph for Fast Point Cloud Registration
This addresses the challenge of fast and accurate point cloud registration for applications like robotics and 3D scanning, though it is an incremental improvement over existing methods.
The paper tackles the problem of high outlier ratios in point cloud registration by proposing an outlier removal strategy based on the reliability of a correspondence graph, achieving effective registration even with over 99% outliers and better efficiency than state-of-the-art methods.
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.