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.
翻译:在基于通信的点云登记中,将函文与点特征技术相匹配可能会导致极高的离差率。目前的方法仍然低效率、准确度和召回率。我们使用简单和直观的方法描述点云登记中的6-DOF(自由度)缩减过程,并根据函文图的可靠性提出外部清除战略。该方法根据给定的函文构建相应的图表,并设计图形节点的可靠性度概念,以便最佳选择候选人,并设计图形边缘的可靠性概念,以获得全球最大共识。该方法可以实现快速和准确的外端清除,同时逐步调整参数估计。关于模拟和质疑真实世界数据集的广泛实验表明,拟议的方法仍然能够有效地进行点云登记,即使是函外端比率也超过99%,效率也好于状态-艺术。代码见https://github.com/WPC-WHU/GROR。