IEEE Trans Pattern Anal Mach Intell. 2023 Jul;45(7):7986-8002. doi: 10.1109/TPAMI.2022.3226498. Epub 2023 Jun 5.
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 (false correspondence) ratio. Current outlier removal methods still suffer from low efficiency, accuracy, and recall rate. We use an 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 achieves 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.
配准是点云处理中的一项基本而关键的任务。在基于对应点的点云配准中，通过点特征技术匹配对应点可能会导致极高的异常值（错误对应）比例。当前的异常值去除方法仍然存在效率、准确性和召回率低的问题。我们使用直观的方法来描述点云配准中的 6 自由度（自由度）截断过程，并提出了一种基于对应图可靠性的异常值去除策略。该方法根据给定的对应关系构建对应图，并设计了图节点可靠性度的概念，用于最优候选选择和图边的可靠性度，以获得全局最大一致集。所提出的方法能够实现快速、准确的异常值去除，并逐步估计对齐参数。在模拟和具有挑战性的真实世界数据集上的广泛实验表明，即使对应异常值比例超过 99%，该方法仍能有效地进行点云配准，并且效率优于现有技术。代码可在 https://github.com/WPC-WHU/GROR 上获得。
