Feature Descriptors and Detectors are two main components of feature-based point cloud registration. However, little attention has been drawn to the explicit representation of local and global semantics in the learning of descriptors and detectors. In this paper, we present a framework that explicitly extracts dual-level descriptors and detectors and performs coarse-to-fine matching with them. First, to explicitly learn local and global semantics, we propose a hierarchical contrastive learning strategy, training the robust matching ability of high-level descriptors, and refining the local feature space using low-level descriptors. Furthermore, we propose to learn dual-level saliency maps that extract two groups of keypoints in two different senses. To overcome the weak supervision of binary matchability labels, we propose a ranking strategy to label the significance ranking of keypoints, and thus provide more fine-grained supervision signals. Finally, we propose a global-to-local matching scheme to obtain robust and accurate correspondences by leveraging the complementary dual-level features.Quantitative experiments on 3DMatch and KITTI odometry datasets show that our method achieves robust and accurate point cloud registration and outperforms recent keypoint-based methods.

翻译：特征描述符与检测器是基于特征的点云配准的两大核心组件。然而，现有研究较少关注描述符与检测器学习中局部与全局语义的显式表达。本文提出一个框架，能够显式提取双层描述符与检测器，并基于它们执行由粗到精的匹配。首先，为显式学习局部与全局语义，我们提出一种分层对比学习策略，训练高层描述符的鲁棒匹配能力，并利用低层描述符精炼局部特征空间。其次，我们提出学习双层显著性图，以从两种不同语义中提取两组关键点。为克服二值匹配性标签的弱监督问题，我们提出一种排序策略来标注关键点的显著性排序，从而提供更细粒度的监督信号。最后，我们提出一种全局到局部的匹配方案，通过利用互补的双层特征获取鲁棒且精确的对应关系。在3DMatch和KITTI里程计数据集上的定量实验表明，本方法实现了鲁棒且精确的点云配准，性能优于现有基于关键点的方法。