HD2Reg：用于点云配准的分层描述子与检测器
HD2Reg: Hierarchical Descriptors and Detectors for Point Cloud Registration
AI总结：
针对点云配准中特征描述子与检测器缺乏局部和全局语义显式表征的问题，提出HD2Reg框架，通过分层对比学习、双层级关键点检测及全局到局部匹配实现更优配准效果。
摘要：
特征描述子与检测器是基于特征的点云配准的两大核心组件。然而，现有研究对描述子与检测器学习过程中局部与全局语义的显式表征关注较少。本文提出一种框架，可显式提取双层级描述子与检测器，并基于二者实现由粗到精的匹配。首先，为显式学习局部与全局语义，我们提出分层对比学习策略，训练高层描述子的鲁棒匹配能力，并利用低层描述子优化局部特征空间。此外，我们提出学习双层级显著性图，以提取两种不同语义层面的关键点组。为解决二值可匹配性标签监督信号薄弱的问题，我们提出一种排序策略，用于标注关键点的重要性排序，从而提供更细粒度的监督信号。最后，我们提出全局到局部的匹配方案，通过利用互补的双层级特征获取鲁棒且精准的对应关系。在3DMatch与KITTI里程计数据集上的定量实验表明，本文方法实现了鲁棒精准的点云配准，性能优于近期基于关键点的方法。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
