While global point cloud registration systems have advanced significantly in all aspects, many studies have focused on specific components, such as feature extraction, graph-theoretic pruning, or pose solvers. In this paper, we take a holistic view on the registration problem and develop an open-source and versatile C++ library for point cloud registration, called \textit{KISS-Matcher}. KISS-Matcher combines a novel feature detector, \textit{Faster-PFH}, that improves over the classical fast point feature histogram (FPFH). Moreover, it adopts a $k$-core-based graph-theoretic pruning to reduce the time complexity of rejecting outlier correspondences. Finally, it combines these modules in a complete, user-friendly, and ready-to-use pipeline. As verified by extensive experiments, KISS-Matcher has superior scalability and broad applicability, achieving a substantial speed-up compared to state-of-the-art outlier-robust registration pipelines while preserving accuracy. Our code will be available at \href{https://github.com/MIT-SPARK/KISS-Matcher}{\texttt{https://github.com/MIT-SPARK/KISS-Matcher}}.

本研究针对点云配准领域中的多种具体组件进行的快速发展，提出了一种全面的解决方案，开发了开源的C++库KISS-Matcher。该库结合了新型特征检测器Faster-PFH和基于$k$-core的图论修剪技术，显著提升了配准速度和应用广度，同时保持了高准确率，展示了优越的可扩展性。

KISS-Matcher：快速而稳健的点云配准再探