KISS-Matcher加速点云配准，兼顾速度与精度。
KISS-Matcher: Fast and Robust Point Cloud Registration Revisited
- 提出新特征检测器Faster-PFH，比传统FPFH更快。
- 采用k-core图剪枝，显著降低异常对应关系的计算量。
- 开源易用，适合需要高效配准的工程应用。
尽管全局点云配准系统在各方面已取得显著进展，但许多研究集中于特定组件，如特征提取、基于图论的剪枝或位姿求解。本文从整体视角重新审视配准问题，开发了一个开源且通用的C++库KISS-Matcher。该库结合了新型特征检测器Faster-PFH，其性能优于经典快速点特征直方图（FPFH）。同时，采用基于k-core的图论剪枝方法，有效降低异常对应关系的拒绝时间复杂度。最终，将这些模块整合为一个完整、用户友好且开箱即用的流水线。大量实验验证了KISS-Matcher具有优异的可扩展性和广泛适用性，在保持精度的同时，相较于现有最先进鲁棒配准管道实现了显著提速。代码将公开于https://github.com/MIT-SPARK/KISS-Matcher。
原文摘要 · Abstract (English)
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 KISS-Matcher. KISS-Matcher combines a novel feature detector, 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 https://github.com/MIT-SPARK/KISS-Matcher.
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