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.
@article{arxiv.2409.15615,
title = {KISS-Matcher: Fast and Robust Point Cloud Registration Revisited},
author = {Hyungtae Lim and Daebeom Kim and Gunhee Shin and Jingnan Shi and Ignacio Vizzo and Hyun Myung and Jaesik Park and Luca Carlone},
journal= {arXiv preprint arXiv:2409.15615},
year = {2025}
}