KISS-Matcher: Fast and Robust Point Cloud Registration Revisited
Hyungtae Lim, Daebeom Kim, Gunhee Shin, Jingnan Shi, Ignacio Vizzo, Hyun Myung, Jaesik Park, Luca Carlone
Abstract
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.
BibTeX
@inproceedings{icra2025_kissmatcherfasta,
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},
booktitle = {ICRA 2025},
year = {2025}
}