{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/kiss-matcher-fast-and-robust-point-cloud","title":"KISS-Matcher: Fast and Robust Point Cloud Registration Revisited","arxiv_id":"2409.15615","date":"2024-09-23","proceeding":null,"authors":["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 \\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}}.","url_abs":"https://arxiv.org/abs/2409.15615v2","url_pdf":"https://arxiv.org/pdf/2409.15615v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"kiss-matcher-fast-and-robust-point-cloud","repo_url":"https://github.com/mit-spark/kiss-matcher","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"point-cloud-registration","task_name":"Point Cloud Registration"}],"methods":[{"method_slug":null,"method_name":"Library"},{"method_slug":"pruning","method_name":"Pruning"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}