KISS-Matcher: Fast and Robust Point Cloud Registration Revisited
🤖 AI Summary
Addressing the challenge of simultaneously achieving robustness, efficiency, and generalization in global point cloud registration, this paper introduces an open-source C++ library. The proposed end-to-end pipeline integrates a lightweight Faster-PFH feature descriptor, a k-core graph-theoretic outlier pruning strategy, and robust pose solvers (e.g., RANSAC and TEASER+). Key contributions include: (i) Faster-PFH, which drastically reduces feature computation overhead while preserving discriminability; (ii) k-core pruning, lowering outlier rejection complexity from O(n²) to near-linear time; and (iii) a modular, highly extensible architecture that maintains high accuracy. Extensive experiments on standard benchmarks—including 3DMatch and KITTI—demonstrate that our method achieves 2–5× speedup over state-of-the-art robust registration approaches, with comparable registration accuracy, while supporting large-scale point clouds and cross-scenario generalization.
Application Category
📝 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 extit{KISS-Matcher}. KISS-Matcher combines a novel feature detector, extit{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}{ exttt{https://github.com/MIT-SPARK/KISS-Matcher}}.
Problem
Research questions and friction points this paper is trying to address.
Develops a fast robust point cloud registration library
Improves feature extraction with novel Faster-PFH detector
Reduces complexity via k-core graph pruning for outliers
Innovation
Methods, ideas, or system contributions that make the work stand out.
Novel feature detector Faster-PFH
k-core-based graph-theoretic pruning
Complete user-friendly registration pipeline
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