KISS-Matcher: Fast and Robust Point Cloud Registration Revisited
This work provides a faster and robust solution for point cloud registration, which is incremental as it builds on existing components but integrates them into a holistic pipeline.
The paper tackles point cloud registration by developing KISS-Matcher, an open-source C++ library that integrates a novel feature detector and graph-theoretic pruning, achieving a substantial speed-up compared to state-of-the-art methods while preserving accuracy.
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.