KISS-Matcher: Fast and Robust Point Cloud Registration Revisited

TL;DR

KISS-Matcher is a comprehensive open-source C++ library for point cloud registration that introduces a faster feature detector and an efficient outlier rejection method, significantly improving speed and scalability while maintaining accuracy.

Contribution

It presents a holistic registration pipeline with a novel feature detector and graph-based pruning, enhancing speed and robustness over existing methods.

Findings

Achieves faster registration compared to state-of-the-art methods.

Demonstrates superior scalability and broad applicability.

Maintains high accuracy despite speed improvements.

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.