KISS-Matcher: Fast and Robust Point Cloud Registration Revisited
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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866908451005792256 |
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| author | Lim, Hyungtae Kim, Daebeom Shin, Gunhee Shi, Jingnan Vizzo, Ignacio Myung, Hyun Park, Jaesik Carlone, Luca |
| author_facet | Lim, Hyungtae Kim, Daebeom Shin, Gunhee Shi, Jingnan Vizzo, Ignacio Myung, Hyun Park, Jaesik Carlone, Luca |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2409_15615 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | KISS-Matcher: Fast and Robust Point Cloud Registration Revisited Lim, Hyungtae Kim, Daebeom Shin, Gunhee Shi, Jingnan Vizzo, Ignacio Myung, Hyun Park, Jaesik Carlone, Luca Computer Vision and Pattern Recognition Robotics 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. |
| title | KISS-Matcher: Fast and Robust Point Cloud Registration Revisited |
| topic | Computer Vision and Pattern Recognition Robotics |
| url | https://arxiv.org/abs/2409.15615 |