Robust Point Cloud Registration Framework Based on Deep Graph Matching
3D point cloud registration is a fundamental problem in computer vision and robotics. Recently, learning-based point cloud registration methods have made great progress. However, these methods are sensitive to outliers, which lead to more incorrect correspondences. In this paper, we propose a novel...
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| Vydáno v: | IEEE transactions on pattern analysis and machine intelligence Ročník 45; číslo 5; s. 1 - 13 |
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| Jazyk: | angličtina |
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IEEE
01.05.2023
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
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| ISSN: | 0162-8828, 1939-3539, 2160-9292, 1939-3539 |
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| Abstract | 3D point cloud registration is a fundamental problem in computer vision and robotics. Recently, learning-based point cloud registration methods have made great progress. However, these methods are sensitive to outliers, which lead to more incorrect correspondences. In this paper, we propose a novel deep graph matching-based framework for point cloud registration. Specifically, we first transform point clouds into graphs and extract deep features for each point. Then, we develop a module based on deep graph matching to calculate a soft correspondence matrix. By using graph matching, not only the local geometry of each point but also its structure and topology in a larger range are considered in establishing correspondences, so that more correct correspondences are found. We train the network with a loss directly defined on the correspondences, and in the test stage the soft correspondences are transformed into hard one-to-one correspondences so that registration can be performed by a correspondence-based solver. Furthermore, we introduce a transformer-based method to generate edges for graph construction, which further improves the quality of the correspondences. Extensive experiments on object-level and scene-level benchmark datasets show that the proposed method achieves state-of-the-art performance. The code is available at: https://github.com/fukexue/RGM . |
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| AbstractList | 3D point cloud registration is a fundamental problem in computer vision and robotics. Recently, learning-based point cloud registration methods have made great progress. However, these methods are sensitive to outliers, which lead to more incorrect correspondences. In this paper, we propose a novel deep graph matching-based framework for point cloud registration. Specifically, we first transform point clouds into graphs and extract deep features for each point. Then, we develop a module based on deep graph matching to calculate a soft correspondence matrix. By using graph matching, not only the local geometry of each point but also its structure and topology in a larger range are considered in establishing correspondences, so that more correct correspondences are found. We train the network with a loss directly defined on the correspondences, and in the test stage the soft correspondences are transformed into hard one-to-one correspondences so that registration can be performed by a correspondence-based solver. Furthermore, we introduce a transformer-based method to generate edges for graph construction, which further improves the quality of the correspondences. Extensive experiments on object-level and scene-level benchmark datasets show that the proposed method achieves state-of-the-art performance. The code is available at: https://github.com/fukexue/RGM . 3D point cloud registration is a fundamental problem in computer vision and robotics. Recently, learning-based point cloud registration methods have made great progress. However, these methods are sensitive to outliers, which lead to more incorrect correspondences. In this paper, we propose a novel deep graph matching-based framework for point cloud registration. Specifically, we first transform point clouds into graphs and extract deep features for each point. Then, we develop a module based on deep graph matching to calculate a soft correspondence matrix. By using graph matching, not only the local geometry of each point but also its structure and topology in a larger range are considered in establishing correspondences, so that more correct correspondences are found. We train the network with a loss directly defined on the correspondences, and in the test stage the soft correspondences are transformed into hard one-to-one correspondences so that registration can be performed by a correspondence-based solver. Furthermore, we introduce a transformer-based method to generate edges for graph construction, which further improves the quality of the correspondences. Extensive experiments on object-level and scene-level benchmark datasets show that the proposed method achieves state-of-the-art performance. 3D point cloud registration is a fundamental problem in computer vision and robotics. Recently, learning-based point cloud registration methods have made great progress. However, these methods are sensitive to outliers, which lead to more incorrect correspondences. In this paper, we propose a novel deep graph matching-based framework for point cloud registration. Specifically, we first transform point clouds into graphs and extract deep features for each point. Then, we develop a module based on deep graph matching to calculate a soft correspondence matrix. By using graph matching, not only the local geometry of each point but also its structure and topology in a larger range are considered in establishing correspondences, so that more correct correspondences are found. We train the network with a loss directly defined on the correspondences, and in the test stage the soft correspondences are transformed into hard one-to-one correspondences so that registration can be performed by a correspondence-based solver. Furthermore, we introduce a transformer-based method to generate edges for graph construction, which further improves the quality of the correspondences. Extensive experiments on object-level and scene-level benchmark datasets show that the proposed method achieves state-of-the-art performance.3D point cloud registration is a fundamental problem in computer vision and robotics. Recently, learning-based point cloud registration methods have made great progress. However, these methods are sensitive to outliers, which lead to more incorrect correspondences. In this paper, we propose a novel deep graph matching-based framework for point cloud registration. Specifically, we first transform point clouds into graphs and extract deep features for each point. Then, we develop a module based on deep graph matching to calculate a soft correspondence matrix. By using graph matching, not only the local geometry of each point but also its structure and topology in a larger range are considered in establishing correspondences, so that more correct correspondences are found. We train the network with a loss directly defined on the correspondences, and in the test stage the soft correspondences are transformed into hard one-to-one correspondences so that registration can be performed by a correspondence-based solver. Furthermore, we introduce a transformer-based method to generate edges for graph construction, which further improves the quality of the correspondences. Extensive experiments on object-level and scene-level benchmark datasets show that the proposed method achieves state-of-the-art performance. |
| Author | Luo, Jiazheng Luo, Xiaoyuan Zhang, Chenxi Liu, Shaolei Fu, Kexue Wang, Manning |
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| References | ref13 ref57 ref12 ref56 ref59 ref14 Wang (ref45) ref58 ref53 ref52 ref11 ref55 ref10 ref54 ref17 ref16 ref19 ref18 Vaswani (ref44) ref51 ref50 ref46 ref48 ref47 ref41 ref49 ref8 ref7 ref9 Chang (ref63) 2015 ref4 ref3 ref6 ref5 Yu (ref25) 2021; 34 ref40 ref35 ref34 ref37 ref36 ref31 ref30 ref33 ref32 ref2 ref1 ref39 ref38 Ulyanov (ref61) 2016 Devlin (ref43) 2018 ref24 ref68 ref23 ref67 ref26 ref69 ref20 ref64 ref22 ref66 ref21 ref28 ref27 Sarode (ref15) ref29 Lucas (ref42) ref60 ref62 Zhou (ref65) 2018 |
| References_xml | – ident: ref36 doi: 10.1007/978-3-030-01267-0_43 – year: 2015 ident: ref63 article-title: Shapenet: An information-rich 3D model repository – ident: ref48 doi: 10.1109/CVPR.2018.00028 – ident: ref23 doi: 10.1109/CVPR42600.2020.00639 – ident: ref38 doi: 10.1007/978-3-319-46475-6_47 – ident: ref40 doi: 10.1145/3326362 – ident: ref26 doi: 10.1145/358669.358692 – ident: ref12 doi: 10.1109/CVPR.2019.01207 – ident: ref24 doi: 10.1109/CVPR.2017.29 – ident: ref55 doi: 10.1109/CVPR.2012.6247667 – ident: ref6 doi: 10.1109/ICRA.2018.8460653 – start-page: 8814 volume-title: Proc. Adv. Neural Inf. Process. Syst. ident: ref45 article-title: PRNet: Self-supervised learning for partial-to-partial registration – ident: ref28 doi: 10.1007/BF02278710 – ident: ref18 doi: 10.1109/ICCV.2019.00362 – ident: ref41 doi: 10.1023/B:VISI.0000011205.11775.fd – ident: ref53 doi: 10.1109/ICCV.2011.6126445 – ident: ref15 article-title: PCRNet: Point cloud registration network using pointnet encoding – ident: ref51 doi: 10.1109/ICCV.2019.00651 – ident: ref35 doi: 10.1109/TPAMI.2010.223 – ident: ref8 doi: 10.1117/12.57955 – ident: ref58 doi: 10.1109/DSW.2018.8439919 – ident: ref13 doi: 10.1109/ROBOT.2009.5152473 – start-page: 5998 volume-title: Proc. Adv. Neural Inf. Process. Syst. ident: ref44 article-title: Attention is all you need – volume: 34 start-page: 23872 year: 2021 ident: ref25 article-title: Cofinet: Reliable coarse-to-fine correspondences for robust pointcloud registration publication-title: Adv. Neural Inf. Process. Syst. – ident: ref68 doi: 10.1109/CVPR46437.2021.01158 – ident: ref29 doi: 10.1002/nav.3800020109 – ident: ref69 doi: 10.1109/ROBOT.2009.5152473 – ident: ref54 doi: 10.1109/CVPR.2017.628 – ident: ref31 doi: 10.1109/ICCV.2017.324 – ident: ref10 doi: 10.1109/TPAMI.2015.2513405 – ident: ref37 doi: 10.1007/978-3-030-01258-8_28 – ident: ref7 doi: 10.1109/ICCV.2007.4409077 – ident: ref52 doi: 10.1109/CVPR46437.2021.00038 – ident: ref32 doi: 10.1145/3306346.3323037 – ident: ref67 doi: 10.1109/CVPR.2019.00569 – ident: ref22 doi: 10.1109/ICCV.2019.00905 – year: 2016 ident: ref61 article-title: Instance normalization: The missing ingredient for fast stylization – ident: ref16 doi: 10.1109/CVPR.2019.00733 – ident: ref27 doi: 10.1109/ICCV.2019.00315 – ident: ref64 doi: 10.1109/ICCV48922.2021.00312 – ident: ref9 doi: 10.15607/RSS.2009.V.021 – ident: ref30 doi: 10.1109/CVPR46437.2021.00878 – ident: ref4 doi: 10.1007/s12206-020-0540-6 – start-page: 121 volume-title: Proc. Imag. Understanding Workshop ident: ref42 article-title: An iterative image registration technique with an application to stereo vision – year: 2018 ident: ref65 article-title: Open3D: A modern library for 3D data processing – ident: ref3 doi: 10.1109/ICRA.2018.8461224 – ident: ref47 doi: 10.1214/aoms/1177703591 – ident: ref49 doi: 10.1007/978-3-030-01228-1_37 – ident: ref39 doi: 10.1109/CVPR.2017.16 – ident: ref34 doi: 10.1109/IM.2001.924423 – ident: ref1 doi: 10.1109/CVPR.2019.00655 – ident: ref21 doi: 10.1109/CVPR46437.2021.00425 – ident: ref11 doi: 10.1109/CVPR.2016.613 – ident: ref33 doi: 10.1561/2300000035 – ident: ref62 doi: 10.1109/CVPR.2015.7298801 – ident: ref66 doi: 10.1109/cvpr.2017.29 – ident: ref17 doi: 10.1109/CVPR46437.2021.01257 – ident: ref57 doi: 10.1109/CVPR42600.2020.00499 – ident: ref60 doi: 10.1142/9789812797926_0003 – ident: ref19 doi: 10.1109/CVPR42600.2020.01184 – ident: ref14 doi: 10.1016/j.cviu.2014.04.011 – ident: ref2 doi: 10.1109/TNNLS.2020.3015992 – ident: ref20 doi: 10.1007/978-3-030-58586-0_23 – ident: ref5 doi: 10.1109/TRO.2018.2882730 – ident: ref50 doi: 10.1109/CVPR.2019.00110 – ident: ref59 doi: 10.1109/CVPR.2018.00284 – ident: ref56 doi: 10.1109/ICCV.2013.11 – ident: ref46 doi: 10.1007/978-3-030-58558-7_43 – year: 2018 ident: ref43 article-title: Bert: Pre-training of deep bidirectional transformers for language understanding |
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| SubjectTerms | Computer vision Correspondence Deep learning Feature extraction Geometry Graph Matching Graph theory Neural networks Outliers (statistics) Point cloud compression Point Cloud Registration Prediction algorithms Registration Robotics Three dimensional models Topology Transformers |
| Title | Robust Point Cloud Registration Framework Based on Deep Graph Matching |
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