Multi-features guidance network for partial-to-partial point cloud registration
Abstract The recent extraction of hybrid features improves point cloud registration performance by emphasizing more integrated information. However, hybrid features ignore the large dimensional differences, big semantic gaps, and mutual interference between the shape features and spatial coordinates...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Wang, Hongyuan [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2021 |
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| Schlagwörter: |
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| Anmerkung: |
© The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2021 |
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| Übergeordnetes Werk: |
Enthalten in: Neural computing & applications - London : Springer, 1993, 34(2021), 2 vom: 14. Sept., Seite 1623-1634 |
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| Übergeordnetes Werk: |
volume:34 ; year:2021 ; number:2 ; day:14 ; month:09 ; pages:1623-1634 |
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| DOI / URN: |
10.1007/s00521-021-06464-y |
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| Katalog-ID: |
SPR046023178 |
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| 100 | 1 | |a Wang, Hongyuan |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a Multi-features guidance network for partial-to-partial point cloud registration |
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| 520 | |a Abstract The recent extraction of hybrid features improves point cloud registration performance by emphasizing more integrated information. However, hybrid features ignore the large dimensional differences, big semantic gaps, and mutual interference between the shape features and spatial coordinates. This paper proposes a novel Multi-Features Guidance Network (MFGNet) for partial-to-partial point cloud registration to overcome the intrinsic flaws of hybrid features, which leverages the shape features and the spatial coordinates to account for correspondences searching independently. The proposed network mainly includes four parts: keypoints’ feature extraction, correspondences search, correspondences credibility computation, and singular value decomposition (SVD), among which correspondences search and correspondences credibility computation are the cores of the network. Specifically, the correspondences search module utilizes the shape features and the spatial coordinates to guide correspondences matching independently and fusing the matching results to obtain the final matching matrix. Moreover, based on the conflicted relationship between the two matching matrices, the correspondences credibility computation module scores each correspondence pair’s reliability, which can reduce the impact of mismatched or non-matched points significantly. Empirical experiments on the ModelNet40 dataset validate the effectiveness of the proposed MFGNet, which achieves 0.19%$^\circ%$, 0.24%$^\circ%$ and 1.3%$^\circ%$ mean absolute errors for rotation matrix and 0.0010, 0.0011, and 0.0068 mean absolute errors for translation vectors, respectively, under the settings of unseen point clouds, unseen categories, and Gaussian noise. | ||
| 650 | 4 | |a Point cloud registration |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a 3D point matching |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Deep learning |7 (dpeaa)DE-He213 | |
| 700 | 1 | |a Liu, Xiang |0 (orcid)0000-0001-8877-3056 |4 aut | |
| 700 | 1 | |a Kang, Wen |4 aut | |
| 700 | 1 | |a Yan, Zhiqiang |4 aut | |
| 700 | 1 | |a Wang, Bingwen |4 aut | |
| 700 | 1 | |a Ning, Qianhao |4 aut | |
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10.1007/s00521-021-06464-y doi (DE-627)SPR046023178 (SPR)s00521-021-06464-y-e DE-627 ger DE-627 rakwb eng Wang, Hongyuan verfasserin aut Multi-features guidance network for partial-to-partial point cloud registration 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2021 Abstract The recent extraction of hybrid features improves point cloud registration performance by emphasizing more integrated information. However, hybrid features ignore the large dimensional differences, big semantic gaps, and mutual interference between the shape features and spatial coordinates. This paper proposes a novel Multi-Features Guidance Network (MFGNet) for partial-to-partial point cloud registration to overcome the intrinsic flaws of hybrid features, which leverages the shape features and the spatial coordinates to account for correspondences searching independently. The proposed network mainly includes four parts: keypoints’ feature extraction, correspondences search, correspondences credibility computation, and singular value decomposition (SVD), among which correspondences search and correspondences credibility computation are the cores of the network. Specifically, the correspondences search module utilizes the shape features and the spatial coordinates to guide correspondences matching independently and fusing the matching results to obtain the final matching matrix. Moreover, based on the conflicted relationship between the two matching matrices, the correspondences credibility computation module scores each correspondence pair’s reliability, which can reduce the impact of mismatched or non-matched points significantly. Empirical experiments on the ModelNet40 dataset validate the effectiveness of the proposed MFGNet, which achieves 0.19%$^\circ%$, 0.24%$^\circ%$ and 1.3%$^\circ%$ mean absolute errors for rotation matrix and 0.0010, 0.0011, and 0.0068 mean absolute errors for translation vectors, respectively, under the settings of unseen point clouds, unseen categories, and Gaussian noise. Point cloud registration (dpeaa)DE-He213 3D point matching (dpeaa)DE-He213 Deep learning (dpeaa)DE-He213 Liu, Xiang (orcid)0000-0001-8877-3056 aut Kang, Wen aut Yan, Zhiqiang aut Wang, Bingwen aut Ning, Qianhao aut Enthalten in Neural computing & applications London : Springer, 1993 34(2021), 2 vom: 14. Sept., Seite 1623-1634 (DE-627)271595574 (DE-600)1480526-1 1433-3058 nnns volume:34 year:2021 number:2 day:14 month:09 pages:1623-1634 https://dx.doi.org/10.1007/s00521-021-06464-y lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 34 2021 2 14 09 1623-1634 |
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10.1007/s00521-021-06464-y doi (DE-627)SPR046023178 (SPR)s00521-021-06464-y-e DE-627 ger DE-627 rakwb eng Wang, Hongyuan verfasserin aut Multi-features guidance network for partial-to-partial point cloud registration 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2021 Abstract The recent extraction of hybrid features improves point cloud registration performance by emphasizing more integrated information. However, hybrid features ignore the large dimensional differences, big semantic gaps, and mutual interference between the shape features and spatial coordinates. This paper proposes a novel Multi-Features Guidance Network (MFGNet) for partial-to-partial point cloud registration to overcome the intrinsic flaws of hybrid features, which leverages the shape features and the spatial coordinates to account for correspondences searching independently. The proposed network mainly includes four parts: keypoints’ feature extraction, correspondences search, correspondences credibility computation, and singular value decomposition (SVD), among which correspondences search and correspondences credibility computation are the cores of the network. Specifically, the correspondences search module utilizes the shape features and the spatial coordinates to guide correspondences matching independently and fusing the matching results to obtain the final matching matrix. Moreover, based on the conflicted relationship between the two matching matrices, the correspondences credibility computation module scores each correspondence pair’s reliability, which can reduce the impact of mismatched or non-matched points significantly. Empirical experiments on the ModelNet40 dataset validate the effectiveness of the proposed MFGNet, which achieves 0.19%$^\circ%$, 0.24%$^\circ%$ and 1.3%$^\circ%$ mean absolute errors for rotation matrix and 0.0010, 0.0011, and 0.0068 mean absolute errors for translation vectors, respectively, under the settings of unseen point clouds, unseen categories, and Gaussian noise. Point cloud registration (dpeaa)DE-He213 3D point matching (dpeaa)DE-He213 Deep learning (dpeaa)DE-He213 Liu, Xiang (orcid)0000-0001-8877-3056 aut Kang, Wen aut Yan, Zhiqiang aut Wang, Bingwen aut Ning, Qianhao aut Enthalten in Neural computing & applications London : Springer, 1993 34(2021), 2 vom: 14. Sept., Seite 1623-1634 (DE-627)271595574 (DE-600)1480526-1 1433-3058 nnns volume:34 year:2021 number:2 day:14 month:09 pages:1623-1634 https://dx.doi.org/10.1007/s00521-021-06464-y lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 34 2021 2 14 09 1623-1634 |
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10.1007/s00521-021-06464-y doi (DE-627)SPR046023178 (SPR)s00521-021-06464-y-e DE-627 ger DE-627 rakwb eng Wang, Hongyuan verfasserin aut Multi-features guidance network for partial-to-partial point cloud registration 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2021 Abstract The recent extraction of hybrid features improves point cloud registration performance by emphasizing more integrated information. However, hybrid features ignore the large dimensional differences, big semantic gaps, and mutual interference between the shape features and spatial coordinates. This paper proposes a novel Multi-Features Guidance Network (MFGNet) for partial-to-partial point cloud registration to overcome the intrinsic flaws of hybrid features, which leverages the shape features and the spatial coordinates to account for correspondences searching independently. The proposed network mainly includes four parts: keypoints’ feature extraction, correspondences search, correspondences credibility computation, and singular value decomposition (SVD), among which correspondences search and correspondences credibility computation are the cores of the network. Specifically, the correspondences search module utilizes the shape features and the spatial coordinates to guide correspondences matching independently and fusing the matching results to obtain the final matching matrix. Moreover, based on the conflicted relationship between the two matching matrices, the correspondences credibility computation module scores each correspondence pair’s reliability, which can reduce the impact of mismatched or non-matched points significantly. Empirical experiments on the ModelNet40 dataset validate the effectiveness of the proposed MFGNet, which achieves 0.19%$^\circ%$, 0.24%$^\circ%$ and 1.3%$^\circ%$ mean absolute errors for rotation matrix and 0.0010, 0.0011, and 0.0068 mean absolute errors for translation vectors, respectively, under the settings of unseen point clouds, unseen categories, and Gaussian noise. Point cloud registration (dpeaa)DE-He213 3D point matching (dpeaa)DE-He213 Deep learning (dpeaa)DE-He213 Liu, Xiang (orcid)0000-0001-8877-3056 aut Kang, Wen aut Yan, Zhiqiang aut Wang, Bingwen aut Ning, Qianhao aut Enthalten in Neural computing & applications London : Springer, 1993 34(2021), 2 vom: 14. Sept., Seite 1623-1634 (DE-627)271595574 (DE-600)1480526-1 1433-3058 nnns volume:34 year:2021 number:2 day:14 month:09 pages:1623-1634 https://dx.doi.org/10.1007/s00521-021-06464-y lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 34 2021 2 14 09 1623-1634 |
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10.1007/s00521-021-06464-y doi (DE-627)SPR046023178 (SPR)s00521-021-06464-y-e DE-627 ger DE-627 rakwb eng Wang, Hongyuan verfasserin aut Multi-features guidance network for partial-to-partial point cloud registration 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2021 Abstract The recent extraction of hybrid features improves point cloud registration performance by emphasizing more integrated information. However, hybrid features ignore the large dimensional differences, big semantic gaps, and mutual interference between the shape features and spatial coordinates. This paper proposes a novel Multi-Features Guidance Network (MFGNet) for partial-to-partial point cloud registration to overcome the intrinsic flaws of hybrid features, which leverages the shape features and the spatial coordinates to account for correspondences searching independently. The proposed network mainly includes four parts: keypoints’ feature extraction, correspondences search, correspondences credibility computation, and singular value decomposition (SVD), among which correspondences search and correspondences credibility computation are the cores of the network. Specifically, the correspondences search module utilizes the shape features and the spatial coordinates to guide correspondences matching independently and fusing the matching results to obtain the final matching matrix. Moreover, based on the conflicted relationship between the two matching matrices, the correspondences credibility computation module scores each correspondence pair’s reliability, which can reduce the impact of mismatched or non-matched points significantly. Empirical experiments on the ModelNet40 dataset validate the effectiveness of the proposed MFGNet, which achieves 0.19%$^\circ%$, 0.24%$^\circ%$ and 1.3%$^\circ%$ mean absolute errors for rotation matrix and 0.0010, 0.0011, and 0.0068 mean absolute errors for translation vectors, respectively, under the settings of unseen point clouds, unseen categories, and Gaussian noise. Point cloud registration (dpeaa)DE-He213 3D point matching (dpeaa)DE-He213 Deep learning (dpeaa)DE-He213 Liu, Xiang (orcid)0000-0001-8877-3056 aut Kang, Wen aut Yan, Zhiqiang aut Wang, Bingwen aut Ning, Qianhao aut Enthalten in Neural computing & applications London : Springer, 1993 34(2021), 2 vom: 14. Sept., Seite 1623-1634 (DE-627)271595574 (DE-600)1480526-1 1433-3058 nnns volume:34 year:2021 number:2 day:14 month:09 pages:1623-1634 https://dx.doi.org/10.1007/s00521-021-06464-y lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 34 2021 2 14 09 1623-1634 |
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10.1007/s00521-021-06464-y doi (DE-627)SPR046023178 (SPR)s00521-021-06464-y-e DE-627 ger DE-627 rakwb eng Wang, Hongyuan verfasserin aut Multi-features guidance network for partial-to-partial point cloud registration 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2021 Abstract The recent extraction of hybrid features improves point cloud registration performance by emphasizing more integrated information. However, hybrid features ignore the large dimensional differences, big semantic gaps, and mutual interference between the shape features and spatial coordinates. This paper proposes a novel Multi-Features Guidance Network (MFGNet) for partial-to-partial point cloud registration to overcome the intrinsic flaws of hybrid features, which leverages the shape features and the spatial coordinates to account for correspondences searching independently. The proposed network mainly includes four parts: keypoints’ feature extraction, correspondences search, correspondences credibility computation, and singular value decomposition (SVD), among which correspondences search and correspondences credibility computation are the cores of the network. Specifically, the correspondences search module utilizes the shape features and the spatial coordinates to guide correspondences matching independently and fusing the matching results to obtain the final matching matrix. Moreover, based on the conflicted relationship between the two matching matrices, the correspondences credibility computation module scores each correspondence pair’s reliability, which can reduce the impact of mismatched or non-matched points significantly. Empirical experiments on the ModelNet40 dataset validate the effectiveness of the proposed MFGNet, which achieves 0.19%$^\circ%$, 0.24%$^\circ%$ and 1.3%$^\circ%$ mean absolute errors for rotation matrix and 0.0010, 0.0011, and 0.0068 mean absolute errors for translation vectors, respectively, under the settings of unseen point clouds, unseen categories, and Gaussian noise. Point cloud registration (dpeaa)DE-He213 3D point matching (dpeaa)DE-He213 Deep learning (dpeaa)DE-He213 Liu, Xiang (orcid)0000-0001-8877-3056 aut Kang, Wen aut Yan, Zhiqiang aut Wang, Bingwen aut Ning, Qianhao aut Enthalten in Neural computing & applications London : Springer, 1993 34(2021), 2 vom: 14. Sept., Seite 1623-1634 (DE-627)271595574 (DE-600)1480526-1 1433-3058 nnns volume:34 year:2021 number:2 day:14 month:09 pages:1623-1634 https://dx.doi.org/10.1007/s00521-021-06464-y lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 34 2021 2 14 09 1623-1634 |
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Wang, Hongyuan @@aut@@ Liu, Xiang @@aut@@ Kang, Wen @@aut@@ Yan, Zhiqiang @@aut@@ Wang, Bingwen @@aut@@ Ning, Qianhao @@aut@@ |
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However, hybrid features ignore the large dimensional differences, big semantic gaps, and mutual interference between the shape features and spatial coordinates. This paper proposes a novel Multi-Features Guidance Network (MFGNet) for partial-to-partial point cloud registration to overcome the intrinsic flaws of hybrid features, which leverages the shape features and the spatial coordinates to account for correspondences searching independently. The proposed network mainly includes four parts: keypoints’ feature extraction, correspondences search, correspondences credibility computation, and singular value decomposition (SVD), among which correspondences search and correspondences credibility computation are the cores of the network. Specifically, the correspondences search module utilizes the shape features and the spatial coordinates to guide correspondences matching independently and fusing the matching results to obtain the final matching matrix. Moreover, based on the conflicted relationship between the two matching matrices, the correspondences credibility computation module scores each correspondence pair’s reliability, which can reduce the impact of mismatched or non-matched points significantly. 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Wang, Hongyuan |
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multi-features guidance network for partial-to-partial point cloud registration |
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Multi-features guidance network for partial-to-partial point cloud registration |
| abstract |
Abstract The recent extraction of hybrid features improves point cloud registration performance by emphasizing more integrated information. However, hybrid features ignore the large dimensional differences, big semantic gaps, and mutual interference between the shape features and spatial coordinates. This paper proposes a novel Multi-Features Guidance Network (MFGNet) for partial-to-partial point cloud registration to overcome the intrinsic flaws of hybrid features, which leverages the shape features and the spatial coordinates to account for correspondences searching independently. The proposed network mainly includes four parts: keypoints’ feature extraction, correspondences search, correspondences credibility computation, and singular value decomposition (SVD), among which correspondences search and correspondences credibility computation are the cores of the network. Specifically, the correspondences search module utilizes the shape features and the spatial coordinates to guide correspondences matching independently and fusing the matching results to obtain the final matching matrix. Moreover, based on the conflicted relationship between the two matching matrices, the correspondences credibility computation module scores each correspondence pair’s reliability, which can reduce the impact of mismatched or non-matched points significantly. Empirical experiments on the ModelNet40 dataset validate the effectiveness of the proposed MFGNet, which achieves 0.19%$^\circ%$, 0.24%$^\circ%$ and 1.3%$^\circ%$ mean absolute errors for rotation matrix and 0.0010, 0.0011, and 0.0068 mean absolute errors for translation vectors, respectively, under the settings of unseen point clouds, unseen categories, and Gaussian noise. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2021 |
| abstractGer |
Abstract The recent extraction of hybrid features improves point cloud registration performance by emphasizing more integrated information. However, hybrid features ignore the large dimensional differences, big semantic gaps, and mutual interference between the shape features and spatial coordinates. This paper proposes a novel Multi-Features Guidance Network (MFGNet) for partial-to-partial point cloud registration to overcome the intrinsic flaws of hybrid features, which leverages the shape features and the spatial coordinates to account for correspondences searching independently. The proposed network mainly includes four parts: keypoints’ feature extraction, correspondences search, correspondences credibility computation, and singular value decomposition (SVD), among which correspondences search and correspondences credibility computation are the cores of the network. Specifically, the correspondences search module utilizes the shape features and the spatial coordinates to guide correspondences matching independently and fusing the matching results to obtain the final matching matrix. Moreover, based on the conflicted relationship between the two matching matrices, the correspondences credibility computation module scores each correspondence pair’s reliability, which can reduce the impact of mismatched or non-matched points significantly. Empirical experiments on the ModelNet40 dataset validate the effectiveness of the proposed MFGNet, which achieves 0.19%$^\circ%$, 0.24%$^\circ%$ and 1.3%$^\circ%$ mean absolute errors for rotation matrix and 0.0010, 0.0011, and 0.0068 mean absolute errors for translation vectors, respectively, under the settings of unseen point clouds, unseen categories, and Gaussian noise. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2021 |
| abstract_unstemmed |
Abstract The recent extraction of hybrid features improves point cloud registration performance by emphasizing more integrated information. However, hybrid features ignore the large dimensional differences, big semantic gaps, and mutual interference between the shape features and spatial coordinates. This paper proposes a novel Multi-Features Guidance Network (MFGNet) for partial-to-partial point cloud registration to overcome the intrinsic flaws of hybrid features, which leverages the shape features and the spatial coordinates to account for correspondences searching independently. The proposed network mainly includes four parts: keypoints’ feature extraction, correspondences search, correspondences credibility computation, and singular value decomposition (SVD), among which correspondences search and correspondences credibility computation are the cores of the network. Specifically, the correspondences search module utilizes the shape features and the spatial coordinates to guide correspondences matching independently and fusing the matching results to obtain the final matching matrix. Moreover, based on the conflicted relationship between the two matching matrices, the correspondences credibility computation module scores each correspondence pair’s reliability, which can reduce the impact of mismatched or non-matched points significantly. Empirical experiments on the ModelNet40 dataset validate the effectiveness of the proposed MFGNet, which achieves 0.19%$^\circ%$, 0.24%$^\circ%$ and 1.3%$^\circ%$ mean absolute errors for rotation matrix and 0.0010, 0.0011, and 0.0068 mean absolute errors for translation vectors, respectively, under the settings of unseen point clouds, unseen categories, and Gaussian noise. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2021 |
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Multi-features guidance network for partial-to-partial point cloud registration |
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https://dx.doi.org/10.1007/s00521-021-06464-y |
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Liu, Xiang Kang, Wen Yan, Zhiqiang Wang, Bingwen Ning, Qianhao |
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Liu, Xiang Kang, Wen Yan, Zhiqiang Wang, Bingwen Ning, Qianhao |
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| doi_str |
10.1007/s00521-021-06464-y |
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2024-07-03T19:50:12.699Z |
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| score |
7.4002886 |