Robust point cloud registration based on topological graph and Cauchy weighted
Point Cloud Registration (PCR) is a fundamental and important issue in photogrammetry and computer vision. Its goal is to find rigid transformations that register multiple 3D point sets. This paper proposes a robust and efficient PCR method based on topological graph and Cauchy weighted...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Li, Jiayuan [verfasserIn] Zhao, Pengcheng [verfasserIn] Hu, Qingwu [verfasserIn] Ai, Mingyao [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2019 |
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| Schlagwörter: |
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| Übergeordnetes Werk: |
Enthalten in: ISPRS journal of photogrammetry and remote sensing - International Society for Photogrammetry and Remote Sensing ; ID: gnd/132008-7, Amsterdam [u.a.] : Elsevier, 1989, 160, Seite 244-259 |
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| Übergeordnetes Werk: |
volume:160 ; pages:244-259 |
| DOI / URN: |
10.1016/j.isprsjprs.2019.12.008 |
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| Katalog-ID: |
ELV003476545 |
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| 520 | |a Point Cloud Registration (PCR) is a fundamental and important issue in photogrammetry and computer vision. Its goal is to find rigid transformations that register multiple 3D point sets. This paper proposes a robust and efficient PCR method based on topological graph and Cauchy weighted l q -norm. Our method does not require initializations and is highly robust to outliers and partial overlaps. It contains two major steps: (1) correspondence-based coarse registration, which is called Weighted l q Coarse Registration (W l q CR). In the W l q CR method, we represent feature point sets as topological graphs and transform the point matching problem to an edge matching problem. We build a mathematical model for edge correspondence maximization. We also present an edge voting strategy to distinguish potential correct matches from mismatches. Then, we define a concept called edge vector, which has a property that it is invariant to translations. Based on this property, six Degrees of Freedoms (DoF) PCR problem can be simplified into two three DoF subproblems, i.e., rotation estimation and translation estimation. (2) fine registration based on Weighted l q Iterative Closest Point (W l q ICP). We propose a new ICP method called W l q ICP, which is much more robust to partial overlaps compared with traditional ICP. In both rotation estimation and W l q ICP, we use a new Cauchy weighted l q -norm ( 0 < q < 1 ) instead of l 2 -norm for object function construction, which has a high degree of robustness. Extensive experiments on both simulated and real data demonstrate the power of the proposed method, i.e., our method is more robust (is able to tolerate up to 99% of outliers) and much faster than compared state-of-the-art methods (W l q CR is almost two orders of magnitude faster than RANdom SAmple Consesus (RANSAC) and its variants under 95% of outliers). The source code will be made publicly available in http://www.escience.cn/people/lijiayuan/index.html. | ||
| 650 | 4 | |a Point Cloud Registration (PCR) | |
| 650 | 4 | |a Coarse-to-fine registration | |
| 650 | 4 | |a Feature correspondence | |
| 650 | 4 | |a Iterative Closest Point (ICP) | |
| 650 | 4 | |a Robust estimation | |
| 700 | 1 | |a Zhao, Pengcheng |e verfasserin |0 (orcid)0000-0002-1581-634X |4 aut | |
| 700 | 1 | |a Hu, Qingwu |e verfasserin |0 (orcid)0000-0003-0866-6678 |4 aut | |
| 700 | 1 | |a Ai, Mingyao |e verfasserin |4 aut | |
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10.1016/j.isprsjprs.2019.12.008 doi (DE-627)ELV003476545 (ELSEVIER)S0924-2716(19)30296-5 DE-627 ger DE-627 rda eng 550 DE-600 38.73 bkl 74.41 bkl Li, Jiayuan verfasserin (orcid)0000-0002-9850-1668 aut Robust point cloud registration based on topological graph and Cauchy weighted 2019 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Point Cloud Registration (PCR) is a fundamental and important issue in photogrammetry and computer vision. Its goal is to find rigid transformations that register multiple 3D point sets. This paper proposes a robust and efficient PCR method based on topological graph and Cauchy weighted l q -norm. Our method does not require initializations and is highly robust to outliers and partial overlaps. It contains two major steps: (1) correspondence-based coarse registration, which is called Weighted l q Coarse Registration (W l q CR). In the W l q CR method, we represent feature point sets as topological graphs and transform the point matching problem to an edge matching problem. We build a mathematical model for edge correspondence maximization. We also present an edge voting strategy to distinguish potential correct matches from mismatches. Then, we define a concept called edge vector, which has a property that it is invariant to translations. Based on this property, six Degrees of Freedoms (DoF) PCR problem can be simplified into two three DoF subproblems, i.e., rotation estimation and translation estimation. (2) fine registration based on Weighted l q Iterative Closest Point (W l q ICP). We propose a new ICP method called W l q ICP, which is much more robust to partial overlaps compared with traditional ICP. In both rotation estimation and W l q ICP, we use a new Cauchy weighted l q -norm ( 0 < q < 1 ) instead of l 2 -norm for object function construction, which has a high degree of robustness. Extensive experiments on both simulated and real data demonstrate the power of the proposed method, i.e., our method is more robust (is able to tolerate up to 99% of outliers) and much faster than compared state-of-the-art methods (W l q CR is almost two orders of magnitude faster than RANdom SAmple Consesus (RANSAC) and its variants under 95% of outliers). The source code will be made publicly available in http://www.escience.cn/people/lijiayuan/index.html. Point Cloud Registration (PCR) Coarse-to-fine registration Feature correspondence Iterative Closest Point (ICP) Robust estimation Zhao, Pengcheng verfasserin (orcid)0000-0002-1581-634X aut Hu, Qingwu verfasserin (orcid)0000-0003-0866-6678 aut Ai, Mingyao verfasserin aut Enthalten in International Society for Photogrammetry and Remote Sensing ; ID: gnd/132008-7 ISPRS journal of photogrammetry and remote sensing Amsterdam [u.a.] : Elsevier, 1989 160, Seite 244-259 Online-Ressource (DE-627)320504557 (DE-600)2012663-3 (DE-576)096806567 0924-2716 nnns volume:160 pages:244-259 GBV_USEFLAG_U SYSFLAG_U GBV_ELV SSG-OPC-GGO SSG-OPC-GEO GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 38.73 Geodäsie 74.41 Luftaufnahmen Photogrammetrie AR 160 244-259 |
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10.1016/j.isprsjprs.2019.12.008 doi (DE-627)ELV003476545 (ELSEVIER)S0924-2716(19)30296-5 DE-627 ger DE-627 rda eng 550 DE-600 38.73 bkl 74.41 bkl Li, Jiayuan verfasserin (orcid)0000-0002-9850-1668 aut Robust point cloud registration based on topological graph and Cauchy weighted 2019 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Point Cloud Registration (PCR) is a fundamental and important issue in photogrammetry and computer vision. Its goal is to find rigid transformations that register multiple 3D point sets. This paper proposes a robust and efficient PCR method based on topological graph and Cauchy weighted l q -norm. Our method does not require initializations and is highly robust to outliers and partial overlaps. It contains two major steps: (1) correspondence-based coarse registration, which is called Weighted l q Coarse Registration (W l q CR). In the W l q CR method, we represent feature point sets as topological graphs and transform the point matching problem to an edge matching problem. We build a mathematical model for edge correspondence maximization. We also present an edge voting strategy to distinguish potential correct matches from mismatches. Then, we define a concept called edge vector, which has a property that it is invariant to translations. Based on this property, six Degrees of Freedoms (DoF) PCR problem can be simplified into two three DoF subproblems, i.e., rotation estimation and translation estimation. (2) fine registration based on Weighted l q Iterative Closest Point (W l q ICP). We propose a new ICP method called W l q ICP, which is much more robust to partial overlaps compared with traditional ICP. In both rotation estimation and W l q ICP, we use a new Cauchy weighted l q -norm ( 0 < q < 1 ) instead of l 2 -norm for object function construction, which has a high degree of robustness. Extensive experiments on both simulated and real data demonstrate the power of the proposed method, i.e., our method is more robust (is able to tolerate up to 99% of outliers) and much faster than compared state-of-the-art methods (W l q CR is almost two orders of magnitude faster than RANdom SAmple Consesus (RANSAC) and its variants under 95% of outliers). The source code will be made publicly available in http://www.escience.cn/people/lijiayuan/index.html. Point Cloud Registration (PCR) Coarse-to-fine registration Feature correspondence Iterative Closest Point (ICP) Robust estimation Zhao, Pengcheng verfasserin (orcid)0000-0002-1581-634X aut Hu, Qingwu verfasserin (orcid)0000-0003-0866-6678 aut Ai, Mingyao verfasserin aut Enthalten in International Society for Photogrammetry and Remote Sensing ; ID: gnd/132008-7 ISPRS journal of photogrammetry and remote sensing Amsterdam [u.a.] : Elsevier, 1989 160, Seite 244-259 Online-Ressource (DE-627)320504557 (DE-600)2012663-3 (DE-576)096806567 0924-2716 nnns volume:160 pages:244-259 GBV_USEFLAG_U SYSFLAG_U GBV_ELV SSG-OPC-GGO SSG-OPC-GEO GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 38.73 Geodäsie 74.41 Luftaufnahmen Photogrammetrie AR 160 244-259 |
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10.1016/j.isprsjprs.2019.12.008 doi (DE-627)ELV003476545 (ELSEVIER)S0924-2716(19)30296-5 DE-627 ger DE-627 rda eng 550 DE-600 38.73 bkl 74.41 bkl Li, Jiayuan verfasserin (orcid)0000-0002-9850-1668 aut Robust point cloud registration based on topological graph and Cauchy weighted 2019 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Point Cloud Registration (PCR) is a fundamental and important issue in photogrammetry and computer vision. Its goal is to find rigid transformations that register multiple 3D point sets. This paper proposes a robust and efficient PCR method based on topological graph and Cauchy weighted l q -norm. Our method does not require initializations and is highly robust to outliers and partial overlaps. It contains two major steps: (1) correspondence-based coarse registration, which is called Weighted l q Coarse Registration (W l q CR). In the W l q CR method, we represent feature point sets as topological graphs and transform the point matching problem to an edge matching problem. We build a mathematical model for edge correspondence maximization. We also present an edge voting strategy to distinguish potential correct matches from mismatches. Then, we define a concept called edge vector, which has a property that it is invariant to translations. Based on this property, six Degrees of Freedoms (DoF) PCR problem can be simplified into two three DoF subproblems, i.e., rotation estimation and translation estimation. (2) fine registration based on Weighted l q Iterative Closest Point (W l q ICP). We propose a new ICP method called W l q ICP, which is much more robust to partial overlaps compared with traditional ICP. In both rotation estimation and W l q ICP, we use a new Cauchy weighted l q -norm ( 0 < q < 1 ) instead of l 2 -norm for object function construction, which has a high degree of robustness. Extensive experiments on both simulated and real data demonstrate the power of the proposed method, i.e., our method is more robust (is able to tolerate up to 99% of outliers) and much faster than compared state-of-the-art methods (W l q CR is almost two orders of magnitude faster than RANdom SAmple Consesus (RANSAC) and its variants under 95% of outliers). The source code will be made publicly available in http://www.escience.cn/people/lijiayuan/index.html. Point Cloud Registration (PCR) Coarse-to-fine registration Feature correspondence Iterative Closest Point (ICP) Robust estimation Zhao, Pengcheng verfasserin (orcid)0000-0002-1581-634X aut Hu, Qingwu verfasserin (orcid)0000-0003-0866-6678 aut Ai, Mingyao verfasserin aut Enthalten in International Society for Photogrammetry and Remote Sensing ; ID: gnd/132008-7 ISPRS journal of photogrammetry and remote sensing Amsterdam [u.a.] : Elsevier, 1989 160, Seite 244-259 Online-Ressource (DE-627)320504557 (DE-600)2012663-3 (DE-576)096806567 0924-2716 nnns volume:160 pages:244-259 GBV_USEFLAG_U SYSFLAG_U GBV_ELV SSG-OPC-GGO SSG-OPC-GEO GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 38.73 Geodäsie 74.41 Luftaufnahmen Photogrammetrie AR 160 244-259 |
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10.1016/j.isprsjprs.2019.12.008 doi (DE-627)ELV003476545 (ELSEVIER)S0924-2716(19)30296-5 DE-627 ger DE-627 rda eng 550 DE-600 38.73 bkl 74.41 bkl Li, Jiayuan verfasserin (orcid)0000-0002-9850-1668 aut Robust point cloud registration based on topological graph and Cauchy weighted 2019 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Point Cloud Registration (PCR) is a fundamental and important issue in photogrammetry and computer vision. Its goal is to find rigid transformations that register multiple 3D point sets. This paper proposes a robust and efficient PCR method based on topological graph and Cauchy weighted l q -norm. Our method does not require initializations and is highly robust to outliers and partial overlaps. It contains two major steps: (1) correspondence-based coarse registration, which is called Weighted l q Coarse Registration (W l q CR). In the W l q CR method, we represent feature point sets as topological graphs and transform the point matching problem to an edge matching problem. We build a mathematical model for edge correspondence maximization. We also present an edge voting strategy to distinguish potential correct matches from mismatches. Then, we define a concept called edge vector, which has a property that it is invariant to translations. Based on this property, six Degrees of Freedoms (DoF) PCR problem can be simplified into two three DoF subproblems, i.e., rotation estimation and translation estimation. (2) fine registration based on Weighted l q Iterative Closest Point (W l q ICP). We propose a new ICP method called W l q ICP, which is much more robust to partial overlaps compared with traditional ICP. In both rotation estimation and W l q ICP, we use a new Cauchy weighted l q -norm ( 0 < q < 1 ) instead of l 2 -norm for object function construction, which has a high degree of robustness. Extensive experiments on both simulated and real data demonstrate the power of the proposed method, i.e., our method is more robust (is able to tolerate up to 99% of outliers) and much faster than compared state-of-the-art methods (W l q CR is almost two orders of magnitude faster than RANdom SAmple Consesus (RANSAC) and its variants under 95% of outliers). The source code will be made publicly available in http://www.escience.cn/people/lijiayuan/index.html. Point Cloud Registration (PCR) Coarse-to-fine registration Feature correspondence Iterative Closest Point (ICP) Robust estimation Zhao, Pengcheng verfasserin (orcid)0000-0002-1581-634X aut Hu, Qingwu verfasserin (orcid)0000-0003-0866-6678 aut Ai, Mingyao verfasserin aut Enthalten in International Society for Photogrammetry and Remote Sensing ; ID: gnd/132008-7 ISPRS journal of photogrammetry and remote sensing Amsterdam [u.a.] : Elsevier, 1989 160, Seite 244-259 Online-Ressource (DE-627)320504557 (DE-600)2012663-3 (DE-576)096806567 0924-2716 nnns volume:160 pages:244-259 GBV_USEFLAG_U SYSFLAG_U GBV_ELV SSG-OPC-GGO SSG-OPC-GEO GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 38.73 Geodäsie 74.41 Luftaufnahmen Photogrammetrie AR 160 244-259 |
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10.1016/j.isprsjprs.2019.12.008 doi (DE-627)ELV003476545 (ELSEVIER)S0924-2716(19)30296-5 DE-627 ger DE-627 rda eng 550 DE-600 38.73 bkl 74.41 bkl Li, Jiayuan verfasserin (orcid)0000-0002-9850-1668 aut Robust point cloud registration based on topological graph and Cauchy weighted 2019 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Point Cloud Registration (PCR) is a fundamental and important issue in photogrammetry and computer vision. Its goal is to find rigid transformations that register multiple 3D point sets. This paper proposes a robust and efficient PCR method based on topological graph and Cauchy weighted l q -norm. Our method does not require initializations and is highly robust to outliers and partial overlaps. It contains two major steps: (1) correspondence-based coarse registration, which is called Weighted l q Coarse Registration (W l q CR). In the W l q CR method, we represent feature point sets as topological graphs and transform the point matching problem to an edge matching problem. We build a mathematical model for edge correspondence maximization. We also present an edge voting strategy to distinguish potential correct matches from mismatches. Then, we define a concept called edge vector, which has a property that it is invariant to translations. Based on this property, six Degrees of Freedoms (DoF) PCR problem can be simplified into two three DoF subproblems, i.e., rotation estimation and translation estimation. (2) fine registration based on Weighted l q Iterative Closest Point (W l q ICP). We propose a new ICP method called W l q ICP, which is much more robust to partial overlaps compared with traditional ICP. In both rotation estimation and W l q ICP, we use a new Cauchy weighted l q -norm ( 0 < q < 1 ) instead of l 2 -norm for object function construction, which has a high degree of robustness. Extensive experiments on both simulated and real data demonstrate the power of the proposed method, i.e., our method is more robust (is able to tolerate up to 99% of outliers) and much faster than compared state-of-the-art methods (W l q CR is almost two orders of magnitude faster than RANdom SAmple Consesus (RANSAC) and its variants under 95% of outliers). The source code will be made publicly available in http://www.escience.cn/people/lijiayuan/index.html. Point Cloud Registration (PCR) Coarse-to-fine registration Feature correspondence Iterative Closest Point (ICP) Robust estimation Zhao, Pengcheng verfasserin (orcid)0000-0002-1581-634X aut Hu, Qingwu verfasserin (orcid)0000-0003-0866-6678 aut Ai, Mingyao verfasserin aut Enthalten in International Society for Photogrammetry and Remote Sensing ; ID: gnd/132008-7 ISPRS journal of photogrammetry and remote sensing Amsterdam [u.a.] : Elsevier, 1989 160, Seite 244-259 Online-Ressource (DE-627)320504557 (DE-600)2012663-3 (DE-576)096806567 0924-2716 nnns volume:160 pages:244-259 GBV_USEFLAG_U SYSFLAG_U GBV_ELV SSG-OPC-GGO SSG-OPC-GEO GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 38.73 Geodäsie 74.41 Luftaufnahmen Photogrammetrie AR 160 244-259 |
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Li, Jiayuan @@aut@@ Zhao, Pengcheng @@aut@@ Hu, Qingwu @@aut@@ Ai, Mingyao @@aut@@ |
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550 DE-600 38.73 bkl 74.41 bkl Robust point cloud registration based on topological graph and Cauchy weighted Point Cloud Registration (PCR) Coarse-to-fine registration Feature correspondence Iterative Closest Point (ICP) Robust estimation |
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robust point cloud registration based on topological graph and cauchy weighted |
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Robust point cloud registration based on topological graph and Cauchy weighted |
| abstract |
Point Cloud Registration (PCR) is a fundamental and important issue in photogrammetry and computer vision. Its goal is to find rigid transformations that register multiple 3D point sets. This paper proposes a robust and efficient PCR method based on topological graph and Cauchy weighted l q -norm. Our method does not require initializations and is highly robust to outliers and partial overlaps. It contains two major steps: (1) correspondence-based coarse registration, which is called Weighted l q Coarse Registration (W l q CR). In the W l q CR method, we represent feature point sets as topological graphs and transform the point matching problem to an edge matching problem. We build a mathematical model for edge correspondence maximization. We also present an edge voting strategy to distinguish potential correct matches from mismatches. Then, we define a concept called edge vector, which has a property that it is invariant to translations. Based on this property, six Degrees of Freedoms (DoF) PCR problem can be simplified into two three DoF subproblems, i.e., rotation estimation and translation estimation. (2) fine registration based on Weighted l q Iterative Closest Point (W l q ICP). We propose a new ICP method called W l q ICP, which is much more robust to partial overlaps compared with traditional ICP. In both rotation estimation and W l q ICP, we use a new Cauchy weighted l q -norm ( 0 < q < 1 ) instead of l 2 -norm for object function construction, which has a high degree of robustness. Extensive experiments on both simulated and real data demonstrate the power of the proposed method, i.e., our method is more robust (is able to tolerate up to 99% of outliers) and much faster than compared state-of-the-art methods (W l q CR is almost two orders of magnitude faster than RANdom SAmple Consesus (RANSAC) and its variants under 95% of outliers). The source code will be made publicly available in http://www.escience.cn/people/lijiayuan/index.html. |
| abstractGer |
Point Cloud Registration (PCR) is a fundamental and important issue in photogrammetry and computer vision. Its goal is to find rigid transformations that register multiple 3D point sets. This paper proposes a robust and efficient PCR method based on topological graph and Cauchy weighted l q -norm. Our method does not require initializations and is highly robust to outliers and partial overlaps. It contains two major steps: (1) correspondence-based coarse registration, which is called Weighted l q Coarse Registration (W l q CR). In the W l q CR method, we represent feature point sets as topological graphs and transform the point matching problem to an edge matching problem. We build a mathematical model for edge correspondence maximization. We also present an edge voting strategy to distinguish potential correct matches from mismatches. Then, we define a concept called edge vector, which has a property that it is invariant to translations. Based on this property, six Degrees of Freedoms (DoF) PCR problem can be simplified into two three DoF subproblems, i.e., rotation estimation and translation estimation. (2) fine registration based on Weighted l q Iterative Closest Point (W l q ICP). We propose a new ICP method called W l q ICP, which is much more robust to partial overlaps compared with traditional ICP. In both rotation estimation and W l q ICP, we use a new Cauchy weighted l q -norm ( 0 < q < 1 ) instead of l 2 -norm for object function construction, which has a high degree of robustness. Extensive experiments on both simulated and real data demonstrate the power of the proposed method, i.e., our method is more robust (is able to tolerate up to 99% of outliers) and much faster than compared state-of-the-art methods (W l q CR is almost two orders of magnitude faster than RANdom SAmple Consesus (RANSAC) and its variants under 95% of outliers). The source code will be made publicly available in http://www.escience.cn/people/lijiayuan/index.html. |
| abstract_unstemmed |
Point Cloud Registration (PCR) is a fundamental and important issue in photogrammetry and computer vision. Its goal is to find rigid transformations that register multiple 3D point sets. This paper proposes a robust and efficient PCR method based on topological graph and Cauchy weighted l q -norm. Our method does not require initializations and is highly robust to outliers and partial overlaps. It contains two major steps: (1) correspondence-based coarse registration, which is called Weighted l q Coarse Registration (W l q CR). In the W l q CR method, we represent feature point sets as topological graphs and transform the point matching problem to an edge matching problem. We build a mathematical model for edge correspondence maximization. We also present an edge voting strategy to distinguish potential correct matches from mismatches. Then, we define a concept called edge vector, which has a property that it is invariant to translations. Based on this property, six Degrees of Freedoms (DoF) PCR problem can be simplified into two three DoF subproblems, i.e., rotation estimation and translation estimation. (2) fine registration based on Weighted l q Iterative Closest Point (W l q ICP). We propose a new ICP method called W l q ICP, which is much more robust to partial overlaps compared with traditional ICP. In both rotation estimation and W l q ICP, we use a new Cauchy weighted l q -norm ( 0 < q < 1 ) instead of l 2 -norm for object function construction, which has a high degree of robustness. Extensive experiments on both simulated and real data demonstrate the power of the proposed method, i.e., our method is more robust (is able to tolerate up to 99% of outliers) and much faster than compared state-of-the-art methods (W l q CR is almost two orders of magnitude faster than RANdom SAmple Consesus (RANSAC) and its variants under 95% of outliers). The source code will be made publicly available in http://www.escience.cn/people/lijiayuan/index.html. |
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