GESAC: Robust graph enhanced sample consensus for point cloud registration
Pairwise point cloud registration (PCR) is a crucial problem in photogrammetry, which aims to find a rigid transformation that registers a pair of point clouds. Typically, PCR is performed in a coarse-to-fine manner. Coarse registration provides good initial transformations for fine registration, wh...
Ausführliche Beschreibung
Autor*in: |
Li, Jiayuan [verfasserIn] Hu, Qingwu [verfasserIn] Ai, Mingyao [verfasserIn] |
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Format: |
E-Artikel |
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Sprache: |
Englisch |
Erschienen: |
2020 |
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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, 167, Seite 363-374 |
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Übergeordnetes Werk: |
volume:167 ; pages:363-374 |
DOI / URN: |
10.1016/j.isprsjprs.2020.07.012 |
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Katalog-ID: |
ELV004539559 |
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520 | |a Pairwise point cloud registration (PCR) is a crucial problem in photogrammetry, which aims to find a rigid transformation that registers a pair of point clouds. Typically, PCR is performed in a coarse-to-fine manner. Coarse registration provides good initial transformations for fine registration, which determines whether the PCR can succeed. RANSAC-based correspondence registration is the most popular technique for coarse registration. However, the outlier rate of feature correspondences extracted from point clouds is generally very high. Current RANSAC-variants require a huge number of trials to achieve satisfactory results at high outlier rates. This paper proposes a fast and robust RANSAC-variant for PCR, called graph enhanced sample consensus (GESAC). GESAC improves classic RANSAC-family in both sampling and model fitting steps. In the sampling, GESAC generates a much larger subset instead of a minimal subset for model fitting. RANSAC-variants treat a subset as a good one only if the correspondences in the subset are all inliers. In contrast to RANSAC-variants, GESAC allows outliers in the subset and only requires three inliers in the case of point cloud coregistration. Hence, the probability to obtain good subsets of GESAC is much larger than the ones of classic RANSAC-variants. GESAC uses an equal-length constraint to filter “degraded” subsets and expresses a subset as a graph. Then, a max-pooling graph matching strategy is applied to remove potential outliers in the subset. In the model fitting, GESAC introduces a shape-annealing robust estimate instead of classic least-squares for rigid transformation estimation. Hence, even if the subset cleaned by graph matching still contains outliers, GESAC is able to recover a correct solution for PCR. Both simulated and real experiments demonstrate the power of GESAC, i.e., it can tolerate up to more than 99% outliers and is 4000 + times faster than RANSAC at outlier rates above 99% (Note that the running time of feature extraction is not included). | ||
650 | 4 | |a Point cloud registration | |
650 | 4 | |a Coarse registration | |
650 | 4 | |a Feature correspondence | |
650 | 4 | |a RANSAC | |
650 | 4 | |a Robust cost | |
700 | 1 | |a Hu, Qingwu |e verfasserin |0 (orcid)0000-0003-0866-6678 |4 aut | |
700 | 1 | |a Ai, Mingyao |e verfasserin |4 aut | |
773 | 0 | 8 | |i Enthalten in |a International Society for Photogrammetry and Remote Sensing ; ID: gnd/132008-7 |t ISPRS journal of photogrammetry and remote sensing |d Amsterdam [u.a.] : Elsevier, 1989 |g 167, Seite 363-374 |h Online-Ressource |w (DE-627)320504557 |w (DE-600)2012663-3 |w (DE-576)096806567 |x 0924-2716 |7 nnns |
773 | 1 | 8 | |g volume:167 |g pages:363-374 |
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10.1016/j.isprsjprs.2020.07.012 doi (DE-627)ELV004539559 (ELSEVIER)S0924-2716(20)30199-4 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 GESAC: Robust graph enhanced sample consensus for point cloud registration 2020 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Pairwise point cloud registration (PCR) is a crucial problem in photogrammetry, which aims to find a rigid transformation that registers a pair of point clouds. Typically, PCR is performed in a coarse-to-fine manner. Coarse registration provides good initial transformations for fine registration, which determines whether the PCR can succeed. RANSAC-based correspondence registration is the most popular technique for coarse registration. However, the outlier rate of feature correspondences extracted from point clouds is generally very high. Current RANSAC-variants require a huge number of trials to achieve satisfactory results at high outlier rates. This paper proposes a fast and robust RANSAC-variant for PCR, called graph enhanced sample consensus (GESAC). GESAC improves classic RANSAC-family in both sampling and model fitting steps. In the sampling, GESAC generates a much larger subset instead of a minimal subset for model fitting. RANSAC-variants treat a subset as a good one only if the correspondences in the subset are all inliers. In contrast to RANSAC-variants, GESAC allows outliers in the subset and only requires three inliers in the case of point cloud coregistration. Hence, the probability to obtain good subsets of GESAC is much larger than the ones of classic RANSAC-variants. GESAC uses an equal-length constraint to filter “degraded” subsets and expresses a subset as a graph. Then, a max-pooling graph matching strategy is applied to remove potential outliers in the subset. In the model fitting, GESAC introduces a shape-annealing robust estimate instead of classic least-squares for rigid transformation estimation. Hence, even if the subset cleaned by graph matching still contains outliers, GESAC is able to recover a correct solution for PCR. Both simulated and real experiments demonstrate the power of GESAC, i.e., it can tolerate up to more than 99% outliers and is 4000 + times faster than RANSAC at outlier rates above 99% (Note that the running time of feature extraction is not included). Point cloud registration Coarse registration Feature correspondence RANSAC Robust cost 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 167, Seite 363-374 Online-Ressource (DE-627)320504557 (DE-600)2012663-3 (DE-576)096806567 0924-2716 nnns volume:167 pages:363-374 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 167 363-374 |
spelling |
10.1016/j.isprsjprs.2020.07.012 doi (DE-627)ELV004539559 (ELSEVIER)S0924-2716(20)30199-4 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 GESAC: Robust graph enhanced sample consensus for point cloud registration 2020 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Pairwise point cloud registration (PCR) is a crucial problem in photogrammetry, which aims to find a rigid transformation that registers a pair of point clouds. Typically, PCR is performed in a coarse-to-fine manner. Coarse registration provides good initial transformations for fine registration, which determines whether the PCR can succeed. RANSAC-based correspondence registration is the most popular technique for coarse registration. However, the outlier rate of feature correspondences extracted from point clouds is generally very high. Current RANSAC-variants require a huge number of trials to achieve satisfactory results at high outlier rates. This paper proposes a fast and robust RANSAC-variant for PCR, called graph enhanced sample consensus (GESAC). GESAC improves classic RANSAC-family in both sampling and model fitting steps. In the sampling, GESAC generates a much larger subset instead of a minimal subset for model fitting. RANSAC-variants treat a subset as a good one only if the correspondences in the subset are all inliers. In contrast to RANSAC-variants, GESAC allows outliers in the subset and only requires three inliers in the case of point cloud coregistration. Hence, the probability to obtain good subsets of GESAC is much larger than the ones of classic RANSAC-variants. GESAC uses an equal-length constraint to filter “degraded” subsets and expresses a subset as a graph. Then, a max-pooling graph matching strategy is applied to remove potential outliers in the subset. In the model fitting, GESAC introduces a shape-annealing robust estimate instead of classic least-squares for rigid transformation estimation. Hence, even if the subset cleaned by graph matching still contains outliers, GESAC is able to recover a correct solution for PCR. Both simulated and real experiments demonstrate the power of GESAC, i.e., it can tolerate up to more than 99% outliers and is 4000 + times faster than RANSAC at outlier rates above 99% (Note that the running time of feature extraction is not included). Point cloud registration Coarse registration Feature correspondence RANSAC Robust cost 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 167, Seite 363-374 Online-Ressource (DE-627)320504557 (DE-600)2012663-3 (DE-576)096806567 0924-2716 nnns volume:167 pages:363-374 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 167 363-374 |
allfields_unstemmed |
10.1016/j.isprsjprs.2020.07.012 doi (DE-627)ELV004539559 (ELSEVIER)S0924-2716(20)30199-4 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 GESAC: Robust graph enhanced sample consensus for point cloud registration 2020 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Pairwise point cloud registration (PCR) is a crucial problem in photogrammetry, which aims to find a rigid transformation that registers a pair of point clouds. Typically, PCR is performed in a coarse-to-fine manner. Coarse registration provides good initial transformations for fine registration, which determines whether the PCR can succeed. RANSAC-based correspondence registration is the most popular technique for coarse registration. However, the outlier rate of feature correspondences extracted from point clouds is generally very high. Current RANSAC-variants require a huge number of trials to achieve satisfactory results at high outlier rates. This paper proposes a fast and robust RANSAC-variant for PCR, called graph enhanced sample consensus (GESAC). GESAC improves classic RANSAC-family in both sampling and model fitting steps. In the sampling, GESAC generates a much larger subset instead of a minimal subset for model fitting. RANSAC-variants treat a subset as a good one only if the correspondences in the subset are all inliers. In contrast to RANSAC-variants, GESAC allows outliers in the subset and only requires three inliers in the case of point cloud coregistration. Hence, the probability to obtain good subsets of GESAC is much larger than the ones of classic RANSAC-variants. GESAC uses an equal-length constraint to filter “degraded” subsets and expresses a subset as a graph. Then, a max-pooling graph matching strategy is applied to remove potential outliers in the subset. In the model fitting, GESAC introduces a shape-annealing robust estimate instead of classic least-squares for rigid transformation estimation. Hence, even if the subset cleaned by graph matching still contains outliers, GESAC is able to recover a correct solution for PCR. Both simulated and real experiments demonstrate the power of GESAC, i.e., it can tolerate up to more than 99% outliers and is 4000 + times faster than RANSAC at outlier rates above 99% (Note that the running time of feature extraction is not included). Point cloud registration Coarse registration Feature correspondence RANSAC Robust cost 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 167, Seite 363-374 Online-Ressource (DE-627)320504557 (DE-600)2012663-3 (DE-576)096806567 0924-2716 nnns volume:167 pages:363-374 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 167 363-374 |
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10.1016/j.isprsjprs.2020.07.012 doi (DE-627)ELV004539559 (ELSEVIER)S0924-2716(20)30199-4 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 GESAC: Robust graph enhanced sample consensus for point cloud registration 2020 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Pairwise point cloud registration (PCR) is a crucial problem in photogrammetry, which aims to find a rigid transformation that registers a pair of point clouds. Typically, PCR is performed in a coarse-to-fine manner. Coarse registration provides good initial transformations for fine registration, which determines whether the PCR can succeed. RANSAC-based correspondence registration is the most popular technique for coarse registration. However, the outlier rate of feature correspondences extracted from point clouds is generally very high. Current RANSAC-variants require a huge number of trials to achieve satisfactory results at high outlier rates. This paper proposes a fast and robust RANSAC-variant for PCR, called graph enhanced sample consensus (GESAC). GESAC improves classic RANSAC-family in both sampling and model fitting steps. In the sampling, GESAC generates a much larger subset instead of a minimal subset for model fitting. RANSAC-variants treat a subset as a good one only if the correspondences in the subset are all inliers. In contrast to RANSAC-variants, GESAC allows outliers in the subset and only requires three inliers in the case of point cloud coregistration. Hence, the probability to obtain good subsets of GESAC is much larger than the ones of classic RANSAC-variants. GESAC uses an equal-length constraint to filter “degraded” subsets and expresses a subset as a graph. Then, a max-pooling graph matching strategy is applied to remove potential outliers in the subset. In the model fitting, GESAC introduces a shape-annealing robust estimate instead of classic least-squares for rigid transformation estimation. Hence, even if the subset cleaned by graph matching still contains outliers, GESAC is able to recover a correct solution for PCR. Both simulated and real experiments demonstrate the power of GESAC, i.e., it can tolerate up to more than 99% outliers and is 4000 + times faster than RANSAC at outlier rates above 99% (Note that the running time of feature extraction is not included). Point cloud registration Coarse registration Feature correspondence RANSAC Robust cost 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 167, Seite 363-374 Online-Ressource (DE-627)320504557 (DE-600)2012663-3 (DE-576)096806567 0924-2716 nnns volume:167 pages:363-374 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 167 363-374 |
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10.1016/j.isprsjprs.2020.07.012 doi (DE-627)ELV004539559 (ELSEVIER)S0924-2716(20)30199-4 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 GESAC: Robust graph enhanced sample consensus for point cloud registration 2020 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Pairwise point cloud registration (PCR) is a crucial problem in photogrammetry, which aims to find a rigid transformation that registers a pair of point clouds. Typically, PCR is performed in a coarse-to-fine manner. Coarse registration provides good initial transformations for fine registration, which determines whether the PCR can succeed. RANSAC-based correspondence registration is the most popular technique for coarse registration. However, the outlier rate of feature correspondences extracted from point clouds is generally very high. Current RANSAC-variants require a huge number of trials to achieve satisfactory results at high outlier rates. This paper proposes a fast and robust RANSAC-variant for PCR, called graph enhanced sample consensus (GESAC). GESAC improves classic RANSAC-family in both sampling and model fitting steps. In the sampling, GESAC generates a much larger subset instead of a minimal subset for model fitting. RANSAC-variants treat a subset as a good one only if the correspondences in the subset are all inliers. In contrast to RANSAC-variants, GESAC allows outliers in the subset and only requires three inliers in the case of point cloud coregistration. Hence, the probability to obtain good subsets of GESAC is much larger than the ones of classic RANSAC-variants. GESAC uses an equal-length constraint to filter “degraded” subsets and expresses a subset as a graph. Then, a max-pooling graph matching strategy is applied to remove potential outliers in the subset. In the model fitting, GESAC introduces a shape-annealing robust estimate instead of classic least-squares for rigid transformation estimation. Hence, even if the subset cleaned by graph matching still contains outliers, GESAC is able to recover a correct solution for PCR. Both simulated and real experiments demonstrate the power of GESAC, i.e., it can tolerate up to more than 99% outliers and is 4000 + times faster than RANSAC at outlier rates above 99% (Note that the running time of feature extraction is not included). Point cloud registration Coarse registration Feature correspondence RANSAC Robust cost 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 167, Seite 363-374 Online-Ressource (DE-627)320504557 (DE-600)2012663-3 (DE-576)096806567 0924-2716 nnns volume:167 pages:363-374 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 167 363-374 |
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Li, Jiayuan |
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Li, Jiayuan ddc 550 bkl 38.73 bkl 74.41 misc Point cloud registration misc Coarse registration misc Feature correspondence misc RANSAC misc Robust cost GESAC: Robust graph enhanced sample consensus for point cloud registration |
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550 DE-600 38.73 bkl 74.41 bkl GESAC: Robust graph enhanced sample consensus for point cloud registration Point cloud registration Coarse registration Feature correspondence RANSAC Robust cost |
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gesac: robust graph enhanced sample consensus for point cloud registration |
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GESAC: Robust graph enhanced sample consensus for point cloud registration |
abstract |
Pairwise point cloud registration (PCR) is a crucial problem in photogrammetry, which aims to find a rigid transformation that registers a pair of point clouds. Typically, PCR is performed in a coarse-to-fine manner. Coarse registration provides good initial transformations for fine registration, which determines whether the PCR can succeed. RANSAC-based correspondence registration is the most popular technique for coarse registration. However, the outlier rate of feature correspondences extracted from point clouds is generally very high. Current RANSAC-variants require a huge number of trials to achieve satisfactory results at high outlier rates. This paper proposes a fast and robust RANSAC-variant for PCR, called graph enhanced sample consensus (GESAC). GESAC improves classic RANSAC-family in both sampling and model fitting steps. In the sampling, GESAC generates a much larger subset instead of a minimal subset for model fitting. RANSAC-variants treat a subset as a good one only if the correspondences in the subset are all inliers. In contrast to RANSAC-variants, GESAC allows outliers in the subset and only requires three inliers in the case of point cloud coregistration. Hence, the probability to obtain good subsets of GESAC is much larger than the ones of classic RANSAC-variants. GESAC uses an equal-length constraint to filter “degraded” subsets and expresses a subset as a graph. Then, a max-pooling graph matching strategy is applied to remove potential outliers in the subset. In the model fitting, GESAC introduces a shape-annealing robust estimate instead of classic least-squares for rigid transformation estimation. Hence, even if the subset cleaned by graph matching still contains outliers, GESAC is able to recover a correct solution for PCR. Both simulated and real experiments demonstrate the power of GESAC, i.e., it can tolerate up to more than 99% outliers and is 4000 + times faster than RANSAC at outlier rates above 99% (Note that the running time of feature extraction is not included). |
abstractGer |
Pairwise point cloud registration (PCR) is a crucial problem in photogrammetry, which aims to find a rigid transformation that registers a pair of point clouds. Typically, PCR is performed in a coarse-to-fine manner. Coarse registration provides good initial transformations for fine registration, which determines whether the PCR can succeed. RANSAC-based correspondence registration is the most popular technique for coarse registration. However, the outlier rate of feature correspondences extracted from point clouds is generally very high. Current RANSAC-variants require a huge number of trials to achieve satisfactory results at high outlier rates. This paper proposes a fast and robust RANSAC-variant for PCR, called graph enhanced sample consensus (GESAC). GESAC improves classic RANSAC-family in both sampling and model fitting steps. In the sampling, GESAC generates a much larger subset instead of a minimal subset for model fitting. RANSAC-variants treat a subset as a good one only if the correspondences in the subset are all inliers. In contrast to RANSAC-variants, GESAC allows outliers in the subset and only requires three inliers in the case of point cloud coregistration. Hence, the probability to obtain good subsets of GESAC is much larger than the ones of classic RANSAC-variants. GESAC uses an equal-length constraint to filter “degraded” subsets and expresses a subset as a graph. Then, a max-pooling graph matching strategy is applied to remove potential outliers in the subset. In the model fitting, GESAC introduces a shape-annealing robust estimate instead of classic least-squares for rigid transformation estimation. Hence, even if the subset cleaned by graph matching still contains outliers, GESAC is able to recover a correct solution for PCR. Both simulated and real experiments demonstrate the power of GESAC, i.e., it can tolerate up to more than 99% outliers and is 4000 + times faster than RANSAC at outlier rates above 99% (Note that the running time of feature extraction is not included). |
abstract_unstemmed |
Pairwise point cloud registration (PCR) is a crucial problem in photogrammetry, which aims to find a rigid transformation that registers a pair of point clouds. Typically, PCR is performed in a coarse-to-fine manner. Coarse registration provides good initial transformations for fine registration, which determines whether the PCR can succeed. RANSAC-based correspondence registration is the most popular technique for coarse registration. However, the outlier rate of feature correspondences extracted from point clouds is generally very high. Current RANSAC-variants require a huge number of trials to achieve satisfactory results at high outlier rates. This paper proposes a fast and robust RANSAC-variant for PCR, called graph enhanced sample consensus (GESAC). GESAC improves classic RANSAC-family in both sampling and model fitting steps. In the sampling, GESAC generates a much larger subset instead of a minimal subset for model fitting. RANSAC-variants treat a subset as a good one only if the correspondences in the subset are all inliers. In contrast to RANSAC-variants, GESAC allows outliers in the subset and only requires three inliers in the case of point cloud coregistration. Hence, the probability to obtain good subsets of GESAC is much larger than the ones of classic RANSAC-variants. GESAC uses an equal-length constraint to filter “degraded” subsets and expresses a subset as a graph. Then, a max-pooling graph matching strategy is applied to remove potential outliers in the subset. In the model fitting, GESAC introduces a shape-annealing robust estimate instead of classic least-squares for rigid transformation estimation. Hence, even if the subset cleaned by graph matching still contains outliers, GESAC is able to recover a correct solution for PCR. Both simulated and real experiments demonstrate the power of GESAC, i.e., it can tolerate up to more than 99% outliers and is 4000 + times faster than RANSAC at outlier rates above 99% (Note that the running time of feature extraction is not included). |
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GESAC: Robust graph enhanced sample consensus for point cloud registration |
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