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, 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). Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Li, Jiayuan [verfasserIn] Hu, Qingwu [verfasserIn] Ai, Mingyao [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2020

Schlagwörter:	Point cloud registration Coarse registration Feature correspondence RANSAC Robust cost

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
Übergeordnetes Werk:	volume:167 ; pages:363-374

DOI / URN:	10.1016/j.isprsjprs.2020.07.012

Katalog-ID:	ELV004539559

Internformat


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100	1		\|a Li, Jiayuan \|e verfasserin \|0 (orcid)0000-0002-9850-1668 \|4 aut
245	1	0	\|a GESAC: Robust graph enhanced sample consensus for point cloud registration
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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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912			\|a GBV_ILN_32
912			\|a GBV_ILN_40
912			\|a GBV_ILN_60
912			\|a GBV_ILN_62
912			\|a GBV_ILN_63
912			\|a GBV_ILN_65
912			\|a GBV_ILN_69
912			\|a GBV_ILN_70
912			\|a GBV_ILN_73
912			\|a GBV_ILN_74
912			\|a GBV_ILN_90
912			\|a GBV_ILN_95
912			\|a GBV_ILN_100
912			\|a GBV_ILN_105
912			\|a GBV_ILN_110
912			\|a GBV_ILN_150
912			\|a GBV_ILN_151
912			\|a GBV_ILN_224
912			\|a GBV_ILN_370
912			\|a GBV_ILN_602
912			\|a GBV_ILN_702
912			\|a GBV_ILN_2003
912			\|a GBV_ILN_2004
912			\|a GBV_ILN_2005
912			\|a GBV_ILN_2011
912			\|a GBV_ILN_2014
912			\|a GBV_ILN_2015
912			\|a GBV_ILN_2020
912			\|a GBV_ILN_2021
912			\|a GBV_ILN_2025
912			\|a GBV_ILN_2027
912			\|a GBV_ILN_2034
912			\|a GBV_ILN_2038
912			\|a GBV_ILN_2044
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912			\|a GBV_ILN_2049
912			\|a GBV_ILN_2050
912			\|a GBV_ILN_2056
912			\|a GBV_ILN_2059
912			\|a GBV_ILN_2061
912			\|a GBV_ILN_2064
912			\|a GBV_ILN_2065
912			\|a GBV_ILN_2068
912			\|a GBV_ILN_2111
912			\|a GBV_ILN_2112
912			\|a GBV_ILN_2113
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912			\|a GBV_ILN_2143
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912			\|a GBV_ILN_2336
912			\|a GBV_ILN_2507
912			\|a GBV_ILN_2522
912			\|a GBV_ILN_4035
912			\|a GBV_ILN_4037
912			\|a GBV_ILN_4112
912			\|a GBV_ILN_4125
912			\|a GBV_ILN_4126
912			\|a GBV_ILN_4242
912			\|a GBV_ILN_4251
912			\|a GBV_ILN_4305
912			\|a GBV_ILN_4313
912			\|a GBV_ILN_4323
912			\|a GBV_ILN_4324
912			\|a GBV_ILN_4326
912			\|a GBV_ILN_4333
912			\|a GBV_ILN_4334
912			\|a GBV_ILN_4335
912			\|a GBV_ILN_4338
912			\|a GBV_ILN_4393
936	b	k	\|a 38.73 \|j Geodäsie
936	b	k	\|a 74.41 \|j Luftaufnahmen \|j Photogrammetrie
951			\|a AR
952			\|d 167 \|h 363-374

Indexfelder

author_variant	j l jl q h qh m a ma
matchkey_str	article:09242716:2020----::earbsgahnacdapeosnufronc
hierarchy_sort_str	2020
bklnumber	38.73 74.41
publishDate	2020
allfields	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
allfieldsGer	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
allfieldsSound	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
language	English
source	Enthalten in ISPRS journal of photogrammetry and remote sensing 167, Seite 363-374 volume:167 pages:363-374
sourceStr	Enthalten in ISPRS journal of photogrammetry and remote sensing 167, Seite 363-374 volume:167 pages:363-374
format_phy_str_mv	Article
bklname	Geodäsie Luftaufnahmen Photogrammetrie
institution	findex.gbv.de
topic_facet	Point cloud registration Coarse registration Feature correspondence RANSAC Robust cost
dewey-raw	550
isfreeaccess_bool	false
container_title	ISPRS journal of photogrammetry and remote sensing
authorswithroles_txt_mv	Li, Jiayuan @@aut@@ Hu, Qingwu @@aut@@ Ai, Mingyao @@aut@@
publishDateDaySort_date	2020-01-01T00:00:00Z
hierarchy_top_id	320504557
dewey-sort	3550
id	ELV004539559
language_de	englisch
fullrecord	<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">ELV004539559</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230524160926.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">230502s2020 xx \|\|\|\|\|o 00\| \|\|eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1016/j.isprsjprs.2020.07.012</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)ELV004539559</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(ELSEVIER)S0924-2716(20)30199-4</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rda</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">550</subfield><subfield code="q">DE-600</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">38.73</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">74.41</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Li, Jiayuan</subfield><subfield code="e">verfasserin</subfield><subfield code="0">(orcid)0000-0002-9850-1668</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">GESAC: Robust graph enhanced sample consensus for point cloud registration</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2020</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">zzz</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="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. 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author	Li, Jiayuan
spellingShingle	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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topic	ddc 550 bkl 38.73 bkl 74.41 misc Point cloud registration misc Coarse registration misc Feature correspondence misc RANSAC misc Robust cost
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title	GESAC: Robust graph enhanced sample consensus for point cloud registration
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title_full	GESAC: Robust graph enhanced sample consensus for point cloud registration
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title_sort	gesac: robust graph enhanced sample consensus for point cloud registration
title_auth	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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title_short	GESAC: Robust graph enhanced sample consensus for point cloud registration
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author2	Hu, Qingwu Ai, Mingyao
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doi_str	10.1016/j.isprsjprs.2020.07.012
up_date	2024-07-06T23:20:09.614Z
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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).</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Point cloud registration</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Coarse registration</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Feature correspondence</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">RANSAC</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Robust cost</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Hu, Qingwu</subfield><subfield code="e">verfasserin</subfield><subfield code="0">(orcid)0000-0003-0866-6678</subfield><subfield code="4">aut</subfield></datafield><datafield 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GESAC: Robust graph enhanced sample consensus for point cloud registration

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