JoKDNet: A joint keypoint detection and description network for large-scale outdoor TLS point clouds registration

Registration of large-scale outdoor Terrestrial Laser Scanning (TLS) point clouds remains many challenges in the scenes with symmetric and repetitive elements (e.g., park, forest, and tunnel), the weak geometric features (e.g., underground excavation), and dramatically changes in different phases (e...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Wang, Yuan [verfasserIn] Yang, Bisheng [verfasserIn] Chen, Yiping [verfasserIn] Liang, Fuxun [verfasserIn] Dong, Zhen [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2021

Schlagwörter:	Point clouds Registration 3D deep learning Keypoint detection Feature descriptor

Übergeordnetes Werk:	Enthalten in: International journal of applied earth observation and geoinformation - Amsterdam [u.a.] : Elsevier Science, 1999, 104
Übergeordnetes Werk:	volume:104

DOI / URN:	10.1016/j.jag.2021.102534

Katalog-ID:	ELV006870414

Internformat


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245	1	0	\|a JoKDNet: A joint keypoint detection and description network for large-scale outdoor TLS point clouds registration
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520			\|a Registration of large-scale outdoor Terrestrial Laser Scanning (TLS) point clouds remains many challenges in the scenes with symmetric and repetitive elements (e.g., park, forest, and tunnel), the weak geometric features (e.g., underground excavation), and dramatically changes in different phases (e.g., mountain). To address these issues, a novel neural network JoKDNet is proposed to jointly learn the keypoint detection and feature description to improve the feasibility and accuracy of point clouds registration. Firstly, a novel keypoint detection module is introduced to automatically learn the score of each sampled point and regard the most significant Top-k sampled points as the detected keypoints. Secondly, an enhanced feature description module is proposed to learn the feature representation of each keypoint by fusing the hierarchical local features and context features. Thirdly, a loss function is designed to make the detected keypoints more distinguishable for matching, which simultaneously maximizes the feature distance between non-corresponding keypoints and minimizes the feature distance of corresponding keypoints. Finally, the distance matrix module and RANdom SAmple Consensus (RANSAC) are utilized to determine the correspondences of source and target point clouds for the transformation calculation. Comprehensive experiments show that the JoKDNet performs effectively on five challenging scenes (e.g., park, forest, tunnel, underground excavation, and mountain) from two datasets (WHU-TLS and ETH-TLS) in terms of registration errors, and robustness to varying scenes, with the maximum rotation error less than 0.06° and maximum translation error less than 0.84 m without ICP.
650		4	\|a Point clouds
650		4	\|a Registration
650		4	\|a 3D deep learning
650		4	\|a Keypoint detection
650		4	\|a Feature descriptor
700	1		\|a Yang, Bisheng \|e verfasserin \|4 aut
700	1		\|a Chen, Yiping \|e verfasserin \|4 aut
700	1		\|a Liang, Fuxun \|e verfasserin \|4 aut
700	1		\|a Dong, Zhen \|e verfasserin \|4 aut
773	0	8	\|i Enthalten in \|t International journal of applied earth observation and geoinformation \|d Amsterdam [u.a.] : Elsevier Science, 1999 \|g 104 \|h Online-Ressource \|w (DE-627)359784119 \|w (DE-600)2097960-5 \|w (DE-576)25927254X \|x 1872-826X \|7 nnns
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936	b	k	\|a 74.41 \|j Luftaufnahmen \|j Photogrammetrie
951			\|a AR
952			\|d 104

Indexfelder

author_variant	y w yw b y by y c yc f l fl z d zd
matchkey_str	article:1872826X:2021----::odeaonkyondtcinndsrpinewrfragsaeudot
hierarchy_sort_str	2021
bklnumber	38.03 74.48 74.41
publishDate	2021
allfields	10.1016/j.jag.2021.102534 doi (DE-627)ELV006870414 (ELSEVIER)S0303-2434(21)00241-5 DE-627 ger DE-627 rda eng 550 DE-600 KARTEN DE-1a fid 38.03 bkl 74.48 bkl 74.41 bkl Wang, Yuan verfasserin aut JoKDNet: A joint keypoint detection and description network for large-scale outdoor TLS point clouds registration 2021 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Registration of large-scale outdoor Terrestrial Laser Scanning (TLS) point clouds remains many challenges in the scenes with symmetric and repetitive elements (e.g., park, forest, and tunnel), the weak geometric features (e.g., underground excavation), and dramatically changes in different phases (e.g., mountain). To address these issues, a novel neural network JoKDNet is proposed to jointly learn the keypoint detection and feature description to improve the feasibility and accuracy of point clouds registration. Firstly, a novel keypoint detection module is introduced to automatically learn the score of each sampled point and regard the most significant Top-k sampled points as the detected keypoints. Secondly, an enhanced feature description module is proposed to learn the feature representation of each keypoint by fusing the hierarchical local features and context features. Thirdly, a loss function is designed to make the detected keypoints more distinguishable for matching, which simultaneously maximizes the feature distance between non-corresponding keypoints and minimizes the feature distance of corresponding keypoints. Finally, the distance matrix module and RANdom SAmple Consensus (RANSAC) are utilized to determine the correspondences of source and target point clouds for the transformation calculation. Comprehensive experiments show that the JoKDNet performs effectively on five challenging scenes (e.g., park, forest, tunnel, underground excavation, and mountain) from two datasets (WHU-TLS and ETH-TLS) in terms of registration errors, and robustness to varying scenes, with the maximum rotation error less than 0.06° and maximum translation error less than 0.84 m without ICP. Point clouds Registration 3D deep learning Keypoint detection Feature descriptor Yang, Bisheng verfasserin aut Chen, Yiping verfasserin aut Liang, Fuxun verfasserin aut Dong, Zhen verfasserin aut Enthalten in International journal of applied earth observation and geoinformation Amsterdam [u.a.] : Elsevier Science, 1999 104 Online-Ressource (DE-627)359784119 (DE-600)2097960-5 (DE-576)25927254X 1872-826X nnns volume:104 GBV_USEFLAG_U SYSFLAG_U GBV_ELV FID-KARTEN SSG-OPC-GGO SSG-OPC-AST SSG-OPC-GEO GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 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_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2008 GBV_ILN_2014 GBV_ILN_2025 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 38.03 Methoden und Techniken der Geowissenschaften 74.48 Geoinformationssysteme 74.41 Luftaufnahmen Photogrammetrie AR 104
spelling	10.1016/j.jag.2021.102534 doi (DE-627)ELV006870414 (ELSEVIER)S0303-2434(21)00241-5 DE-627 ger DE-627 rda eng 550 DE-600 KARTEN DE-1a fid 38.03 bkl 74.48 bkl 74.41 bkl Wang, Yuan verfasserin aut JoKDNet: A joint keypoint detection and description network for large-scale outdoor TLS point clouds registration 2021 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Registration of large-scale outdoor Terrestrial Laser Scanning (TLS) point clouds remains many challenges in the scenes with symmetric and repetitive elements (e.g., park, forest, and tunnel), the weak geometric features (e.g., underground excavation), and dramatically changes in different phases (e.g., mountain). To address these issues, a novel neural network JoKDNet is proposed to jointly learn the keypoint detection and feature description to improve the feasibility and accuracy of point clouds registration. Firstly, a novel keypoint detection module is introduced to automatically learn the score of each sampled point and regard the most significant Top-k sampled points as the detected keypoints. Secondly, an enhanced feature description module is proposed to learn the feature representation of each keypoint by fusing the hierarchical local features and context features. Thirdly, a loss function is designed to make the detected keypoints more distinguishable for matching, which simultaneously maximizes the feature distance between non-corresponding keypoints and minimizes the feature distance of corresponding keypoints. Finally, the distance matrix module and RANdom SAmple Consensus (RANSAC) are utilized to determine the correspondences of source and target point clouds for the transformation calculation. Comprehensive experiments show that the JoKDNet performs effectively on five challenging scenes (e.g., park, forest, tunnel, underground excavation, and mountain) from two datasets (WHU-TLS and ETH-TLS) in terms of registration errors, and robustness to varying scenes, with the maximum rotation error less than 0.06° and maximum translation error less than 0.84 m without ICP. Point clouds Registration 3D deep learning Keypoint detection Feature descriptor Yang, Bisheng verfasserin aut Chen, Yiping verfasserin aut Liang, Fuxun verfasserin aut Dong, Zhen verfasserin aut Enthalten in International journal of applied earth observation and geoinformation Amsterdam [u.a.] : Elsevier Science, 1999 104 Online-Ressource (DE-627)359784119 (DE-600)2097960-5 (DE-576)25927254X 1872-826X nnns volume:104 GBV_USEFLAG_U SYSFLAG_U GBV_ELV FID-KARTEN SSG-OPC-GGO SSG-OPC-AST SSG-OPC-GEO GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 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_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2008 GBV_ILN_2014 GBV_ILN_2025 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 38.03 Methoden und Techniken der Geowissenschaften 74.48 Geoinformationssysteme 74.41 Luftaufnahmen Photogrammetrie AR 104
allfields_unstemmed	10.1016/j.jag.2021.102534 doi (DE-627)ELV006870414 (ELSEVIER)S0303-2434(21)00241-5 DE-627 ger DE-627 rda eng 550 DE-600 KARTEN DE-1a fid 38.03 bkl 74.48 bkl 74.41 bkl Wang, Yuan verfasserin aut JoKDNet: A joint keypoint detection and description network for large-scale outdoor TLS point clouds registration 2021 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Registration of large-scale outdoor Terrestrial Laser Scanning (TLS) point clouds remains many challenges in the scenes with symmetric and repetitive elements (e.g., park, forest, and tunnel), the weak geometric features (e.g., underground excavation), and dramatically changes in different phases (e.g., mountain). To address these issues, a novel neural network JoKDNet is proposed to jointly learn the keypoint detection and feature description to improve the feasibility and accuracy of point clouds registration. Firstly, a novel keypoint detection module is introduced to automatically learn the score of each sampled point and regard the most significant Top-k sampled points as the detected keypoints. Secondly, an enhanced feature description module is proposed to learn the feature representation of each keypoint by fusing the hierarchical local features and context features. Thirdly, a loss function is designed to make the detected keypoints more distinguishable for matching, which simultaneously maximizes the feature distance between non-corresponding keypoints and minimizes the feature distance of corresponding keypoints. Finally, the distance matrix module and RANdom SAmple Consensus (RANSAC) are utilized to determine the correspondences of source and target point clouds for the transformation calculation. Comprehensive experiments show that the JoKDNet performs effectively on five challenging scenes (e.g., park, forest, tunnel, underground excavation, and mountain) from two datasets (WHU-TLS and ETH-TLS) in terms of registration errors, and robustness to varying scenes, with the maximum rotation error less than 0.06° and maximum translation error less than 0.84 m without ICP. Point clouds Registration 3D deep learning Keypoint detection Feature descriptor Yang, Bisheng verfasserin aut Chen, Yiping verfasserin aut Liang, Fuxun verfasserin aut Dong, Zhen verfasserin aut Enthalten in International journal of applied earth observation and geoinformation Amsterdam [u.a.] : Elsevier Science, 1999 104 Online-Ressource (DE-627)359784119 (DE-600)2097960-5 (DE-576)25927254X 1872-826X nnns volume:104 GBV_USEFLAG_U SYSFLAG_U GBV_ELV FID-KARTEN SSG-OPC-GGO SSG-OPC-AST SSG-OPC-GEO GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 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_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2008 GBV_ILN_2014 GBV_ILN_2025 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 38.03 Methoden und Techniken der Geowissenschaften 74.48 Geoinformationssysteme 74.41 Luftaufnahmen Photogrammetrie AR 104
allfieldsGer	10.1016/j.jag.2021.102534 doi (DE-627)ELV006870414 (ELSEVIER)S0303-2434(21)00241-5 DE-627 ger DE-627 rda eng 550 DE-600 KARTEN DE-1a fid 38.03 bkl 74.48 bkl 74.41 bkl Wang, Yuan verfasserin aut JoKDNet: A joint keypoint detection and description network for large-scale outdoor TLS point clouds registration 2021 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Registration of large-scale outdoor Terrestrial Laser Scanning (TLS) point clouds remains many challenges in the scenes with symmetric and repetitive elements (e.g., park, forest, and tunnel), the weak geometric features (e.g., underground excavation), and dramatically changes in different phases (e.g., mountain). To address these issues, a novel neural network JoKDNet is proposed to jointly learn the keypoint detection and feature description to improve the feasibility and accuracy of point clouds registration. Firstly, a novel keypoint detection module is introduced to automatically learn the score of each sampled point and regard the most significant Top-k sampled points as the detected keypoints. Secondly, an enhanced feature description module is proposed to learn the feature representation of each keypoint by fusing the hierarchical local features and context features. Thirdly, a loss function is designed to make the detected keypoints more distinguishable for matching, which simultaneously maximizes the feature distance between non-corresponding keypoints and minimizes the feature distance of corresponding keypoints. Finally, the distance matrix module and RANdom SAmple Consensus (RANSAC) are utilized to determine the correspondences of source and target point clouds for the transformation calculation. Comprehensive experiments show that the JoKDNet performs effectively on five challenging scenes (e.g., park, forest, tunnel, underground excavation, and mountain) from two datasets (WHU-TLS and ETH-TLS) in terms of registration errors, and robustness to varying scenes, with the maximum rotation error less than 0.06° and maximum translation error less than 0.84 m without ICP. Point clouds Registration 3D deep learning Keypoint detection Feature descriptor Yang, Bisheng verfasserin aut Chen, Yiping verfasserin aut Liang, Fuxun verfasserin aut Dong, Zhen verfasserin aut Enthalten in International journal of applied earth observation and geoinformation Amsterdam [u.a.] : Elsevier Science, 1999 104 Online-Ressource (DE-627)359784119 (DE-600)2097960-5 (DE-576)25927254X 1872-826X nnns volume:104 GBV_USEFLAG_U SYSFLAG_U GBV_ELV FID-KARTEN SSG-OPC-GGO SSG-OPC-AST SSG-OPC-GEO GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 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_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2008 GBV_ILN_2014 GBV_ILN_2025 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 38.03 Methoden und Techniken der Geowissenschaften 74.48 Geoinformationssysteme 74.41 Luftaufnahmen Photogrammetrie AR 104
allfieldsSound	10.1016/j.jag.2021.102534 doi (DE-627)ELV006870414 (ELSEVIER)S0303-2434(21)00241-5 DE-627 ger DE-627 rda eng 550 DE-600 KARTEN DE-1a fid 38.03 bkl 74.48 bkl 74.41 bkl Wang, Yuan verfasserin aut JoKDNet: A joint keypoint detection and description network for large-scale outdoor TLS point clouds registration 2021 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Registration of large-scale outdoor Terrestrial Laser Scanning (TLS) point clouds remains many challenges in the scenes with symmetric and repetitive elements (e.g., park, forest, and tunnel), the weak geometric features (e.g., underground excavation), and dramatically changes in different phases (e.g., mountain). To address these issues, a novel neural network JoKDNet is proposed to jointly learn the keypoint detection and feature description to improve the feasibility and accuracy of point clouds registration. Firstly, a novel keypoint detection module is introduced to automatically learn the score of each sampled point and regard the most significant Top-k sampled points as the detected keypoints. Secondly, an enhanced feature description module is proposed to learn the feature representation of each keypoint by fusing the hierarchical local features and context features. Thirdly, a loss function is designed to make the detected keypoints more distinguishable for matching, which simultaneously maximizes the feature distance between non-corresponding keypoints and minimizes the feature distance of corresponding keypoints. Finally, the distance matrix module and RANdom SAmple Consensus (RANSAC) are utilized to determine the correspondences of source and target point clouds for the transformation calculation. Comprehensive experiments show that the JoKDNet performs effectively on five challenging scenes (e.g., park, forest, tunnel, underground excavation, and mountain) from two datasets (WHU-TLS and ETH-TLS) in terms of registration errors, and robustness to varying scenes, with the maximum rotation error less than 0.06° and maximum translation error less than 0.84 m without ICP. Point clouds Registration 3D deep learning Keypoint detection Feature descriptor Yang, Bisheng verfasserin aut Chen, Yiping verfasserin aut Liang, Fuxun verfasserin aut Dong, Zhen verfasserin aut Enthalten in International journal of applied earth observation and geoinformation Amsterdam [u.a.] : Elsevier Science, 1999 104 Online-Ressource (DE-627)359784119 (DE-600)2097960-5 (DE-576)25927254X 1872-826X nnns volume:104 GBV_USEFLAG_U SYSFLAG_U GBV_ELV FID-KARTEN SSG-OPC-GGO SSG-OPC-AST SSG-OPC-GEO GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 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_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2008 GBV_ILN_2014 GBV_ILN_2025 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 38.03 Methoden und Techniken der Geowissenschaften 74.48 Geoinformationssysteme 74.41 Luftaufnahmen Photogrammetrie AR 104
language	English
source	Enthalten in International journal of applied earth observation and geoinformation 104 volume:104
sourceStr	Enthalten in International journal of applied earth observation and geoinformation 104 volume:104
format_phy_str_mv	Article
bklname	Methoden und Techniken der Geowissenschaften Geoinformationssysteme Luftaufnahmen Photogrammetrie
institution	findex.gbv.de
topic_facet	Point clouds Registration 3D deep learning Keypoint detection Feature descriptor
dewey-raw	550
isfreeaccess_bool	false
container_title	International journal of applied earth observation and geoinformation
authorswithroles_txt_mv	Wang, Yuan @@aut@@ Yang, Bisheng @@aut@@ Chen, Yiping @@aut@@ Liang, Fuxun @@aut@@ Dong, Zhen @@aut@@
publishDateDaySort_date	2021-01-01T00:00:00Z
hierarchy_top_id	359784119
dewey-sort	3550
id	ELV006870414
language_de	englisch
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author	Wang, Yuan
spellingShingle	Wang, Yuan ddc 550 fid KARTEN bkl 38.03 bkl 74.48 bkl 74.41 misc Point clouds misc Registration misc 3D deep learning misc Keypoint detection misc Feature descriptor JoKDNet: A joint keypoint detection and description network for large-scale outdoor TLS point clouds registration
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topic_title	550 DE-600 KARTEN DE-1a fid 38.03 bkl 74.48 bkl 74.41 bkl JoKDNet: A joint keypoint detection and description network for large-scale outdoor TLS point clouds registration Point clouds Registration 3D deep learning Keypoint detection Feature descriptor
topic	ddc 550 fid KARTEN bkl 38.03 bkl 74.48 bkl 74.41 misc Point clouds misc Registration misc 3D deep learning misc Keypoint detection misc Feature descriptor
topic_unstemmed	ddc 550 fid KARTEN bkl 38.03 bkl 74.48 bkl 74.41 misc Point clouds misc Registration misc 3D deep learning misc Keypoint detection misc Feature descriptor
topic_browse	ddc 550 fid KARTEN bkl 38.03 bkl 74.48 bkl 74.41 misc Point clouds misc Registration misc 3D deep learning misc Keypoint detection misc Feature descriptor
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title	JoKDNet: A joint keypoint detection and description network for large-scale outdoor TLS point clouds registration
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title_full	JoKDNet: A joint keypoint detection and description network for large-scale outdoor TLS point clouds registration
author_sort	Wang, Yuan
journal	International journal of applied earth observation and geoinformation
journalStr	International journal of applied earth observation and geoinformation
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author_browse	Wang, Yuan Yang, Bisheng Chen, Yiping Liang, Fuxun Dong, Zhen
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author-letter	Wang, Yuan
doi_str_mv	10.1016/j.jag.2021.102534
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title_sort	jokdnet: a joint keypoint detection and description network for large-scale outdoor tls point clouds registration
title_auth	JoKDNet: A joint keypoint detection and description network for large-scale outdoor TLS point clouds registration
abstract	Registration of large-scale outdoor Terrestrial Laser Scanning (TLS) point clouds remains many challenges in the scenes with symmetric and repetitive elements (e.g., park, forest, and tunnel), the weak geometric features (e.g., underground excavation), and dramatically changes in different phases (e.g., mountain). To address these issues, a novel neural network JoKDNet is proposed to jointly learn the keypoint detection and feature description to improve the feasibility and accuracy of point clouds registration. Firstly, a novel keypoint detection module is introduced to automatically learn the score of each sampled point and regard the most significant Top-k sampled points as the detected keypoints. Secondly, an enhanced feature description module is proposed to learn the feature representation of each keypoint by fusing the hierarchical local features and context features. Thirdly, a loss function is designed to make the detected keypoints more distinguishable for matching, which simultaneously maximizes the feature distance between non-corresponding keypoints and minimizes the feature distance of corresponding keypoints. Finally, the distance matrix module and RANdom SAmple Consensus (RANSAC) are utilized to determine the correspondences of source and target point clouds for the transformation calculation. Comprehensive experiments show that the JoKDNet performs effectively on five challenging scenes (e.g., park, forest, tunnel, underground excavation, and mountain) from two datasets (WHU-TLS and ETH-TLS) in terms of registration errors, and robustness to varying scenes, with the maximum rotation error less than 0.06° and maximum translation error less than 0.84 m without ICP.
abstractGer	Registration of large-scale outdoor Terrestrial Laser Scanning (TLS) point clouds remains many challenges in the scenes with symmetric and repetitive elements (e.g., park, forest, and tunnel), the weak geometric features (e.g., underground excavation), and dramatically changes in different phases (e.g., mountain). To address these issues, a novel neural network JoKDNet is proposed to jointly learn the keypoint detection and feature description to improve the feasibility and accuracy of point clouds registration. Firstly, a novel keypoint detection module is introduced to automatically learn the score of each sampled point and regard the most significant Top-k sampled points as the detected keypoints. Secondly, an enhanced feature description module is proposed to learn the feature representation of each keypoint by fusing the hierarchical local features and context features. Thirdly, a loss function is designed to make the detected keypoints more distinguishable for matching, which simultaneously maximizes the feature distance between non-corresponding keypoints and minimizes the feature distance of corresponding keypoints. Finally, the distance matrix module and RANdom SAmple Consensus (RANSAC) are utilized to determine the correspondences of source and target point clouds for the transformation calculation. Comprehensive experiments show that the JoKDNet performs effectively on five challenging scenes (e.g., park, forest, tunnel, underground excavation, and mountain) from two datasets (WHU-TLS and ETH-TLS) in terms of registration errors, and robustness to varying scenes, with the maximum rotation error less than 0.06° and maximum translation error less than 0.84 m without ICP.
abstract_unstemmed	Registration of large-scale outdoor Terrestrial Laser Scanning (TLS) point clouds remains many challenges in the scenes with symmetric and repetitive elements (e.g., park, forest, and tunnel), the weak geometric features (e.g., underground excavation), and dramatically changes in different phases (e.g., mountain). To address these issues, a novel neural network JoKDNet is proposed to jointly learn the keypoint detection and feature description to improve the feasibility and accuracy of point clouds registration. Firstly, a novel keypoint detection module is introduced to automatically learn the score of each sampled point and regard the most significant Top-k sampled points as the detected keypoints. Secondly, an enhanced feature description module is proposed to learn the feature representation of each keypoint by fusing the hierarchical local features and context features. Thirdly, a loss function is designed to make the detected keypoints more distinguishable for matching, which simultaneously maximizes the feature distance between non-corresponding keypoints and minimizes the feature distance of corresponding keypoints. Finally, the distance matrix module and RANdom SAmple Consensus (RANSAC) are utilized to determine the correspondences of source and target point clouds for the transformation calculation. Comprehensive experiments show that the JoKDNet performs effectively on five challenging scenes (e.g., park, forest, tunnel, underground excavation, and mountain) from two datasets (WHU-TLS and ETH-TLS) in terms of registration errors, and robustness to varying scenes, with the maximum rotation error less than 0.06° and maximum translation error less than 0.84 m without ICP.
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title_short	JoKDNet: A joint keypoint detection and description network for large-scale outdoor TLS point clouds registration
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author2	Yang, Bisheng Chen, Yiping Liang, Fuxun Dong, Zhen
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doi_str	10.1016/j.jag.2021.102534
up_date	2024-07-06T22:50:02.181Z
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JoKDNet: A joint keypoint detection and description network for large-scale outdoor TLS point clouds registration

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