Boundary-Aware graph Markov neural network for semiautomated object segmentation from point clouds

Due to the advantages of 3D point clouds over 2D optical images, the related researches on scene understanding in 3D point clouds have been increasingly attracting wide attention from academy and industry. However, many 3D scene understanding methods largely require abundant supervised information f...
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Autor*in:	Huan Luo [verfasserIn] Quan Zheng [verfasserIn] Lina Fang [verfasserIn] Yingya Guo [verfasserIn] Wenzhong Guo [verfasserIn] Cheng Wang [verfasserIn] Jonathan Li [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2021

Schlagwörter:	Point Cloud 3D Object Segmentation Boundary Constraint Graph Neural Network Markov Random Field

Übergeordnetes Werk:	In: International Journal of Applied Earth Observations and Geoinformation - Elsevier, 2022, 104(2021), Seite 102564-
Übergeordnetes Werk:	volume:104 ; year:2021 ; pages:102564-

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.1016/j.jag.2021.102564

Katalog-ID:	DOAJ029130336

Internformat


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allfields	10.1016/j.jag.2021.102564 doi (DE-627)DOAJ029130336 (DE-599)DOAJ88ddb1228eb24c109cc757e7c362f8aa DE-627 ger DE-627 rakwb eng GB3-5030 GE1-350 Huan Luo verfasserin aut Boundary-Aware graph Markov neural network for semiautomated object segmentation from point clouds 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Due to the advantages of 3D point clouds over 2D optical images, the related researches on scene understanding in 3D point clouds have been increasingly attracting wide attention from academy and industry. However, many 3D scene understanding methods largely require abundant supervised information for training a data-driven model. The acquisition of such supervised information relies on manual annotations which are laborious and arduous. Therefore, to mitigate such manual efforts for annotating training samples, this paper studies a unified neural network to segment 3D objects out of point clouds interactively. Particularly, to improve the segmentation performance on the accurate object segmentation, the boundary information of 3D objects in point clouds are encoded as a boundary energy term in the Markov Random Field (MRF) model. Moreover, the MRF model with the boundary energy term is naturally integrated with the Graphical Neural Network (GNN) to obtain a compact representation for generating the boundary-preserved 3D objects. The proposed method is evaluated on two point clouds datasets obtained from different types of laser scanning systems, i.e. terrestrial laser scanning system and mobile laser scanning system. Comparative experiments show that the proposed method is superior and effective in 3D objects segmentation in different point-cloud scenarios. Point Cloud 3D Object Segmentation Boundary Constraint Graph Neural Network Markov Random Field Physical geography Environmental sciences Quan Zheng verfasserin aut Lina Fang verfasserin aut Yingya Guo verfasserin aut Wenzhong Guo verfasserin aut Cheng Wang verfasserin aut Jonathan Li verfasserin aut In International Journal of Applied Earth Observations and Geoinformation Elsevier, 2022 104(2021), Seite 102564- (DE-627)359784119 (DE-600)2097960-5 1872826X nnns volume:104 year:2021 pages:102564- https://doi.org/10.1016/j.jag.2021.102564 kostenfrei https://doaj.org/article/88ddb1228eb24c109cc757e7c362f8aa kostenfrei http://www.sciencedirect.com/science/article/pii/S0303243421002713 kostenfrei https://doaj.org/toc/1569-8432 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA 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 AR 104 2021 102564-
spelling	10.1016/j.jag.2021.102564 doi (DE-627)DOAJ029130336 (DE-599)DOAJ88ddb1228eb24c109cc757e7c362f8aa DE-627 ger DE-627 rakwb eng GB3-5030 GE1-350 Huan Luo verfasserin aut Boundary-Aware graph Markov neural network for semiautomated object segmentation from point clouds 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Due to the advantages of 3D point clouds over 2D optical images, the related researches on scene understanding in 3D point clouds have been increasingly attracting wide attention from academy and industry. However, many 3D scene understanding methods largely require abundant supervised information for training a data-driven model. The acquisition of such supervised information relies on manual annotations which are laborious and arduous. Therefore, to mitigate such manual efforts for annotating training samples, this paper studies a unified neural network to segment 3D objects out of point clouds interactively. Particularly, to improve the segmentation performance on the accurate object segmentation, the boundary information of 3D objects in point clouds are encoded as a boundary energy term in the Markov Random Field (MRF) model. Moreover, the MRF model with the boundary energy term is naturally integrated with the Graphical Neural Network (GNN) to obtain a compact representation for generating the boundary-preserved 3D objects. The proposed method is evaluated on two point clouds datasets obtained from different types of laser scanning systems, i.e. terrestrial laser scanning system and mobile laser scanning system. Comparative experiments show that the proposed method is superior and effective in 3D objects segmentation in different point-cloud scenarios. Point Cloud 3D Object Segmentation Boundary Constraint Graph Neural Network Markov Random Field Physical geography Environmental sciences Quan Zheng verfasserin aut Lina Fang verfasserin aut Yingya Guo verfasserin aut Wenzhong Guo verfasserin aut Cheng Wang verfasserin aut Jonathan Li verfasserin aut In International Journal of Applied Earth Observations and Geoinformation Elsevier, 2022 104(2021), Seite 102564- (DE-627)359784119 (DE-600)2097960-5 1872826X nnns volume:104 year:2021 pages:102564- https://doi.org/10.1016/j.jag.2021.102564 kostenfrei https://doaj.org/article/88ddb1228eb24c109cc757e7c362f8aa kostenfrei http://www.sciencedirect.com/science/article/pii/S0303243421002713 kostenfrei https://doaj.org/toc/1569-8432 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA 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 AR 104 2021 102564-
allfields_unstemmed	10.1016/j.jag.2021.102564 doi (DE-627)DOAJ029130336 (DE-599)DOAJ88ddb1228eb24c109cc757e7c362f8aa DE-627 ger DE-627 rakwb eng GB3-5030 GE1-350 Huan Luo verfasserin aut Boundary-Aware graph Markov neural network for semiautomated object segmentation from point clouds 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Due to the advantages of 3D point clouds over 2D optical images, the related researches on scene understanding in 3D point clouds have been increasingly attracting wide attention from academy and industry. However, many 3D scene understanding methods largely require abundant supervised information for training a data-driven model. The acquisition of such supervised information relies on manual annotations which are laborious and arduous. Therefore, to mitigate such manual efforts for annotating training samples, this paper studies a unified neural network to segment 3D objects out of point clouds interactively. Particularly, to improve the segmentation performance on the accurate object segmentation, the boundary information of 3D objects in point clouds are encoded as a boundary energy term in the Markov Random Field (MRF) model. Moreover, the MRF model with the boundary energy term is naturally integrated with the Graphical Neural Network (GNN) to obtain a compact representation for generating the boundary-preserved 3D objects. The proposed method is evaluated on two point clouds datasets obtained from different types of laser scanning systems, i.e. terrestrial laser scanning system and mobile laser scanning system. Comparative experiments show that the proposed method is superior and effective in 3D objects segmentation in different point-cloud scenarios. Point Cloud 3D Object Segmentation Boundary Constraint Graph Neural Network Markov Random Field Physical geography Environmental sciences Quan Zheng verfasserin aut Lina Fang verfasserin aut Yingya Guo verfasserin aut Wenzhong Guo verfasserin aut Cheng Wang verfasserin aut Jonathan Li verfasserin aut In International Journal of Applied Earth Observations and Geoinformation Elsevier, 2022 104(2021), Seite 102564- (DE-627)359784119 (DE-600)2097960-5 1872826X nnns volume:104 year:2021 pages:102564- https://doi.org/10.1016/j.jag.2021.102564 kostenfrei https://doaj.org/article/88ddb1228eb24c109cc757e7c362f8aa kostenfrei http://www.sciencedirect.com/science/article/pii/S0303243421002713 kostenfrei https://doaj.org/toc/1569-8432 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA 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 AR 104 2021 102564-
allfieldsGer	10.1016/j.jag.2021.102564 doi (DE-627)DOAJ029130336 (DE-599)DOAJ88ddb1228eb24c109cc757e7c362f8aa DE-627 ger DE-627 rakwb eng GB3-5030 GE1-350 Huan Luo verfasserin aut Boundary-Aware graph Markov neural network for semiautomated object segmentation from point clouds 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Due to the advantages of 3D point clouds over 2D optical images, the related researches on scene understanding in 3D point clouds have been increasingly attracting wide attention from academy and industry. However, many 3D scene understanding methods largely require abundant supervised information for training a data-driven model. The acquisition of such supervised information relies on manual annotations which are laborious and arduous. Therefore, to mitigate such manual efforts for annotating training samples, this paper studies a unified neural network to segment 3D objects out of point clouds interactively. Particularly, to improve the segmentation performance on the accurate object segmentation, the boundary information of 3D objects in point clouds are encoded as a boundary energy term in the Markov Random Field (MRF) model. Moreover, the MRF model with the boundary energy term is naturally integrated with the Graphical Neural Network (GNN) to obtain a compact representation for generating the boundary-preserved 3D objects. The proposed method is evaluated on two point clouds datasets obtained from different types of laser scanning systems, i.e. terrestrial laser scanning system and mobile laser scanning system. Comparative experiments show that the proposed method is superior and effective in 3D objects segmentation in different point-cloud scenarios. Point Cloud 3D Object Segmentation Boundary Constraint Graph Neural Network Markov Random Field Physical geography Environmental sciences Quan Zheng verfasserin aut Lina Fang verfasserin aut Yingya Guo verfasserin aut Wenzhong Guo verfasserin aut Cheng Wang verfasserin aut Jonathan Li verfasserin aut In International Journal of Applied Earth Observations and Geoinformation Elsevier, 2022 104(2021), Seite 102564- (DE-627)359784119 (DE-600)2097960-5 1872826X nnns volume:104 year:2021 pages:102564- https://doi.org/10.1016/j.jag.2021.102564 kostenfrei https://doaj.org/article/88ddb1228eb24c109cc757e7c362f8aa kostenfrei http://www.sciencedirect.com/science/article/pii/S0303243421002713 kostenfrei https://doaj.org/toc/1569-8432 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA 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 AR 104 2021 102564-
allfieldsSound	10.1016/j.jag.2021.102564 doi (DE-627)DOAJ029130336 (DE-599)DOAJ88ddb1228eb24c109cc757e7c362f8aa DE-627 ger DE-627 rakwb eng GB3-5030 GE1-350 Huan Luo verfasserin aut Boundary-Aware graph Markov neural network for semiautomated object segmentation from point clouds 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Due to the advantages of 3D point clouds over 2D optical images, the related researches on scene understanding in 3D point clouds have been increasingly attracting wide attention from academy and industry. However, many 3D scene understanding methods largely require abundant supervised information for training a data-driven model. The acquisition of such supervised information relies on manual annotations which are laborious and arduous. Therefore, to mitigate such manual efforts for annotating training samples, this paper studies a unified neural network to segment 3D objects out of point clouds interactively. Particularly, to improve the segmentation performance on the accurate object segmentation, the boundary information of 3D objects in point clouds are encoded as a boundary energy term in the Markov Random Field (MRF) model. Moreover, the MRF model with the boundary energy term is naturally integrated with the Graphical Neural Network (GNN) to obtain a compact representation for generating the boundary-preserved 3D objects. The proposed method is evaluated on two point clouds datasets obtained from different types of laser scanning systems, i.e. terrestrial laser scanning system and mobile laser scanning system. Comparative experiments show that the proposed method is superior and effective in 3D objects segmentation in different point-cloud scenarios. Point Cloud 3D Object Segmentation Boundary Constraint Graph Neural Network Markov Random Field Physical geography Environmental sciences Quan Zheng verfasserin aut Lina Fang verfasserin aut Yingya Guo verfasserin aut Wenzhong Guo verfasserin aut Cheng Wang verfasserin aut Jonathan Li verfasserin aut In International Journal of Applied Earth Observations and Geoinformation Elsevier, 2022 104(2021), Seite 102564- (DE-627)359784119 (DE-600)2097960-5 1872826X nnns volume:104 year:2021 pages:102564- https://doi.org/10.1016/j.jag.2021.102564 kostenfrei https://doaj.org/article/88ddb1228eb24c109cc757e7c362f8aa kostenfrei http://www.sciencedirect.com/science/article/pii/S0303243421002713 kostenfrei https://doaj.org/toc/1569-8432 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA 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 AR 104 2021 102564-
language	English
source	In International Journal of Applied Earth Observations and Geoinformation 104(2021), Seite 102564- volume:104 year:2021 pages:102564-
sourceStr	In International Journal of Applied Earth Observations and Geoinformation 104(2021), Seite 102564- volume:104 year:2021 pages:102564-
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Point Cloud 3D Object Segmentation Boundary Constraint Graph Neural Network Markov Random Field Physical geography Environmental sciences
isfreeaccess_bool	true
container_title	International Journal of Applied Earth Observations and Geoinformation
authorswithroles_txt_mv	Huan Luo @@aut@@ Quan Zheng @@aut@@ Lina Fang @@aut@@ Yingya Guo @@aut@@ Wenzhong Guo @@aut@@ Cheng Wang @@aut@@ Jonathan Li @@aut@@
publishDateDaySort_date	2021-01-01T00:00:00Z
hierarchy_top_id	359784119
id	DOAJ029130336
language_de	englisch
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callnumber-first	G - Geography, Anthropology, Recreation
author	Huan Luo
spellingShingle	Huan Luo misc GB3-5030 misc GE1-350 misc Point Cloud misc 3D Object Segmentation misc Boundary Constraint misc Graph Neural Network misc Markov Random Field misc Physical geography misc Environmental sciences Boundary-Aware graph Markov neural network for semiautomated object segmentation from point clouds
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topic_title	GB3-5030 GE1-350 Boundary-Aware graph Markov neural network for semiautomated object segmentation from point clouds Point Cloud 3D Object Segmentation Boundary Constraint Graph Neural Network Markov Random Field
topic	misc GB3-5030 misc GE1-350 misc Point Cloud misc 3D Object Segmentation misc Boundary Constraint misc Graph Neural Network misc Markov Random Field misc Physical geography misc Environmental sciences
topic_unstemmed	misc GB3-5030 misc GE1-350 misc Point Cloud misc 3D Object Segmentation misc Boundary Constraint misc Graph Neural Network misc Markov Random Field misc Physical geography misc Environmental sciences
topic_browse	misc GB3-5030 misc GE1-350 misc Point Cloud misc 3D Object Segmentation misc Boundary Constraint misc Graph Neural Network misc Markov Random Field misc Physical geography misc Environmental sciences
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title	Boundary-Aware graph Markov neural network for semiautomated object segmentation from point clouds
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title_full	Boundary-Aware graph Markov neural network for semiautomated object segmentation from point clouds
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author_browse	Huan Luo Quan Zheng Lina Fang Yingya Guo Wenzhong Guo Cheng Wang Jonathan Li
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title_sort	boundary-aware graph markov neural network for semiautomated object segmentation from point clouds
callnumber	GB3-5030
title_auth	Boundary-Aware graph Markov neural network for semiautomated object segmentation from point clouds
abstract	Due to the advantages of 3D point clouds over 2D optical images, the related researches on scene understanding in 3D point clouds have been increasingly attracting wide attention from academy and industry. However, many 3D scene understanding methods largely require abundant supervised information for training a data-driven model. The acquisition of such supervised information relies on manual annotations which are laborious and arduous. Therefore, to mitigate such manual efforts for annotating training samples, this paper studies a unified neural network to segment 3D objects out of point clouds interactively. Particularly, to improve the segmentation performance on the accurate object segmentation, the boundary information of 3D objects in point clouds are encoded as a boundary energy term in the Markov Random Field (MRF) model. Moreover, the MRF model with the boundary energy term is naturally integrated with the Graphical Neural Network (GNN) to obtain a compact representation for generating the boundary-preserved 3D objects. The proposed method is evaluated on two point clouds datasets obtained from different types of laser scanning systems, i.e. terrestrial laser scanning system and mobile laser scanning system. Comparative experiments show that the proposed method is superior and effective in 3D objects segmentation in different point-cloud scenarios.
abstractGer	Due to the advantages of 3D point clouds over 2D optical images, the related researches on scene understanding in 3D point clouds have been increasingly attracting wide attention from academy and industry. However, many 3D scene understanding methods largely require abundant supervised information for training a data-driven model. The acquisition of such supervised information relies on manual annotations which are laborious and arduous. Therefore, to mitigate such manual efforts for annotating training samples, this paper studies a unified neural network to segment 3D objects out of point clouds interactively. Particularly, to improve the segmentation performance on the accurate object segmentation, the boundary information of 3D objects in point clouds are encoded as a boundary energy term in the Markov Random Field (MRF) model. Moreover, the MRF model with the boundary energy term is naturally integrated with the Graphical Neural Network (GNN) to obtain a compact representation for generating the boundary-preserved 3D objects. The proposed method is evaluated on two point clouds datasets obtained from different types of laser scanning systems, i.e. terrestrial laser scanning system and mobile laser scanning system. Comparative experiments show that the proposed method is superior and effective in 3D objects segmentation in different point-cloud scenarios.
abstract_unstemmed	Due to the advantages of 3D point clouds over 2D optical images, the related researches on scene understanding in 3D point clouds have been increasingly attracting wide attention from academy and industry. However, many 3D scene understanding methods largely require abundant supervised information for training a data-driven model. The acquisition of such supervised information relies on manual annotations which are laborious and arduous. Therefore, to mitigate such manual efforts for annotating training samples, this paper studies a unified neural network to segment 3D objects out of point clouds interactively. Particularly, to improve the segmentation performance on the accurate object segmentation, the boundary information of 3D objects in point clouds are encoded as a boundary energy term in the Markov Random Field (MRF) model. Moreover, the MRF model with the boundary energy term is naturally integrated with the Graphical Neural Network (GNN) to obtain a compact representation for generating the boundary-preserved 3D objects. The proposed method is evaluated on two point clouds datasets obtained from different types of laser scanning systems, i.e. terrestrial laser scanning system and mobile laser scanning system. Comparative experiments show that the proposed method is superior and effective in 3D objects segmentation in different point-cloud scenarios.
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title_short	Boundary-Aware graph Markov neural network for semiautomated object segmentation from point clouds
url	https://doi.org/10.1016/j.jag.2021.102564 https://doaj.org/article/88ddb1228eb24c109cc757e7c362f8aa http://www.sciencedirect.com/science/article/pii/S0303243421002713 https://doaj.org/toc/1569-8432
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up_date	2024-07-03T21:19:15.535Z
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Boundary-Aware graph Markov neural network for semiautomated object segmentation from point clouds

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