Filtering of Point Clouds Using Fusion of Three Types of Primitives Including Points, Objects and Key Points

Primitive, being the basic processing unit, is one of the key factors to determine the accuracy and efficiency of point cloud filtering. Triangular irregular network (TIN) progressive densification (TPD) and object-based TIN progressive densification (OTPD) are two existing filtering methods, but si...
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Autor*in:	LIN Xiangguo [verfasserIn] ZHANG Jixian [verfasserIn] NING Xiaogang [verfasserIn] DUAN Minyan [verfasserIn] ZANG Yi [verfasserIn]

Format:	E-Artikel
Sprache:	Chinesisch

Erschienen:	2016

Schlagwörter:	filtering LiDAR point cloud photogrammetric point cloud objects triangular irregular network

Übergeordnetes Werk:	In: Acta Geodaetica et Cartographica Sinica - Surveying and Mapping Press, 2014, 45(2016), 11, Seite 1308-1317
Übergeordnetes Werk:	volume:45 ; year:2016 ; number:11 ; pages:1308-1317

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc Journal toc

DOI / URN:	10.11947/j.AGCS.2016.20160372

Katalog-ID:	DOAJ052254003

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520			\|a Primitive, being the basic processing unit, is one of the key factors to determine the accuracy and efficiency of point cloud filtering. Triangular irregular network (TIN) progressive densification (TPD) and object-based TIN progressive densification (OTPD) are two existing filtering methods, but single primitive is employed by them. A multiple-primitives-based TIN progressive densification (MPTPD) filtering method is proposed. It is composed of three key stages, including point cloud segmentation, extraction of key points of objects, the key-points-based judging of the objects. Specifically, point, object and the key points are the primitive of the above three stages respectively. Four testing datasets, including two airborne LiDAR and two photogrammetric point clouds, are used to verify the overall performances of the above three filtering methods. Experimental results suggest that the proposed MPTPD has the best overall performance. In the viewpoint of accuracy, MPTPD and OTPD have the similar accuracy. Moreover, compared with the TPD, MPTPD is able to reduce omission errors and total errors by 22.07% and 8.44% respectively. In the viewpoint of efficiency, under most of the cases, TPD is the highest, MPTPD is the second, and OTPD is the slowest. Moreover, the total time cost of MPTPD is only 57.93% of the one of OTPD.
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allfields	10.11947/j.AGCS.2016.20160372 doi (DE-627)DOAJ052254003 (DE-599)DOAJ7fde3f70bf1b40ca9178ac423d2e97f1 DE-627 ger DE-627 rakwb chi GA1-1776 LIN Xiangguo verfasserin aut Filtering of Point Clouds Using Fusion of Three Types of Primitives Including Points, Objects and Key Points 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Primitive, being the basic processing unit, is one of the key factors to determine the accuracy and efficiency of point cloud filtering. Triangular irregular network (TIN) progressive densification (TPD) and object-based TIN progressive densification (OTPD) are two existing filtering methods, but single primitive is employed by them. A multiple-primitives-based TIN progressive densification (MPTPD) filtering method is proposed. It is composed of three key stages, including point cloud segmentation, extraction of key points of objects, the key-points-based judging of the objects. Specifically, point, object and the key points are the primitive of the above three stages respectively. Four testing datasets, including two airborne LiDAR and two photogrammetric point clouds, are used to verify the overall performances of the above three filtering methods. Experimental results suggest that the proposed MPTPD has the best overall performance. In the viewpoint of accuracy, MPTPD and OTPD have the similar accuracy. Moreover, compared with the TPD, MPTPD is able to reduce omission errors and total errors by 22.07% and 8.44% respectively. In the viewpoint of efficiency, under most of the cases, TPD is the highest, MPTPD is the second, and OTPD is the slowest. Moreover, the total time cost of MPTPD is only 57.93% of the one of OTPD. filtering LiDAR point cloud photogrammetric point cloud objects triangular irregular network Mathematical geography. Cartography ZHANG Jixian verfasserin aut NING Xiaogang verfasserin aut DUAN Minyan verfasserin aut ZANG Yi verfasserin aut In Acta Geodaetica et Cartographica Sinica Surveying and Mapping Press, 2014 45(2016), 11, Seite 1308-1317 (DE-627)57517014X (DE-600)2445687-1 10011595 nnns volume:45 year:2016 number:11 pages:1308-1317 https://doi.org/10.11947/j.AGCS.2016.20160372 kostenfrei https://doaj.org/article/7fde3f70bf1b40ca9178ac423d2e97f1 kostenfrei http://html.rhhz.net/CHXB/html/2016-11-1308.htm kostenfrei https://doaj.org/toc/1001-1595 Journal toc kostenfrei https://doaj.org/toc/1001-1595 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ 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_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 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_4392 GBV_ILN_4700 AR 45 2016 11 1308-1317
spelling	10.11947/j.AGCS.2016.20160372 doi (DE-627)DOAJ052254003 (DE-599)DOAJ7fde3f70bf1b40ca9178ac423d2e97f1 DE-627 ger DE-627 rakwb chi GA1-1776 LIN Xiangguo verfasserin aut Filtering of Point Clouds Using Fusion of Three Types of Primitives Including Points, Objects and Key Points 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Primitive, being the basic processing unit, is one of the key factors to determine the accuracy and efficiency of point cloud filtering. Triangular irregular network (TIN) progressive densification (TPD) and object-based TIN progressive densification (OTPD) are two existing filtering methods, but single primitive is employed by them. A multiple-primitives-based TIN progressive densification (MPTPD) filtering method is proposed. It is composed of three key stages, including point cloud segmentation, extraction of key points of objects, the key-points-based judging of the objects. Specifically, point, object and the key points are the primitive of the above three stages respectively. Four testing datasets, including two airborne LiDAR and two photogrammetric point clouds, are used to verify the overall performances of the above three filtering methods. Experimental results suggest that the proposed MPTPD has the best overall performance. In the viewpoint of accuracy, MPTPD and OTPD have the similar accuracy. Moreover, compared with the TPD, MPTPD is able to reduce omission errors and total errors by 22.07% and 8.44% respectively. In the viewpoint of efficiency, under most of the cases, TPD is the highest, MPTPD is the second, and OTPD is the slowest. Moreover, the total time cost of MPTPD is only 57.93% of the one of OTPD. filtering LiDAR point cloud photogrammetric point cloud objects triangular irregular network Mathematical geography. Cartography ZHANG Jixian verfasserin aut NING Xiaogang verfasserin aut DUAN Minyan verfasserin aut ZANG Yi verfasserin aut In Acta Geodaetica et Cartographica Sinica Surveying and Mapping Press, 2014 45(2016), 11, Seite 1308-1317 (DE-627)57517014X (DE-600)2445687-1 10011595 nnns volume:45 year:2016 number:11 pages:1308-1317 https://doi.org/10.11947/j.AGCS.2016.20160372 kostenfrei https://doaj.org/article/7fde3f70bf1b40ca9178ac423d2e97f1 kostenfrei http://html.rhhz.net/CHXB/html/2016-11-1308.htm kostenfrei https://doaj.org/toc/1001-1595 Journal toc kostenfrei https://doaj.org/toc/1001-1595 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ 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_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 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_4392 GBV_ILN_4700 AR 45 2016 11 1308-1317
allfields_unstemmed	10.11947/j.AGCS.2016.20160372 doi (DE-627)DOAJ052254003 (DE-599)DOAJ7fde3f70bf1b40ca9178ac423d2e97f1 DE-627 ger DE-627 rakwb chi GA1-1776 LIN Xiangguo verfasserin aut Filtering of Point Clouds Using Fusion of Three Types of Primitives Including Points, Objects and Key Points 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Primitive, being the basic processing unit, is one of the key factors to determine the accuracy and efficiency of point cloud filtering. Triangular irregular network (TIN) progressive densification (TPD) and object-based TIN progressive densification (OTPD) are two existing filtering methods, but single primitive is employed by them. A multiple-primitives-based TIN progressive densification (MPTPD) filtering method is proposed. It is composed of three key stages, including point cloud segmentation, extraction of key points of objects, the key-points-based judging of the objects. Specifically, point, object and the key points are the primitive of the above three stages respectively. Four testing datasets, including two airborne LiDAR and two photogrammetric point clouds, are used to verify the overall performances of the above three filtering methods. Experimental results suggest that the proposed MPTPD has the best overall performance. In the viewpoint of accuracy, MPTPD and OTPD have the similar accuracy. Moreover, compared with the TPD, MPTPD is able to reduce omission errors and total errors by 22.07% and 8.44% respectively. In the viewpoint of efficiency, under most of the cases, TPD is the highest, MPTPD is the second, and OTPD is the slowest. Moreover, the total time cost of MPTPD is only 57.93% of the one of OTPD. filtering LiDAR point cloud photogrammetric point cloud objects triangular irregular network Mathematical geography. Cartography ZHANG Jixian verfasserin aut NING Xiaogang verfasserin aut DUAN Minyan verfasserin aut ZANG Yi verfasserin aut In Acta Geodaetica et Cartographica Sinica Surveying and Mapping Press, 2014 45(2016), 11, Seite 1308-1317 (DE-627)57517014X (DE-600)2445687-1 10011595 nnns volume:45 year:2016 number:11 pages:1308-1317 https://doi.org/10.11947/j.AGCS.2016.20160372 kostenfrei https://doaj.org/article/7fde3f70bf1b40ca9178ac423d2e97f1 kostenfrei http://html.rhhz.net/CHXB/html/2016-11-1308.htm kostenfrei https://doaj.org/toc/1001-1595 Journal toc kostenfrei https://doaj.org/toc/1001-1595 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ 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_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 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_4392 GBV_ILN_4700 AR 45 2016 11 1308-1317
allfieldsGer	10.11947/j.AGCS.2016.20160372 doi (DE-627)DOAJ052254003 (DE-599)DOAJ7fde3f70bf1b40ca9178ac423d2e97f1 DE-627 ger DE-627 rakwb chi GA1-1776 LIN Xiangguo verfasserin aut Filtering of Point Clouds Using Fusion of Three Types of Primitives Including Points, Objects and Key Points 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Primitive, being the basic processing unit, is one of the key factors to determine the accuracy and efficiency of point cloud filtering. Triangular irregular network (TIN) progressive densification (TPD) and object-based TIN progressive densification (OTPD) are two existing filtering methods, but single primitive is employed by them. A multiple-primitives-based TIN progressive densification (MPTPD) filtering method is proposed. It is composed of three key stages, including point cloud segmentation, extraction of key points of objects, the key-points-based judging of the objects. Specifically, point, object and the key points are the primitive of the above three stages respectively. Four testing datasets, including two airborne LiDAR and two photogrammetric point clouds, are used to verify the overall performances of the above three filtering methods. Experimental results suggest that the proposed MPTPD has the best overall performance. In the viewpoint of accuracy, MPTPD and OTPD have the similar accuracy. Moreover, compared with the TPD, MPTPD is able to reduce omission errors and total errors by 22.07% and 8.44% respectively. In the viewpoint of efficiency, under most of the cases, TPD is the highest, MPTPD is the second, and OTPD is the slowest. Moreover, the total time cost of MPTPD is only 57.93% of the one of OTPD. filtering LiDAR point cloud photogrammetric point cloud objects triangular irregular network Mathematical geography. Cartography ZHANG Jixian verfasserin aut NING Xiaogang verfasserin aut DUAN Minyan verfasserin aut ZANG Yi verfasserin aut In Acta Geodaetica et Cartographica Sinica Surveying and Mapping Press, 2014 45(2016), 11, Seite 1308-1317 (DE-627)57517014X (DE-600)2445687-1 10011595 nnns volume:45 year:2016 number:11 pages:1308-1317 https://doi.org/10.11947/j.AGCS.2016.20160372 kostenfrei https://doaj.org/article/7fde3f70bf1b40ca9178ac423d2e97f1 kostenfrei http://html.rhhz.net/CHXB/html/2016-11-1308.htm kostenfrei https://doaj.org/toc/1001-1595 Journal toc kostenfrei https://doaj.org/toc/1001-1595 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ 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_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 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_4392 GBV_ILN_4700 AR 45 2016 11 1308-1317
allfieldsSound	10.11947/j.AGCS.2016.20160372 doi (DE-627)DOAJ052254003 (DE-599)DOAJ7fde3f70bf1b40ca9178ac423d2e97f1 DE-627 ger DE-627 rakwb chi GA1-1776 LIN Xiangguo verfasserin aut Filtering of Point Clouds Using Fusion of Three Types of Primitives Including Points, Objects and Key Points 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Primitive, being the basic processing unit, is one of the key factors to determine the accuracy and efficiency of point cloud filtering. Triangular irregular network (TIN) progressive densification (TPD) and object-based TIN progressive densification (OTPD) are two existing filtering methods, but single primitive is employed by them. A multiple-primitives-based TIN progressive densification (MPTPD) filtering method is proposed. It is composed of three key stages, including point cloud segmentation, extraction of key points of objects, the key-points-based judging of the objects. Specifically, point, object and the key points are the primitive of the above three stages respectively. Four testing datasets, including two airborne LiDAR and two photogrammetric point clouds, are used to verify the overall performances of the above three filtering methods. Experimental results suggest that the proposed MPTPD has the best overall performance. In the viewpoint of accuracy, MPTPD and OTPD have the similar accuracy. Moreover, compared with the TPD, MPTPD is able to reduce omission errors and total errors by 22.07% and 8.44% respectively. In the viewpoint of efficiency, under most of the cases, TPD is the highest, MPTPD is the second, and OTPD is the slowest. Moreover, the total time cost of MPTPD is only 57.93% of the one of OTPD. filtering LiDAR point cloud photogrammetric point cloud objects triangular irregular network Mathematical geography. Cartography ZHANG Jixian verfasserin aut NING Xiaogang verfasserin aut DUAN Minyan verfasserin aut ZANG Yi verfasserin aut In Acta Geodaetica et Cartographica Sinica Surveying and Mapping Press, 2014 45(2016), 11, Seite 1308-1317 (DE-627)57517014X (DE-600)2445687-1 10011595 nnns volume:45 year:2016 number:11 pages:1308-1317 https://doi.org/10.11947/j.AGCS.2016.20160372 kostenfrei https://doaj.org/article/7fde3f70bf1b40ca9178ac423d2e97f1 kostenfrei http://html.rhhz.net/CHXB/html/2016-11-1308.htm kostenfrei https://doaj.org/toc/1001-1595 Journal toc kostenfrei https://doaj.org/toc/1001-1595 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ 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_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 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_4392 GBV_ILN_4700 AR 45 2016 11 1308-1317
language	Chinese
source	In Acta Geodaetica et Cartographica Sinica 45(2016), 11, Seite 1308-1317 volume:45 year:2016 number:11 pages:1308-1317
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topic_facet	filtering LiDAR point cloud photogrammetric point cloud objects triangular irregular network Mathematical geography. Cartography
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container_title	Acta Geodaetica et Cartographica Sinica
authorswithroles_txt_mv	LIN Xiangguo @@aut@@ ZHANG Jixian @@aut@@ NING Xiaogang @@aut@@ DUAN Minyan @@aut@@ ZANG Yi @@aut@@
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callnumber-first	G - Geography, Anthropology, Recreation
author	LIN Xiangguo
spellingShingle	LIN Xiangguo misc GA1-1776 misc filtering misc LiDAR point cloud misc photogrammetric point cloud misc objects misc triangular irregular network misc Mathematical geography. Cartography Filtering of Point Clouds Using Fusion of Three Types of Primitives Including Points, Objects and Key Points
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topic_title	GA1-1776 Filtering of Point Clouds Using Fusion of Three Types of Primitives Including Points, Objects and Key Points filtering LiDAR point cloud photogrammetric point cloud objects triangular irregular network
topic	misc GA1-1776 misc filtering misc LiDAR point cloud misc photogrammetric point cloud misc objects misc triangular irregular network misc Mathematical geography. Cartography
topic_unstemmed	misc GA1-1776 misc filtering misc LiDAR point cloud misc photogrammetric point cloud misc objects misc triangular irregular network misc Mathematical geography. Cartography
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title	Filtering of Point Clouds Using Fusion of Three Types of Primitives Including Points, Objects and Key Points
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title_sort	filtering of point clouds using fusion of three types of primitives including points, objects and key points
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title_auth	Filtering of Point Clouds Using Fusion of Three Types of Primitives Including Points, Objects and Key Points
abstract	Primitive, being the basic processing unit, is one of the key factors to determine the accuracy and efficiency of point cloud filtering. Triangular irregular network (TIN) progressive densification (TPD) and object-based TIN progressive densification (OTPD) are two existing filtering methods, but single primitive is employed by them. A multiple-primitives-based TIN progressive densification (MPTPD) filtering method is proposed. It is composed of three key stages, including point cloud segmentation, extraction of key points of objects, the key-points-based judging of the objects. Specifically, point, object and the key points are the primitive of the above three stages respectively. Four testing datasets, including two airborne LiDAR and two photogrammetric point clouds, are used to verify the overall performances of the above three filtering methods. Experimental results suggest that the proposed MPTPD has the best overall performance. In the viewpoint of accuracy, MPTPD and OTPD have the similar accuracy. Moreover, compared with the TPD, MPTPD is able to reduce omission errors and total errors by 22.07% and 8.44% respectively. In the viewpoint of efficiency, under most of the cases, TPD is the highest, MPTPD is the second, and OTPD is the slowest. Moreover, the total time cost of MPTPD is only 57.93% of the one of OTPD.
abstractGer	Primitive, being the basic processing unit, is one of the key factors to determine the accuracy and efficiency of point cloud filtering. Triangular irregular network (TIN) progressive densification (TPD) and object-based TIN progressive densification (OTPD) are two existing filtering methods, but single primitive is employed by them. A multiple-primitives-based TIN progressive densification (MPTPD) filtering method is proposed. It is composed of three key stages, including point cloud segmentation, extraction of key points of objects, the key-points-based judging of the objects. Specifically, point, object and the key points are the primitive of the above three stages respectively. Four testing datasets, including two airborne LiDAR and two photogrammetric point clouds, are used to verify the overall performances of the above three filtering methods. Experimental results suggest that the proposed MPTPD has the best overall performance. In the viewpoint of accuracy, MPTPD and OTPD have the similar accuracy. Moreover, compared with the TPD, MPTPD is able to reduce omission errors and total errors by 22.07% and 8.44% respectively. In the viewpoint of efficiency, under most of the cases, TPD is the highest, MPTPD is the second, and OTPD is the slowest. Moreover, the total time cost of MPTPD is only 57.93% of the one of OTPD.
abstract_unstemmed	Primitive, being the basic processing unit, is one of the key factors to determine the accuracy and efficiency of point cloud filtering. Triangular irregular network (TIN) progressive densification (TPD) and object-based TIN progressive densification (OTPD) are two existing filtering methods, but single primitive is employed by them. A multiple-primitives-based TIN progressive densification (MPTPD) filtering method is proposed. It is composed of three key stages, including point cloud segmentation, extraction of key points of objects, the key-points-based judging of the objects. Specifically, point, object and the key points are the primitive of the above three stages respectively. Four testing datasets, including two airborne LiDAR and two photogrammetric point clouds, are used to verify the overall performances of the above three filtering methods. Experimental results suggest that the proposed MPTPD has the best overall performance. In the viewpoint of accuracy, MPTPD and OTPD have the similar accuracy. Moreover, compared with the TPD, MPTPD is able to reduce omission errors and total errors by 22.07% and 8.44% respectively. In the viewpoint of efficiency, under most of the cases, TPD is the highest, MPTPD is the second, and OTPD is the slowest. Moreover, the total time cost of MPTPD is only 57.93% of the one of OTPD.
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title_short	Filtering of Point Clouds Using Fusion of Three Types of Primitives Including Points, Objects and Key Points
url	https://doi.org/10.11947/j.AGCS.2016.20160372 https://doaj.org/article/7fde3f70bf1b40ca9178ac423d2e97f1 http://html.rhhz.net/CHXB/html/2016-11-1308.htm https://doaj.org/toc/1001-1595
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author2	ZHANG Jixian NING Xiaogang DUAN Minyan ZANG Yi
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doi_str	10.11947/j.AGCS.2016.20160372
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up_date	2024-07-04T00:20:46.335Z
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Filtering of Point Clouds Using Fusion of Three Types of Primitives Including Points, Objects and Key Points

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