Three-Dimensional Reconstruction with a Laser Line Based on Image In-Painting and Multi-Spectral Photometric Stereo

This paper presents a multi-spectral photometric stereo (MPS) method based on image in-painting, which can reconstruct the shape using a multi-spectral image with a laser line. One of the difficulties in multi-spectral photometric stereo is to extract the laser line because the required illumination...
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Autor*in:	Liang Lu [verfasserIn] Hongbao Zhu [verfasserIn] Junyu Dong [verfasserIn] Yakun Ju [verfasserIn] Huiyu Zhou [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2021

Schlagwörter:	image in-painting generative adversarial network multi-spectral photometric stereo and laser extraction

Übergeordnetes Werk:	In: Sensors - MDPI AG, 2003, 21(2021), 6, p 2131
Übergeordnetes Werk:	volume:21 ; year:2021 ; number:6, p 2131

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3390/s21062131

Katalog-ID:	DOAJ029831776

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allfields	10.3390/s21062131 doi (DE-627)DOAJ029831776 (DE-599)DOAJ6fdc2b859a224686a18c81ddeb103630 DE-627 ger DE-627 rakwb eng TP1-1185 Liang Lu verfasserin aut Three-Dimensional Reconstruction with a Laser Line Based on Image In-Painting and Multi-Spectral Photometric Stereo 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier This paper presents a multi-spectral photometric stereo (MPS) method based on image in-painting, which can reconstruct the shape using a multi-spectral image with a laser line. One of the difficulties in multi-spectral photometric stereo is to extract the laser line because the required illumination for MPS, e.g., red, green, and blue light, may pollute the laser color. Unlike previous methods, through the improvement of the network proposed by Isola, a Generative Adversarial Network based on image in-painting was proposed, to separate a multi-spectral image with a laser line into a clean laser image and an uncorrupted multi-spectral image without the laser line. Then these results were substituted into the method proposed by Fan to obtain high-precision 3D reconstruction results. To make the proposed method applicable to real-world objects, a rendered image dataset obtained using the rendering models in ShapeNet has been used for training the network. Evaluation using the rendered images and real-world images shows the superiority of the proposed approach over several previous methods. image in-painting generative adversarial network multi-spectral photometric stereo and laser extraction Chemical technology Hongbao Zhu verfasserin aut Junyu Dong verfasserin aut Yakun Ju verfasserin aut Huiyu Zhou verfasserin aut In Sensors MDPI AG, 2003 21(2021), 6, p 2131 (DE-627)331640910 (DE-600)2052857-7 14248220 nnns volume:21 year:2021 number:6, p 2131 https://doi.org/10.3390/s21062131 kostenfrei https://doaj.org/article/6fdc2b859a224686a18c81ddeb103630 kostenfrei https://www.mdpi.com/1424-8220/21/6/2131 kostenfrei https://doaj.org/toc/1424-8220 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ 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_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2111 GBV_ILN_2507 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_4700 AR 21 2021 6, p 2131
spelling	10.3390/s21062131 doi (DE-627)DOAJ029831776 (DE-599)DOAJ6fdc2b859a224686a18c81ddeb103630 DE-627 ger DE-627 rakwb eng TP1-1185 Liang Lu verfasserin aut Three-Dimensional Reconstruction with a Laser Line Based on Image In-Painting and Multi-Spectral Photometric Stereo 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier This paper presents a multi-spectral photometric stereo (MPS) method based on image in-painting, which can reconstruct the shape using a multi-spectral image with a laser line. One of the difficulties in multi-spectral photometric stereo is to extract the laser line because the required illumination for MPS, e.g., red, green, and blue light, may pollute the laser color. Unlike previous methods, through the improvement of the network proposed by Isola, a Generative Adversarial Network based on image in-painting was proposed, to separate a multi-spectral image with a laser line into a clean laser image and an uncorrupted multi-spectral image without the laser line. Then these results were substituted into the method proposed by Fan to obtain high-precision 3D reconstruction results. To make the proposed method applicable to real-world objects, a rendered image dataset obtained using the rendering models in ShapeNet has been used for training the network. Evaluation using the rendered images and real-world images shows the superiority of the proposed approach over several previous methods. image in-painting generative adversarial network multi-spectral photometric stereo and laser extraction Chemical technology Hongbao Zhu verfasserin aut Junyu Dong verfasserin aut Yakun Ju verfasserin aut Huiyu Zhou verfasserin aut In Sensors MDPI AG, 2003 21(2021), 6, p 2131 (DE-627)331640910 (DE-600)2052857-7 14248220 nnns volume:21 year:2021 number:6, p 2131 https://doi.org/10.3390/s21062131 kostenfrei https://doaj.org/article/6fdc2b859a224686a18c81ddeb103630 kostenfrei https://www.mdpi.com/1424-8220/21/6/2131 kostenfrei https://doaj.org/toc/1424-8220 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ 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_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2111 GBV_ILN_2507 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_4700 AR 21 2021 6, p 2131
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allfieldsSound	10.3390/s21062131 doi (DE-627)DOAJ029831776 (DE-599)DOAJ6fdc2b859a224686a18c81ddeb103630 DE-627 ger DE-627 rakwb eng TP1-1185 Liang Lu verfasserin aut Three-Dimensional Reconstruction with a Laser Line Based on Image In-Painting and Multi-Spectral Photometric Stereo 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier This paper presents a multi-spectral photometric stereo (MPS) method based on image in-painting, which can reconstruct the shape using a multi-spectral image with a laser line. One of the difficulties in multi-spectral photometric stereo is to extract the laser line because the required illumination for MPS, e.g., red, green, and blue light, may pollute the laser color. Unlike previous methods, through the improvement of the network proposed by Isola, a Generative Adversarial Network based on image in-painting was proposed, to separate a multi-spectral image with a laser line into a clean laser image and an uncorrupted multi-spectral image without the laser line. Then these results were substituted into the method proposed by Fan to obtain high-precision 3D reconstruction results. To make the proposed method applicable to real-world objects, a rendered image dataset obtained using the rendering models in ShapeNet has been used for training the network. Evaluation using the rendered images and real-world images shows the superiority of the proposed approach over several previous methods. image in-painting generative adversarial network multi-spectral photometric stereo and laser extraction Chemical technology Hongbao Zhu verfasserin aut Junyu Dong verfasserin aut Yakun Ju verfasserin aut Huiyu Zhou verfasserin aut In Sensors MDPI AG, 2003 21(2021), 6, p 2131 (DE-627)331640910 (DE-600)2052857-7 14248220 nnns volume:21 year:2021 number:6, p 2131 https://doi.org/10.3390/s21062131 kostenfrei https://doaj.org/article/6fdc2b859a224686a18c81ddeb103630 kostenfrei https://www.mdpi.com/1424-8220/21/6/2131 kostenfrei https://doaj.org/toc/1424-8220 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ 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_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2111 GBV_ILN_2507 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_4700 AR 21 2021 6, p 2131
language	English
source	In Sensors 21(2021), 6, p 2131 volume:21 year:2021 number:6, p 2131
sourceStr	In Sensors 21(2021), 6, p 2131 volume:21 year:2021 number:6, p 2131
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	image in-painting generative adversarial network multi-spectral photometric stereo and laser extraction Chemical technology
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container_title	Sensors
authorswithroles_txt_mv	Liang Lu @@aut@@ Hongbao Zhu @@aut@@ Junyu Dong @@aut@@ Yakun Ju @@aut@@ Huiyu Zhou @@aut@@
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callnumber-first	T - Technology
author	Liang Lu
spellingShingle	Liang Lu misc TP1-1185 misc image in-painting misc generative adversarial network misc multi-spectral photometric stereo misc and laser extraction misc Chemical technology Three-Dimensional Reconstruction with a Laser Line Based on Image In-Painting and Multi-Spectral Photometric Stereo
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topic_title	TP1-1185 Three-Dimensional Reconstruction with a Laser Line Based on Image In-Painting and Multi-Spectral Photometric Stereo image in-painting generative adversarial network multi-spectral photometric stereo and laser extraction
topic	misc TP1-1185 misc image in-painting misc generative adversarial network misc multi-spectral photometric stereo misc and laser extraction misc Chemical technology
topic_unstemmed	misc TP1-1185 misc image in-painting misc generative adversarial network misc multi-spectral photometric stereo misc and laser extraction misc Chemical technology
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title	Three-Dimensional Reconstruction with a Laser Line Based on Image In-Painting and Multi-Spectral Photometric Stereo
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title_full	Three-Dimensional Reconstruction with a Laser Line Based on Image In-Painting and Multi-Spectral Photometric Stereo
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author_browse	Liang Lu Hongbao Zhu Junyu Dong Yakun Ju Huiyu Zhou
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title_sort	three-dimensional reconstruction with a laser line based on image in-painting and multi-spectral photometric stereo
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title_auth	Three-Dimensional Reconstruction with a Laser Line Based on Image In-Painting and Multi-Spectral Photometric Stereo
abstract	This paper presents a multi-spectral photometric stereo (MPS) method based on image in-painting, which can reconstruct the shape using a multi-spectral image with a laser line. One of the difficulties in multi-spectral photometric stereo is to extract the laser line because the required illumination for MPS, e.g., red, green, and blue light, may pollute the laser color. Unlike previous methods, through the improvement of the network proposed by Isola, a Generative Adversarial Network based on image in-painting was proposed, to separate a multi-spectral image with a laser line into a clean laser image and an uncorrupted multi-spectral image without the laser line. Then these results were substituted into the method proposed by Fan to obtain high-precision 3D reconstruction results. To make the proposed method applicable to real-world objects, a rendered image dataset obtained using the rendering models in ShapeNet has been used for training the network. Evaluation using the rendered images and real-world images shows the superiority of the proposed approach over several previous methods.
abstractGer	This paper presents a multi-spectral photometric stereo (MPS) method based on image in-painting, which can reconstruct the shape using a multi-spectral image with a laser line. One of the difficulties in multi-spectral photometric stereo is to extract the laser line because the required illumination for MPS, e.g., red, green, and blue light, may pollute the laser color. Unlike previous methods, through the improvement of the network proposed by Isola, a Generative Adversarial Network based on image in-painting was proposed, to separate a multi-spectral image with a laser line into a clean laser image and an uncorrupted multi-spectral image without the laser line. Then these results were substituted into the method proposed by Fan to obtain high-precision 3D reconstruction results. To make the proposed method applicable to real-world objects, a rendered image dataset obtained using the rendering models in ShapeNet has been used for training the network. Evaluation using the rendered images and real-world images shows the superiority of the proposed approach over several previous methods.
abstract_unstemmed	This paper presents a multi-spectral photometric stereo (MPS) method based on image in-painting, which can reconstruct the shape using a multi-spectral image with a laser line. One of the difficulties in multi-spectral photometric stereo is to extract the laser line because the required illumination for MPS, e.g., red, green, and blue light, may pollute the laser color. Unlike previous methods, through the improvement of the network proposed by Isola, a Generative Adversarial Network based on image in-painting was proposed, to separate a multi-spectral image with a laser line into a clean laser image and an uncorrupted multi-spectral image without the laser line. Then these results were substituted into the method proposed by Fan to obtain high-precision 3D reconstruction results. To make the proposed method applicable to real-world objects, a rendered image dataset obtained using the rendering models in ShapeNet has been used for training the network. Evaluation using the rendered images and real-world images shows the superiority of the proposed approach over several previous methods.
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container_issue	6, p 2131
title_short	Three-Dimensional Reconstruction with a Laser Line Based on Image In-Painting and Multi-Spectral Photometric Stereo
url	https://doi.org/10.3390/s21062131 https://doaj.org/article/6fdc2b859a224686a18c81ddeb103630 https://www.mdpi.com/1424-8220/21/6/2131 https://doaj.org/toc/1424-8220
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author2	Hongbao Zhu Junyu Dong Yakun Ju Huiyu Zhou
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