Optimized segmentation with image inpainting for semantic mapping in dynamic scenes
Abstract Moving objects will obscure static objects in a dynamic scene. When the existing semantic segmentation methods deal with these static objects, there are often missing or errors in segmentation results. To solve this problem, we propose a framework that combines image inpainting and semantic...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Zhang, Jianfeng [verfasserIn] |
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| Format: |
Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2022 |
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| Schlagwörter: |
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| Anmerkung: |
© The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022 |
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| Übergeordnetes Werk: |
Enthalten in: Applied intelligence - Springer US, 1991, 53(2022), 2 vom: 05. Mai, Seite 2173-2188 |
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| Übergeordnetes Werk: |
volume:53 ; year:2022 ; number:2 ; day:05 ; month:05 ; pages:2173-2188 |
| Links: |
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| DOI / URN: |
10.1007/s10489-022-03487-3 |
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OLC2080215647 |
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| 520 | |a Abstract Moving objects will obscure static objects in a dynamic scene. When the existing semantic segmentation methods deal with these static objects, there are often missing or errors in segmentation results. To solve this problem, we propose a framework that combines image inpainting and semantic segmentation, termed SIS. Our framework adds an image inpainting network and an identical semantic segmentation network in series following an original semantic segmentation network, which can make full use of the two semantic segmentation results to obtain the optimized semantic segmentation results in this scene. Moreover, we combined our framework with Simultaneous Localization and Mapping (SLAM), and conducted experiments on the TUM RGB-D dataset. Experimental results show, the combined SLAM system can construct a semantic octree map with more complete and stable semantic information in dynamic scenes. | ||
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| 700 | 1 | |a Zhan, Jiao |4 aut | |
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10.1007/s10489-022-03487-3 doi (DE-627)OLC2080215647 (DE-He213)s10489-022-03487-3-p DE-627 ger DE-627 rakwb eng 004 VZ Zhang, Jianfeng verfasserin aut Optimized segmentation with image inpainting for semantic mapping in dynamic scenes 2022 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022 Abstract Moving objects will obscure static objects in a dynamic scene. When the existing semantic segmentation methods deal with these static objects, there are often missing or errors in segmentation results. To solve this problem, we propose a framework that combines image inpainting and semantic segmentation, termed SIS. Our framework adds an image inpainting network and an identical semantic segmentation network in series following an original semantic segmentation network, which can make full use of the two semantic segmentation results to obtain the optimized semantic segmentation results in this scene. Moreover, we combined our framework with Simultaneous Localization and Mapping (SLAM), and conducted experiments on the TUM RGB-D dataset. Experimental results show, the combined SLAM system can construct a semantic octree map with more complete and stable semantic information in dynamic scenes. Semantic segmentation Image inpainting Semantic mapping Dynamic scenes Visual SLAM Liu, Yang aut Guo, Chi aut Zhan, Jiao aut Enthalten in Applied intelligence Springer US, 1991 53(2022), 2 vom: 05. Mai, Seite 2173-2188 (DE-627)130990515 (DE-600)1080229-0 (DE-576)029154286 0924-669X nnns volume:53 year:2022 number:2 day:05 month:05 pages:2173-2188 https://doi.org/10.1007/s10489-022-03487-3 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT AR 53 2022 2 05 05 2173-2188 |
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10.1007/s10489-022-03487-3 doi (DE-627)OLC2080215647 (DE-He213)s10489-022-03487-3-p DE-627 ger DE-627 rakwb eng 004 VZ Zhang, Jianfeng verfasserin aut Optimized segmentation with image inpainting for semantic mapping in dynamic scenes 2022 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022 Abstract Moving objects will obscure static objects in a dynamic scene. When the existing semantic segmentation methods deal with these static objects, there are often missing or errors in segmentation results. To solve this problem, we propose a framework that combines image inpainting and semantic segmentation, termed SIS. Our framework adds an image inpainting network and an identical semantic segmentation network in series following an original semantic segmentation network, which can make full use of the two semantic segmentation results to obtain the optimized semantic segmentation results in this scene. Moreover, we combined our framework with Simultaneous Localization and Mapping (SLAM), and conducted experiments on the TUM RGB-D dataset. Experimental results show, the combined SLAM system can construct a semantic octree map with more complete and stable semantic information in dynamic scenes. Semantic segmentation Image inpainting Semantic mapping Dynamic scenes Visual SLAM Liu, Yang aut Guo, Chi aut Zhan, Jiao aut Enthalten in Applied intelligence Springer US, 1991 53(2022), 2 vom: 05. Mai, Seite 2173-2188 (DE-627)130990515 (DE-600)1080229-0 (DE-576)029154286 0924-669X nnns volume:53 year:2022 number:2 day:05 month:05 pages:2173-2188 https://doi.org/10.1007/s10489-022-03487-3 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT AR 53 2022 2 05 05 2173-2188 |
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10.1007/s10489-022-03487-3 doi (DE-627)OLC2080215647 (DE-He213)s10489-022-03487-3-p DE-627 ger DE-627 rakwb eng 004 VZ Zhang, Jianfeng verfasserin aut Optimized segmentation with image inpainting for semantic mapping in dynamic scenes 2022 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022 Abstract Moving objects will obscure static objects in a dynamic scene. When the existing semantic segmentation methods deal with these static objects, there are often missing or errors in segmentation results. To solve this problem, we propose a framework that combines image inpainting and semantic segmentation, termed SIS. Our framework adds an image inpainting network and an identical semantic segmentation network in series following an original semantic segmentation network, which can make full use of the two semantic segmentation results to obtain the optimized semantic segmentation results in this scene. Moreover, we combined our framework with Simultaneous Localization and Mapping (SLAM), and conducted experiments on the TUM RGB-D dataset. Experimental results show, the combined SLAM system can construct a semantic octree map with more complete and stable semantic information in dynamic scenes. Semantic segmentation Image inpainting Semantic mapping Dynamic scenes Visual SLAM Liu, Yang aut Guo, Chi aut Zhan, Jiao aut Enthalten in Applied intelligence Springer US, 1991 53(2022), 2 vom: 05. Mai, Seite 2173-2188 (DE-627)130990515 (DE-600)1080229-0 (DE-576)029154286 0924-669X nnns volume:53 year:2022 number:2 day:05 month:05 pages:2173-2188 https://doi.org/10.1007/s10489-022-03487-3 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT AR 53 2022 2 05 05 2173-2188 |
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10.1007/s10489-022-03487-3 doi (DE-627)OLC2080215647 (DE-He213)s10489-022-03487-3-p DE-627 ger DE-627 rakwb eng 004 VZ Zhang, Jianfeng verfasserin aut Optimized segmentation with image inpainting for semantic mapping in dynamic scenes 2022 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022 Abstract Moving objects will obscure static objects in a dynamic scene. When the existing semantic segmentation methods deal with these static objects, there are often missing or errors in segmentation results. To solve this problem, we propose a framework that combines image inpainting and semantic segmentation, termed SIS. Our framework adds an image inpainting network and an identical semantic segmentation network in series following an original semantic segmentation network, which can make full use of the two semantic segmentation results to obtain the optimized semantic segmentation results in this scene. Moreover, we combined our framework with Simultaneous Localization and Mapping (SLAM), and conducted experiments on the TUM RGB-D dataset. Experimental results show, the combined SLAM system can construct a semantic octree map with more complete and stable semantic information in dynamic scenes. Semantic segmentation Image inpainting Semantic mapping Dynamic scenes Visual SLAM Liu, Yang aut Guo, Chi aut Zhan, Jiao aut Enthalten in Applied intelligence Springer US, 1991 53(2022), 2 vom: 05. Mai, Seite 2173-2188 (DE-627)130990515 (DE-600)1080229-0 (DE-576)029154286 0924-669X nnns volume:53 year:2022 number:2 day:05 month:05 pages:2173-2188 https://doi.org/10.1007/s10489-022-03487-3 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT AR 53 2022 2 05 05 2173-2188 |
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10.1007/s10489-022-03487-3 doi (DE-627)OLC2080215647 (DE-He213)s10489-022-03487-3-p DE-627 ger DE-627 rakwb eng 004 VZ Zhang, Jianfeng verfasserin aut Optimized segmentation with image inpainting for semantic mapping in dynamic scenes 2022 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022 Abstract Moving objects will obscure static objects in a dynamic scene. When the existing semantic segmentation methods deal with these static objects, there are often missing or errors in segmentation results. To solve this problem, we propose a framework that combines image inpainting and semantic segmentation, termed SIS. Our framework adds an image inpainting network and an identical semantic segmentation network in series following an original semantic segmentation network, which can make full use of the two semantic segmentation results to obtain the optimized semantic segmentation results in this scene. Moreover, we combined our framework with Simultaneous Localization and Mapping (SLAM), and conducted experiments on the TUM RGB-D dataset. Experimental results show, the combined SLAM system can construct a semantic octree map with more complete and stable semantic information in dynamic scenes. Semantic segmentation Image inpainting Semantic mapping Dynamic scenes Visual SLAM Liu, Yang aut Guo, Chi aut Zhan, Jiao aut Enthalten in Applied intelligence Springer US, 1991 53(2022), 2 vom: 05. Mai, Seite 2173-2188 (DE-627)130990515 (DE-600)1080229-0 (DE-576)029154286 0924-669X nnns volume:53 year:2022 number:2 day:05 month:05 pages:2173-2188 https://doi.org/10.1007/s10489-022-03487-3 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT AR 53 2022 2 05 05 2173-2188 |
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Abstract Moving objects will obscure static objects in a dynamic scene. When the existing semantic segmentation methods deal with these static objects, there are often missing or errors in segmentation results. To solve this problem, we propose a framework that combines image inpainting and semantic segmentation, termed SIS. Our framework adds an image inpainting network and an identical semantic segmentation network in series following an original semantic segmentation network, which can make full use of the two semantic segmentation results to obtain the optimized semantic segmentation results in this scene. Moreover, we combined our framework with Simultaneous Localization and Mapping (SLAM), and conducted experiments on the TUM RGB-D dataset. Experimental results show, the combined SLAM system can construct a semantic octree map with more complete and stable semantic information in dynamic scenes. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022 |
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Abstract Moving objects will obscure static objects in a dynamic scene. When the existing semantic segmentation methods deal with these static objects, there are often missing or errors in segmentation results. To solve this problem, we propose a framework that combines image inpainting and semantic segmentation, termed SIS. Our framework adds an image inpainting network and an identical semantic segmentation network in series following an original semantic segmentation network, which can make full use of the two semantic segmentation results to obtain the optimized semantic segmentation results in this scene. Moreover, we combined our framework with Simultaneous Localization and Mapping (SLAM), and conducted experiments on the TUM RGB-D dataset. Experimental results show, the combined SLAM system can construct a semantic octree map with more complete and stable semantic information in dynamic scenes. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022 |
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Abstract Moving objects will obscure static objects in a dynamic scene. When the existing semantic segmentation methods deal with these static objects, there are often missing or errors in segmentation results. To solve this problem, we propose a framework that combines image inpainting and semantic segmentation, termed SIS. Our framework adds an image inpainting network and an identical semantic segmentation network in series following an original semantic segmentation network, which can make full use of the two semantic segmentation results to obtain the optimized semantic segmentation results in this scene. Moreover, we combined our framework with Simultaneous Localization and Mapping (SLAM), and conducted experiments on the TUM RGB-D dataset. Experimental results show, the combined SLAM system can construct a semantic octree map with more complete and stable semantic information in dynamic scenes. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022 |
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<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">OLC2080215647</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230506143734.0</controlfield><controlfield tag="007">tu</controlfield><controlfield tag="008">230131s2022 xx ||||| 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s10489-022-03487-3</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)OLC2080215647</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-He213)s10489-022-03487-3-p</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">004</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Zhang, Jianfeng</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Optimized segmentation with image inpainting for semantic mapping in dynamic scenes</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2022</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">ohne Hilfsmittel zu benutzen</subfield><subfield code="b">n</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Band</subfield><subfield code="b">nc</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">© The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract Moving objects will obscure static objects in a dynamic scene. When the existing semantic segmentation methods deal with these static objects, there are often missing or errors in segmentation results. To solve this problem, we propose a framework that combines image inpainting and semantic segmentation, termed SIS. Our framework adds an image inpainting network and an identical semantic segmentation network in series following an original semantic segmentation network, which can make full use of the two semantic segmentation results to obtain the optimized semantic segmentation results in this scene. Moreover, we combined our framework with Simultaneous Localization and Mapping (SLAM), and conducted experiments on the TUM RGB-D dataset. 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Mai, Seite 2173-2188</subfield><subfield code="w">(DE-627)130990515</subfield><subfield code="w">(DE-600)1080229-0</subfield><subfield code="w">(DE-576)029154286</subfield><subfield code="x">0924-669X</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:53</subfield><subfield code="g">year:2022</subfield><subfield code="g">number:2</subfield><subfield code="g">day:05</subfield><subfield code="g">month:05</subfield><subfield code="g">pages:2173-2188</subfield></datafield><datafield tag="856" ind1="4" ind2="1"><subfield code="u">https://doi.org/10.1007/s10489-022-03487-3</subfield><subfield code="z">lizenzpflichtig</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_OLC</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-MAT</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">53</subfield><subfield code="j">2022</subfield><subfield code="e">2</subfield><subfield code="b">05</subfield><subfield code="c">05</subfield><subfield code="h">2173-2188</subfield></datafield></record></collection>
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