Automatic Registration of Panoramic Image and Point Cloud Based on the Shape of the Overall Ground Object
This paper presents a novel method for registering panoramic images and 3D point clouds using the shape of the overall ground object in the scene as registration primitives. Firstly, a semantic segmentation method is applied to the panoramic image to extract the ground object and remove the sky. Nex...
Gespeichert in:
| Veröffentlicht in: | IEEE access 2023-01, Vol.11, p.1-1 |
|---|---|
| Hauptverfasser: | , , , , , |
| Format: | Artikel |
| Sprache: | eng |
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | 1 |
|---|---|
| container_issue | |
| container_start_page | 1 |
| container_title | IEEE access |
| container_volume | 11 |
| creator | Wang, Buyun Li, Hongwei Zhao, Shan He, Linqing Qin, Yulu Yang, Xiaoyue |
| description | This paper presents a novel method for registering panoramic images and 3D point clouds using the shape of the overall ground object in the scene as registration primitives. Firstly, a semantic segmentation method is applied to the panoramic image to extract the ground object and remove the sky. Next, the cloth simulation filtering algorithm (CSF) is employed to eliminate the ground points in the 3D point cloud. The remaining 3D ground objects are then projected onto a two-dimensional plane using the imaging model of the panoramic camera to obtain the registration primitives. Finally, we adopt the whale algorithm to perform a coarse-to-fine registration, utilizing overlap degree and mutual information as the similarity measures. The proposed method is evaluated in four different scenes and compared with the other four registration methods. The results demonstrate that the proposed method is accurate and effective, with an average registration error of 11.48 pixels (image resolution is 11000x5500 pixels) compared to the EOPs of the system of 101.67 pixels. |
| doi_str_mv | 10.1109/ACCESS.2023.3260847 |
| format | Article |
| fullrecord | <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_crossref_primary_10_1109_ACCESS_2023_3260847</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><ieee_id>10078851</ieee_id><doaj_id>oai_doaj_org_article_a241db56917f4ba8b6a8c8a0c6c93b6c</doaj_id><sourcerecordid>2793211751</sourcerecordid><originalsourceid>FETCH-LOGICAL-c359t-c9b02fb6bd50af78247fd33f9c805c7eb3f235fe383398668496e36fb09458de3</originalsourceid><addsrcrecordid>eNpNkV1r2zAUhs3YYKXtL9guBLtOpg_r6zIzXRcopCzbtTiSj1IHx8pke9B_P2UupbrROUfv80rirapPjK4Zo_brpmnu9vs1p1ysBVfU1PpddcWZsishhXr_pv5Y3Y7jkZZlykjqq6rbzFM6wdQF8hMP3TjlUqeBpEgeYUgZTuVke4IDEhha8pi6YSJNn-aWfIMRW1K00xOS_ROc8UJdmt1fzND35D6nuUA7f8Qw3VQfIvQj3r7s19Xv73e_mh-rh939ttk8rIKQdloF6ymPXvlWUoja8FrHVohog6EyaPQiciEjCiOENUqZ2ioUKnpqa2laFNfVdvFtExzdOXcnyM8uQef-D1I-OMjlvz064DVrvVSW6Vh7MF6BCQZoUMEKr0Lx-rJ4nXP6M-M4uWOa81Ce77i2gjOmJSsqsahCTuOYMb7eyqi7ROSWiNwlIvcSUaE-L1SHiG8Iqo0ppv8AKMeMYA</addsrcrecordid><sourcetype>Open Website</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2793211751</pqid></control><display><type>article</type><title>Automatic Registration of Panoramic Image and Point Cloud Based on the Shape of the Overall Ground Object</title><source>IEEE Open Access Journals</source><source>DOAJ Directory of Open Access Journals</source><source>EZB-FREE-00999 freely available EZB journals</source><creator>Wang, Buyun ; Li, Hongwei ; Zhao, Shan ; He, Linqing ; Qin, Yulu ; Yang, Xiaoyue</creator><creatorcontrib>Wang, Buyun ; Li, Hongwei ; Zhao, Shan ; He, Linqing ; Qin, Yulu ; Yang, Xiaoyue</creatorcontrib><description>This paper presents a novel method for registering panoramic images and 3D point clouds using the shape of the overall ground object in the scene as registration primitives. Firstly, a semantic segmentation method is applied to the panoramic image to extract the ground object and remove the sky. Next, the cloth simulation filtering algorithm (CSF) is employed to eliminate the ground points in the 3D point cloud. The remaining 3D ground objects are then projected onto a two-dimensional plane using the imaging model of the panoramic camera to obtain the registration primitives. Finally, we adopt the whale algorithm to perform a coarse-to-fine registration, utilizing overlap degree and mutual information as the similarity measures. The proposed method is evaluated in four different scenes and compared with the other four registration methods. The results demonstrate that the proposed method is accurate and effective, with an average registration error of 11.48 pixels (image resolution is 11000x5500 pixels) compared to the EOPs of the system of 101.67 pixels.</description><identifier>ISSN: 2169-3536</identifier><identifier>EISSN: 2169-3536</identifier><identifier>DOI: 10.1109/ACCESS.2023.3260847</identifier><identifier>CODEN: IAECCG</identifier><language>eng</language><publisher>Piscataway: IEEE</publisher><subject>Algorithms ; Cameras ; Feature extraction ; Image resolution ; Image segmentation ; Laser radar ; Optical imaging ; Panoramic cameras ; Panoramic image ; Pixels ; Point cloud ; Point cloud compression ; Registration ; Semantic segmentation ; Semantics ; Three dimensional models ; Three-dimensional displays</subject><ispartof>IEEE access, 2023-01, Vol.11, p.1-1</ispartof><rights>Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2023</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><cites>FETCH-LOGICAL-c359t-c9b02fb6bd50af78247fd33f9c805c7eb3f235fe383398668496e36fb09458de3</cites><orcidid>0000-0003-4568-0057 ; 0009-0007-0492-5456 ; 0009-0001-2768-3054 ; 0009-0001-2518-1704 ; 0009-0001-1682-5934</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/10078851$$EHTML$$P50$$Gieee$$Hfree_for_read</linktohtml><link.rule.ids>314,776,780,860,2096,27610,27901,27902,54908</link.rule.ids></links><search><creatorcontrib>Wang, Buyun</creatorcontrib><creatorcontrib>Li, Hongwei</creatorcontrib><creatorcontrib>Zhao, Shan</creatorcontrib><creatorcontrib>He, Linqing</creatorcontrib><creatorcontrib>Qin, Yulu</creatorcontrib><creatorcontrib>Yang, Xiaoyue</creatorcontrib><title>Automatic Registration of Panoramic Image and Point Cloud Based on the Shape of the Overall Ground Object</title><title>IEEE access</title><addtitle>Access</addtitle><description>This paper presents a novel method for registering panoramic images and 3D point clouds using the shape of the overall ground object in the scene as registration primitives. Firstly, a semantic segmentation method is applied to the panoramic image to extract the ground object and remove the sky. Next, the cloth simulation filtering algorithm (CSF) is employed to eliminate the ground points in the 3D point cloud. The remaining 3D ground objects are then projected onto a two-dimensional plane using the imaging model of the panoramic camera to obtain the registration primitives. Finally, we adopt the whale algorithm to perform a coarse-to-fine registration, utilizing overlap degree and mutual information as the similarity measures. The proposed method is evaluated in four different scenes and compared with the other four registration methods. The results demonstrate that the proposed method is accurate and effective, with an average registration error of 11.48 pixels (image resolution is 11000x5500 pixels) compared to the EOPs of the system of 101.67 pixels.</description><subject>Algorithms</subject><subject>Cameras</subject><subject>Feature extraction</subject><subject>Image resolution</subject><subject>Image segmentation</subject><subject>Laser radar</subject><subject>Optical imaging</subject><subject>Panoramic cameras</subject><subject>Panoramic image</subject><subject>Pixels</subject><subject>Point cloud</subject><subject>Point cloud compression</subject><subject>Registration</subject><subject>Semantic segmentation</subject><subject>Semantics</subject><subject>Three dimensional models</subject><subject>Three-dimensional displays</subject><issn>2169-3536</issn><issn>2169-3536</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2023</creationdate><recordtype>article</recordtype><sourceid>ESBDL</sourceid><sourceid>RIE</sourceid><sourceid>DOA</sourceid><recordid>eNpNkV1r2zAUhs3YYKXtL9guBLtOpg_r6zIzXRcopCzbtTiSj1IHx8pke9B_P2UupbrROUfv80rirapPjK4Zo_brpmnu9vs1p1ysBVfU1PpddcWZsishhXr_pv5Y3Y7jkZZlykjqq6rbzFM6wdQF8hMP3TjlUqeBpEgeYUgZTuVke4IDEhha8pi6YSJNn-aWfIMRW1K00xOS_ROc8UJdmt1fzND35D6nuUA7f8Qw3VQfIvQj3r7s19Xv73e_mh-rh939ttk8rIKQdloF6ymPXvlWUoja8FrHVohog6EyaPQiciEjCiOENUqZ2ioUKnpqa2laFNfVdvFtExzdOXcnyM8uQef-D1I-OMjlvz064DVrvVSW6Vh7MF6BCQZoUMEKr0Lx-rJ4nXP6M-M4uWOa81Ce77i2gjOmJSsqsahCTuOYMb7eyqi7ROSWiNwlIvcSUaE-L1SHiG8Iqo0ppv8AKMeMYA</recordid><startdate>20230101</startdate><enddate>20230101</enddate><creator>Wang, Buyun</creator><creator>Li, Hongwei</creator><creator>Zhao, Shan</creator><creator>He, Linqing</creator><creator>Qin, Yulu</creator><creator>Yang, Xiaoyue</creator><general>IEEE</general><general>The Institute of Electrical and Electronics Engineers, Inc. (IEEE)</general><scope>97E</scope><scope>ESBDL</scope><scope>RIA</scope><scope>RIE</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7SC</scope><scope>7SP</scope><scope>7SR</scope><scope>8BQ</scope><scope>8FD</scope><scope>JG9</scope><scope>JQ2</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope><scope>DOA</scope><orcidid>https://orcid.org/0000-0003-4568-0057</orcidid><orcidid>https://orcid.org/0009-0007-0492-5456</orcidid><orcidid>https://orcid.org/0009-0001-2768-3054</orcidid><orcidid>https://orcid.org/0009-0001-2518-1704</orcidid><orcidid>https://orcid.org/0009-0001-1682-5934</orcidid></search><sort><creationdate>20230101</creationdate><title>Automatic Registration of Panoramic Image and Point Cloud Based on the Shape of the Overall Ground Object</title><author>Wang, Buyun ; Li, Hongwei ; Zhao, Shan ; He, Linqing ; Qin, Yulu ; Yang, Xiaoyue</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c359t-c9b02fb6bd50af78247fd33f9c805c7eb3f235fe383398668496e36fb09458de3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2023</creationdate><topic>Algorithms</topic><topic>Cameras</topic><topic>Feature extraction</topic><topic>Image resolution</topic><topic>Image segmentation</topic><topic>Laser radar</topic><topic>Optical imaging</topic><topic>Panoramic cameras</topic><topic>Panoramic image</topic><topic>Pixels</topic><topic>Point cloud</topic><topic>Point cloud compression</topic><topic>Registration</topic><topic>Semantic segmentation</topic><topic>Semantics</topic><topic>Three dimensional models</topic><topic>Three-dimensional displays</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Wang, Buyun</creatorcontrib><creatorcontrib>Li, Hongwei</creatorcontrib><creatorcontrib>Zhao, Shan</creatorcontrib><creatorcontrib>He, Linqing</creatorcontrib><creatorcontrib>Qin, Yulu</creatorcontrib><creatorcontrib>Yang, Xiaoyue</creatorcontrib><collection>IEEE All-Society Periodicals Package (ASPP) 2005-present</collection><collection>IEEE Open Access Journals</collection><collection>IEEE All-Society Periodicals Package (ASPP) 1998-Present</collection><collection>IEEE Electronic Library (IEL)</collection><collection>CrossRef</collection><collection>Computer and Information Systems Abstracts</collection><collection>Electronics & Communications Abstracts</collection><collection>Engineered Materials Abstracts</collection><collection>METADEX</collection><collection>Technology Research Database</collection><collection>Materials Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><collection>DOAJ Directory of Open Access Journals</collection><jtitle>IEEE access</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Wang, Buyun</au><au>Li, Hongwei</au><au>Zhao, Shan</au><au>He, Linqing</au><au>Qin, Yulu</au><au>Yang, Xiaoyue</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Automatic Registration of Panoramic Image and Point Cloud Based on the Shape of the Overall Ground Object</atitle><jtitle>IEEE access</jtitle><stitle>Access</stitle><date>2023-01-01</date><risdate>2023</risdate><volume>11</volume><spage>1</spage><epage>1</epage><pages>1-1</pages><issn>2169-3536</issn><eissn>2169-3536</eissn><coden>IAECCG</coden><abstract>This paper presents a novel method for registering panoramic images and 3D point clouds using the shape of the overall ground object in the scene as registration primitives. Firstly, a semantic segmentation method is applied to the panoramic image to extract the ground object and remove the sky. Next, the cloth simulation filtering algorithm (CSF) is employed to eliminate the ground points in the 3D point cloud. The remaining 3D ground objects are then projected onto a two-dimensional plane using the imaging model of the panoramic camera to obtain the registration primitives. Finally, we adopt the whale algorithm to perform a coarse-to-fine registration, utilizing overlap degree and mutual information as the similarity measures. The proposed method is evaluated in four different scenes and compared with the other four registration methods. The results demonstrate that the proposed method is accurate and effective, with an average registration error of 11.48 pixels (image resolution is 11000x5500 pixels) compared to the EOPs of the system of 101.67 pixels.</abstract><cop>Piscataway</cop><pub>IEEE</pub><doi>10.1109/ACCESS.2023.3260847</doi><tpages>1</tpages><orcidid>https://orcid.org/0000-0003-4568-0057</orcidid><orcidid>https://orcid.org/0009-0007-0492-5456</orcidid><orcidid>https://orcid.org/0009-0001-2768-3054</orcidid><orcidid>https://orcid.org/0009-0001-2518-1704</orcidid><orcidid>https://orcid.org/0009-0001-1682-5934</orcidid><oa>free_for_read</oa></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 2169-3536 |
| ispartof | IEEE access, 2023-01, Vol.11, p.1-1 |
| issn | 2169-3536 2169-3536 |
| language | eng |
| recordid | cdi_crossref_primary_10_1109_ACCESS_2023_3260847 |
| source | IEEE Open Access Journals; DOAJ Directory of Open Access Journals; EZB-FREE-00999 freely available EZB journals |
| subjects | Algorithms Cameras Feature extraction Image resolution Image segmentation Laser radar Optical imaging Panoramic cameras Panoramic image Pixels Point cloud Point cloud compression Registration Semantic segmentation Semantics Three dimensional models Three-dimensional displays |
| title | Automatic Registration of Panoramic Image and Point Cloud Based on the Shape of the Overall Ground Object |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-02-10T10%3A58%3A33IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_cross&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Automatic%20Registration%20of%20Panoramic%20Image%20and%20Point%20Cloud%20Based%20on%20the%20Shape%20of%20the%20Overall%20Ground%20Object&rft.jtitle=IEEE%20access&rft.au=Wang,%20Buyun&rft.date=2023-01-01&rft.volume=11&rft.spage=1&rft.epage=1&rft.pages=1-1&rft.issn=2169-3536&rft.eissn=2169-3536&rft.coden=IAECCG&rft_id=info:doi/10.1109/ACCESS.2023.3260847&rft_dat=%3Cproquest_cross%3E2793211751%3C/proquest_cross%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2793211751&rft_id=info:pmid/&rft_ieee_id=10078851&rft_doaj_id=oai_doaj_org_article_a241db56917f4ba8b6a8c8a0c6c93b6c&rfr_iscdi=true |