Computer vision algorithms for 3D object recognition and orientation: a bibliometric study
Autor(a) principal: | |
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Data de Publicação: | 2023 |
Outros Autores: | , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | http://hdl.handle.net/10198/29000 |
Resumo: | This paper consists of a bibliometric study that covers the topic of 3D object detection from 2022 until the present day. It employs various analysis approaches that shed light on the leading authors, affiliations, and countries within this research domain alongside the main themes of interest related to it. The findings revealed that China is the leading country in this domain given the fact that it is responsible for most of the scientific literature as well as being a host for the most productive universities and authors in terms of the number of publications. China is also responsible for initiating a significant number of collaborations with various nations around the world. The most basic theme related to this field is deep learning, along with autonomous driving, point cloud, robotics, and LiDAR. The work also includes an in-depth review that underlines some of the latest frameworks that took on various challenges regarding this topic, the improvement of object detection from point clouds, and training end-to-end fusion methods using both camera and LiDAR sensors, to name a few. |
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Computer vision algorithms for 3D object recognition and orientation: a bibliometric study3D objectObject detectionObject orientationBibliometric analysisThis paper consists of a bibliometric study that covers the topic of 3D object detection from 2022 until the present day. It employs various analysis approaches that shed light on the leading authors, affiliations, and countries within this research domain alongside the main themes of interest related to it. The findings revealed that China is the leading country in this domain given the fact that it is responsible for most of the scientific literature as well as being a host for the most productive universities and authors in terms of the number of publications. China is also responsible for initiating a significant number of collaborations with various nations around the world. The most basic theme related to this field is deep learning, along with autonomous driving, point cloud, robotics, and LiDAR. The work also includes an in-depth review that underlines some of the latest frameworks that took on various challenges regarding this topic, the improvement of object detection from point clouds, and training end-to-end fusion methods using both camera and LiDAR sensors, to name a few.This research was funded by the Foundation for Science and Technology (FCT, Portugal) for financial support through national funds FCT/MCTES (PIDDAC) to CeDRI (UIDB/05757/2020 and UIDP/05757/2020) and SusTEC (LA/P/0007/2021).MDPIBiblioteca Digital do IPBYahia, YoussefLopes, Júlio CastroLopes, Rui Pedro2023-12-20T16:36:20Z20232023-01-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10198/29000engYahia, Youssef; Lopes, Júlio Castro; Lopes, Rui Pedro (2023). Computer vision algorithms for 3D object recognition and orientation: a bibliometric study. Electronics. eISSN 2079-9292. 12:20, p. 1-1610.3390/electronics122042182079-9292info:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2023-12-27T01:17:03Zoai:bibliotecadigital.ipb.pt:10198/29000Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T00:56:12.638517Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse |
dc.title.none.fl_str_mv |
Computer vision algorithms for 3D object recognition and orientation: a bibliometric study |
title |
Computer vision algorithms for 3D object recognition and orientation: a bibliometric study |
spellingShingle |
Computer vision algorithms for 3D object recognition and orientation: a bibliometric study Yahia, Youssef 3D object Object detection Object orientation Bibliometric analysis |
title_short |
Computer vision algorithms for 3D object recognition and orientation: a bibliometric study |
title_full |
Computer vision algorithms for 3D object recognition and orientation: a bibliometric study |
title_fullStr |
Computer vision algorithms for 3D object recognition and orientation: a bibliometric study |
title_full_unstemmed |
Computer vision algorithms for 3D object recognition and orientation: a bibliometric study |
title_sort |
Computer vision algorithms for 3D object recognition and orientation: a bibliometric study |
author |
Yahia, Youssef |
author_facet |
Yahia, Youssef Lopes, Júlio Castro Lopes, Rui Pedro |
author_role |
author |
author2 |
Lopes, Júlio Castro Lopes, Rui Pedro |
author2_role |
author author |
dc.contributor.none.fl_str_mv |
Biblioteca Digital do IPB |
dc.contributor.author.fl_str_mv |
Yahia, Youssef Lopes, Júlio Castro Lopes, Rui Pedro |
dc.subject.por.fl_str_mv |
3D object Object detection Object orientation Bibliometric analysis |
topic |
3D object Object detection Object orientation Bibliometric analysis |
description |
This paper consists of a bibliometric study that covers the topic of 3D object detection from 2022 until the present day. It employs various analysis approaches that shed light on the leading authors, affiliations, and countries within this research domain alongside the main themes of interest related to it. The findings revealed that China is the leading country in this domain given the fact that it is responsible for most of the scientific literature as well as being a host for the most productive universities and authors in terms of the number of publications. China is also responsible for initiating a significant number of collaborations with various nations around the world. The most basic theme related to this field is deep learning, along with autonomous driving, point cloud, robotics, and LiDAR. The work also includes an in-depth review that underlines some of the latest frameworks that took on various challenges regarding this topic, the improvement of object detection from point clouds, and training end-to-end fusion methods using both camera and LiDAR sensors, to name a few. |
publishDate |
2023 |
dc.date.none.fl_str_mv |
2023-12-20T16:36:20Z 2023 2023-01-01T00:00:00Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://hdl.handle.net/10198/29000 |
url |
http://hdl.handle.net/10198/29000 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Yahia, Youssef; Lopes, Júlio Castro; Lopes, Rui Pedro (2023). Computer vision algorithms for 3D object recognition and orientation: a bibliometric study. Electronics. eISSN 2079-9292. 12:20, p. 1-16 10.3390/electronics12204218 2079-9292 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.publisher.none.fl_str_mv |
MDPI |
publisher.none.fl_str_mv |
MDPI |
dc.source.none.fl_str_mv |
reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação instacron:RCAAP |
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Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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RCAAP |
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RCAAP |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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1799136447572213760 |