View in EDS HTML Full Text PDF Full Text

Monocular RGB 6D object pose estimation for augmented reality: a survey.

Saved in:
Bibliographic Details
Title: Monocular RGB 6D object pose estimation for augmented reality: a survey.
Authors: Aguirrezabal, Pablo1,2 (AUTHOR) pablo.aguirrezabal@tecnalia.com, Aguinaga, Iker2,3 (AUTHOR) iaguinaga@ceit.es, Alvarez-Gila, Aitor4 (AUTHOR) aitor.alvarez@tecnalia.com
Source: Virtual Reality. Jun2026, Vol. 30 Issue 2, p1-26. 26p.
Subjects: Pose estimation (Computer vision), Augmented reality, Deep learning
Abstract: Accurate determination of the 6D pose of an object is important in Augmented Reality (AR) to align and anchor virtual elements within the real world. Achieving seamless and proper alignment of virtual objects within RGB image sequences enables AR to provide spatial value. For this purpose, the estimation of the 6D pose is one of the most relevant techniques, yet it remains a significant challenge. While there has been significant research in the field of 6D object estimation from RGB images, many challenges remain unresolved. Our analysis offers a thorough examination of modern methods based on deep learning due to their ability to deliver state-of-the-art results in 6D pose estimation, while addressing challenges such as changes in lighting, occlusions, background clutter and other environmental factors. We also consider the standard datasets and metrics to compare performance, perform a qualitative analysis from different perspectives and summarize the main technical challenges and trends in this topic, always from an application-oriented perspective in the context of AR. [ABSTRACT FROM AUTHOR]
Copyright of Virtual Reality is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
Full text is not displayed to guests.
FullText Links:
  – Type: pdflink
Text:
  Availability: 1
Header DbId: egs
DbLabel: Engineering Source
An: 191658285
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
IllustrationInfo
Items – Name: Title
  Label: Title
  Group: Ti
  Data: Monocular RGB 6D object pose estimation for augmented reality: a survey.
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22Aguirrezabal%2C+Pablo%22">Aguirrezabal, Pablo</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<i> pablo.aguirrezabal@tecnalia.com</i><br /><searchLink fieldCode="AR" term="%22Aguinaga%2C+Iker%22">Aguinaga, Iker</searchLink><relatesTo>2,3</relatesTo> (AUTHOR)<i> iaguinaga@ceit.es</i><br /><searchLink fieldCode="AR" term="%22Alvarez-Gila%2C+Aitor%22">Alvarez-Gila, Aitor</searchLink><relatesTo>4</relatesTo> (AUTHOR)<i> aitor.alvarez@tecnalia.com</i>
– Name: TitleSource
  Label: Source
  Group: Src
  Data: <searchLink fieldCode="JN" term="%22Virtual+Reality%22">Virtual Reality</searchLink>. Jun2026, Vol. 30 Issue 2, p1-26. 26p.
– Name: Subject
  Label: Subjects
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22Pose+estimation+%28Computer+vision%29%22">Pose estimation (Computer vision)</searchLink><br /><searchLink fieldCode="DE" term="%22Augmented+reality%22">Augmented reality</searchLink><br /><searchLink fieldCode="DE" term="%22Deep+learning%22">Deep learning</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Accurate determination of the 6D pose of an object is important in Augmented Reality (AR) to align and anchor virtual elements within the real world. Achieving seamless and proper alignment of virtual objects within RGB image sequences enables AR to provide spatial value. For this purpose, the estimation of the 6D pose is one of the most relevant techniques, yet it remains a significant challenge. While there has been significant research in the field of 6D object estimation from RGB images, many challenges remain unresolved. Our analysis offers a thorough examination of modern methods based on deep learning due to their ability to deliver state-of-the-art results in 6D pose estimation, while addressing challenges such as changes in lighting, occlusions, background clutter and other environmental factors. We also consider the standard datasets and metrics to compare performance, perform a qualitative analysis from different perspectives and summarize the main technical challenges and trends in this topic, always from an application-oriented perspective in the context of AR. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Virtual Reality is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=egs&AN=191658285
RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.1007/s10055-026-01315-4
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 26
        StartPage: 1
    Subjects:
      – SubjectFull: Pose estimation (Computer vision)
        Type: general
      – SubjectFull: Augmented reality
        Type: general
      – SubjectFull: Deep learning
        Type: general
    Titles:
      – TitleFull: Monocular RGB 6D object pose estimation for augmented reality: a survey.
        Type: main
  BibRelationships:
    HasContributorRelationships:
      – PersonEntity:
          Name:
            NameFull: Aguirrezabal, Pablo
      – PersonEntity:
          Name:
            NameFull: Aguinaga, Iker
      – PersonEntity:
          Name:
            NameFull: Alvarez-Gila, Aitor
    IsPartOfRelationships:
      – BibEntity:
          Dates:
            – D: 01
              M: 06
              Text: Jun2026
              Type: published
              Y: 2026
          Identifiers:
            – Type: issn-print
              Value: 13594338
          Numbering:
            – Type: volume
              Value: 30
            – Type: issue
              Value: 2
          Titles:
            – TitleFull: Virtual Reality
              Type: main
ResultId 1