Academic Journal
Convergent binocular stereo: Depth perception for humanoid robot vision.
| Τίτλος: | Convergent binocular stereo: Depth perception for humanoid robot vision. |
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| Συγγραφείς: | Chi, Mingshi, Tsotsos, John K. |
| Πηγή: | Science Robotics; 8/12/2026, Vol. 11 Issue 117, p1-14, 14p |
| Θεματικοί όροι: | Binocular vision, Depth perception, Stereopsis, Stereo image processing, Robot vision |
| Περίληψη: | The design of robotic binocular camera systems has been inspired by human vision, as has their use in humanoid robots. One aspect of this inspiration has yet to play a major role, namely, that humans determine depth using a convergent binocular imaging geometry with both eyes pointing at the same location. Although some robot heads have the functionality to use vergence and version movements and thus alter binocular imaging geometry, a method to exploit it for depth computation has not been explored. Even though to an observer, their motions and appearance might resemble that of humans, in detail, the resemblance to the motions required for actual convergent depth computation is not seen. To bridge this gap, we present convergent binocular stereo (CBS), a stereo algorithm designed to provide a foundation for purposeful binocular computations under the humanoid constraint, intended for active binocular robots. CBS computes horizontal and vertical disparities using a coarse-to-fine refinement strategy with Gabor-filtered responses. We also introduce the Convergent Binocular Stereo–BenchMark (CBS-BM), a convergent, natural image dataset containing 49 scenes with ground-truth horizontal disparity collected on a four-degrees-of-freedom robotic system. Our evaluation, a quantitative comparison between parallel and convergent stereo systems, shows that CBS is broadly competitive with state-of-the-art parallel methods, even outperforming them in scenes with repeated patterns and in mean horizontal disparity and depth error over all scenes. Although not intended to replace parallel stereo where humanlike behavior is unnecessary, CBS enables functionally realistic depth computation for humanoid robotic heads. [ABSTRACT FROM AUTHOR] |
| Copyright of Science Robotics is the property of American Association for the Advancement of Science 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.) | |
| Βάση Δεδομένων: | Complementary Index |
| FullText | Text: Availability: 0 |
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| Header | DbId: edb DbLabel: Complementary Index An: 197075316 RelevancyScore: 1162 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 1162.42492675781 |
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| Items | – Name: Title Label: Title Group: Ti Data: Convergent binocular stereo: Depth perception for humanoid robot vision. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Chi%2C+Mingshi%22">Chi, Mingshi</searchLink><br /><searchLink fieldCode="AR" term="%22Tsotsos%2C+John+K%2E%22">Tsotsos, John K.</searchLink> – Name: TitleSource Label: Source Group: Src Data: Science Robotics; 8/12/2026, Vol. 11 Issue 117, p1-14, 14p – Name: Subject Label: Subject Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Binocular+vision%22">Binocular vision</searchLink><br /><searchLink fieldCode="DE" term="%22Depth+perception%22">Depth perception</searchLink><br /><searchLink fieldCode="DE" term="%22Stereopsis%22">Stereopsis</searchLink><br /><searchLink fieldCode="DE" term="%22Stereo+image+processing%22">Stereo image processing</searchLink><br /><searchLink fieldCode="DE" term="%22Robot+vision%22">Robot vision</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: The design of robotic binocular camera systems has been inspired by human vision, as has their use in humanoid robots. One aspect of this inspiration has yet to play a major role, namely, that humans determine depth using a convergent binocular imaging geometry with both eyes pointing at the same location. Although some robot heads have the functionality to use vergence and version movements and thus alter binocular imaging geometry, a method to exploit it for depth computation has not been explored. Even though to an observer, their motions and appearance might resemble that of humans, in detail, the resemblance to the motions required for actual convergent depth computation is not seen. To bridge this gap, we present convergent binocular stereo (CBS), a stereo algorithm designed to provide a foundation for purposeful binocular computations under the humanoid constraint, intended for active binocular robots. CBS computes horizontal and vertical disparities using a coarse-to-fine refinement strategy with Gabor-filtered responses. We also introduce the Convergent Binocular Stereo–BenchMark (CBS-BM), a convergent, natural image dataset containing 49 scenes with ground-truth horizontal disparity collected on a four-degrees-of-freedom robotic system. Our evaluation, a quantitative comparison between parallel and convergent stereo systems, shows that CBS is broadly competitive with state-of-the-art parallel methods, even outperforming them in scenes with repeated patterns and in mean horizontal disparity and depth error over all scenes. Although not intended to replace parallel stereo where humanlike behavior is unnecessary, CBS enables functionally realistic depth computation for humanoid robotic heads. [ABSTRACT FROM AUTHOR] – Name: Abstract Label: Group: Ab Data: <i>Copyright of Science Robotics is the property of American Association for the Advancement of Science 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=edb&AN=197075316 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1126/scirobotics.aec7205 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 14 StartPage: 1 Subjects: – SubjectFull: Binocular vision Type: general – SubjectFull: Depth perception Type: general – SubjectFull: Stereopsis Type: general – SubjectFull: Stereo image processing Type: general – SubjectFull: Robot vision Type: general Titles: – TitleFull: Convergent binocular stereo: Depth perception for humanoid robot vision. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Chi, Mingshi – PersonEntity: Name: NameFull: Tsotsos, John K. IsPartOfRelationships: – BibEntity: Dates: – D: 12 M: 08 Text: 8/12/2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 24709476 Numbering: – Type: volume Value: 11 – Type: issue Value: 117 Titles: – TitleFull: Science Robotics Type: main |
| ResultId | 1 |