CameraVQ: Vector‐Quantized Representations for Monocular Camera Calibration.
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| Title: | CameraVQ: Vector‐Quantized Representations for Monocular Camera Calibration. |
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| Authors: | Cheong, DaEun1 (AUTHOR), Han, JungHyun1 (AUTHOR) jhan@korea.ac.kr |
| Source: | Computer Animation & Virtual Worlds. May/Jun2026, Vol. 37 Issue 3, p1-9. 9p. |
| Subjects: | Camera calibration, Vector quantization, Machine learning, Computer vision, Image processing, Calibration |
| Abstract: | We present a monocular camera calibration method, dubbed CameraVQ. It reformulates monocular camera calibration as classification over vector‐quantized camera intrinsics. Existing methods based on geometric cues or direct regression often suffer from unstable optimization and poor generalization. In contrast, CameraVQ learns a discrete codebook of camera intrinsics and predicts the latent code from a single image. This discrete formulation constrains predictions to a statistically learned manifold of valid configurations, enabling robust calibration and strong generalization. Through extensive evaluation on diverse calibration benchmarks, CameraVQ achieves state‐of‐the‐art performance on the majority of datasets. [ABSTRACT FROM AUTHOR] |
| Copyright of Computer Animation & Virtual Worlds is the property of Wiley-Blackwell 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 |
| FullText | Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 194920604 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: CameraVQ: Vector‐Quantized Representations for Monocular Camera Calibration. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Cheong%2C+DaEun%22">Cheong, DaEun</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Han%2C+JungHyun%22">Han, JungHyun</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> jhan@korea.ac.kr</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Computer+Animation+%26+Virtual+Worlds%22">Computer Animation & Virtual Worlds</searchLink>. May/Jun2026, Vol. 37 Issue 3, p1-9. 9p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Camera+calibration%22">Camera calibration</searchLink><br /><searchLink fieldCode="DE" term="%22Vector+quantization%22">Vector quantization</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Computer+vision%22">Computer vision</searchLink><br /><searchLink fieldCode="DE" term="%22Image+processing%22">Image processing</searchLink><br /><searchLink fieldCode="DE" term="%22Calibration%22">Calibration</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: We present a monocular camera calibration method, dubbed CameraVQ. It reformulates monocular camera calibration as classification over vector‐quantized camera intrinsics. Existing methods based on geometric cues or direct regression often suffer from unstable optimization and poor generalization. In contrast, CameraVQ learns a discrete codebook of camera intrinsics and predicts the latent code from a single image. This discrete formulation constrains predictions to a statistically learned manifold of valid configurations, enabling robust calibration and strong generalization. Through extensive evaluation on diverse calibration benchmarks, CameraVQ achieves state‐of‐the‐art performance on the majority of datasets. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Computer Animation & Virtual Worlds is the property of Wiley-Blackwell 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=194920604 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1002/cav.70144 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 9 StartPage: 1 Subjects: – SubjectFull: Camera calibration Type: general – SubjectFull: Vector quantization Type: general – SubjectFull: Machine learning Type: general – SubjectFull: Computer vision Type: general – SubjectFull: Image processing Type: general – SubjectFull: Calibration Type: general Titles: – TitleFull: CameraVQ: Vector‐Quantized Representations for Monocular Camera Calibration. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Cheong, DaEun – PersonEntity: Name: NameFull: Han, JungHyun IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 05 Text: May/Jun2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 15464261 Numbering: – Type: volume Value: 37 – Type: issue Value: 3 Titles: – TitleFull: Computer Animation & Virtual Worlds Type: main |
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