Real-time lightweight self-supervised monocular depth estimation.
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| Title: | Real-time lightweight self-supervised monocular depth estimation. |
|---|---|
| Authors: | YANG, Tianxiang1, MENG, Lingjun1 menglingjun@nuc.edu.cn, JIN, Hong2, FENG, Wenjie1, LIU, Xinhao3 |
| Source: | Journal of Measurement Science & Instrumentation. Jun2026, Vol. 17 Issue 2, p278-296. 19p. |
| Subjects: | Depth maps (Digital image processing), Real-time computing, Artificial neural networks, Machine learning, Computer vision, Edge computing |
| Abstract: | Monocular depth estimation aims to predict depth information within a scene from a single RGB image, but many models remain computationally intensive for real-time inference on resource-constrained edge devices. This paper presents a lightweight self-supervised monocular depth estimation network that balances accuracy and efficiency through targeted encoder - decoder design. The encoder employed a synergistic modeling approach combining decomposable large-kernel convolutions and local depthwise convolutions to capture both long-range context and local details with low computational overhead. The decoder utilized cross-scale feature differences as guidance to dynamically fuse multi-scale features, enhancing detail recovery and geometric consistency under lightweight constraints. In addition, a temporal soft fusion reprojection loss was employed to better leverage the complementary information of forward and backward frames, improving the robustness of self-supervised training. The model contained 3.0 M parameters and required 3.5 GFLOPs of computation. On KITTI, it achieves Abs Rel=0.105 and δ1=0.892. On Make3D, it achieves Abs Rel=0.308 in a zero-shot setting. On a Rockchip RK3588S, a hybrid-quantized multi-thread implementation runs at 67 frames/s. The results demonstrated that the proposed method achieved a favorable accuracy-efficiency balance on edge devices, making it suitable for real-time monocular depth estimation tasks. [ABSTRACT FROM AUTHOR] |
| Copyright of Journal of Measurement Science & Instrumentation is the property of Journal of Measurement Science & Instrumentation 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 | Links: – Type: pdflink Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 195003976 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Real-time lightweight self-supervised monocular depth estimation. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22YANG%2C+Tianxiang%22">YANG, Tianxiang</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22MENG%2C+Lingjun%22">MENG, Lingjun</searchLink><relatesTo>1</relatesTo><i> menglingjun@nuc.edu.cn</i><br /><searchLink fieldCode="AR" term="%22JIN%2C+Hong%22">JIN, Hong</searchLink><relatesTo>2</relatesTo><br /><searchLink fieldCode="AR" term="%22FENG%2C+Wenjie%22">FENG, Wenjie</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22LIU%2C+Xinhao%22">LIU, Xinhao</searchLink><relatesTo>3</relatesTo> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Journal+of+Measurement+Science+%26+Instrumentation%22">Journal of Measurement Science & Instrumentation</searchLink>. Jun2026, Vol. 17 Issue 2, p278-296. 19p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Depth+maps+%28Digital+image+processing%29%22">Depth maps (Digital image processing)</searchLink><br /><searchLink fieldCode="DE" term="%22Real-time+computing%22">Real-time computing</searchLink><br /><searchLink fieldCode="DE" term="%22Artificial+neural+networks%22">Artificial neural networks</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="%22Edge+computing%22">Edge computing</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Monocular depth estimation aims to predict depth information within a scene from a single RGB image, but many models remain computationally intensive for real-time inference on resource-constrained edge devices. This paper presents a lightweight self-supervised monocular depth estimation network that balances accuracy and efficiency through targeted encoder - decoder design. The encoder employed a synergistic modeling approach combining decomposable large-kernel convolutions and local depthwise convolutions to capture both long-range context and local details with low computational overhead. The decoder utilized cross-scale feature differences as guidance to dynamically fuse multi-scale features, enhancing detail recovery and geometric consistency under lightweight constraints. In addition, a temporal soft fusion reprojection loss was employed to better leverage the complementary information of forward and backward frames, improving the robustness of self-supervised training. The model contained 3.0 M parameters and required 3.5 GFLOPs of computation. On KITTI, it achieves Abs Rel=0.105 and δ1=0.892. On Make3D, it achieves Abs Rel=0.308 in a zero-shot setting. On a Rockchip RK3588S, a hybrid-quantized multi-thread implementation runs at 67 frames/s. The results demonstrated that the proposed method achieved a favorable accuracy-efficiency balance on edge devices, making it suitable for real-time monocular depth estimation tasks. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Journal of Measurement Science & Instrumentation is the property of Journal of Measurement Science & Instrumentation 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.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.62756/jmsi.1674-8042.2026024 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 19 StartPage: 278 Subjects: – SubjectFull: Depth maps (Digital image processing) Type: general – SubjectFull: Real-time computing Type: general – SubjectFull: Artificial neural networks Type: general – SubjectFull: Machine learning Type: general – SubjectFull: Computer vision Type: general – SubjectFull: Edge computing Type: general Titles: – TitleFull: Real-time lightweight self-supervised monocular depth estimation. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: YANG, Tianxiang – PersonEntity: Name: NameFull: MENG, Lingjun – PersonEntity: Name: NameFull: JIN, Hong – PersonEntity: Name: NameFull: FENG, Wenjie – PersonEntity: Name: NameFull: LIU, Xinhao IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 06 Text: Jun2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 16748042 Numbering: – Type: volume Value: 17 – Type: issue Value: 2 Titles: – TitleFull: Journal of Measurement Science & Instrumentation Type: main |
| ResultId | 1 |
