App. Res. of Comp. Accepted Paper
Vol. 43, 2026 No. 10

Self-supervised monocular depth estimation network based on edge-aware modeling and feature enhancement

Yuan Shuai1,2,3
Wang Zijian1,2,3
Lei Mingbo1,2,3
Yang Yongliang4
1. School of Computer Science and Engineering, Shenyang Jianzhu University, Shenyang 110168, China
2. Liaoning Provincial Key Laboratory of Urban Construction Big Data Management and Analysis, Shenyang 110168, China
3. Shenyang Branch of the National Special Computer Engineering Technology Research Center, Shenyang 110168, China
4. State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China
DOI: 10.19734/j.issn.1001-3695.2025.12.0542

Abstract

Research addressed the limitations of self-supervised monocular depth estimation in preserving structural details and maintaining computational efficiency. Existing attention-based models capture long-range dependencies effectively. However, repeated downsampling and upsampling often weaken edge information and increase computational cost. A network architecture that integrates an edge-aware mechanism with an enhanced feature fusion strategy was developed. In the encoding stage, an edge-aware module modeled gradient variations in feature maps and strengthened object boundaries and depth discontinuities. In the decoding stage, a feature fusion enhancement module integrated high-level semantic features with low-level spatial details and reduced high-frequency information loss during upsampling. Comparative experiments on the KITTI dataset showed improvements in quantitative metrics and visual quality. Cross-dataset evaluations on Make3D and NYUv2 further confirmed generalization capability. The results indicate that the proposed architecture improves structural representation in depth estimation while maintaining computational

Foundation Support

辽宁省科技厅项目(2023JH2/101300212)
国家自然科学基金项目(62073227)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2025.12.0542
Publish at: Application Research of Computers Accepted Paper, Vol. 43, 2026 No. 10

Publish History

[2026-07-03] Accepted Paper

Cite This Article

袁帅, 王梓健, 雷鸣波, 等. 基于边缘感知模型与特征增强的自监督单目深度估计网络 [J]. 计算机应用研究, 2026, 43 (10). (2026-07-30). https://doi.org/10.19734/j.issn.1001-3695.2025.12.0542. (Yuan Shuai, Wang Zijian, Lei Mingbo, et al. Self-supervised monocular depth estimation network based on edge-aware modeling and feature enhancement [J]. Application Research of Computers, 2026, 43 (10). (2026-07-30). https://doi.org/10.19734/j.issn.1001-3695.2025.12.0542. )

About the Journal

  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

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