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ShanghaiTech University Knowledge Management System
| Discriminative-Guided Diffusion-Based Self-supervised Monocular Depth Estimation | |
| 2025 | |
| 会议录名称 | LECTURE NOTES IN COMPUTER SCIENCE (INCLUDING SUBSERIES LECTURE NOTES IN ARTIFICIAL INTELLIGENCE AND LECTURE NOTES IN BIOINFORMATICS)
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| ISSN | 0302-9743 |
| 卷号 | 15036 LNCS |
| 页码 | 328-342 |
| 发表状态 | 已发表 |
| DOI | 10.1007/978-981-97-8508-7_23 |
| 摘要 | Self-supervised monocular depth estimation is a critical task in computer vision. Existing methods can be typically categorized into discriminative-based and generative-based methods according to different data modeling approaches. Discriminative-based methods are distinguished by high accuracy, while generative-based methods are notable for superior robustness. Given that images captured in real-world scenarios are inevitably influenced by various external factors, it is essential to develop a robust and accurate depth estimation algorithm. However, there is limited research on the balance of robustness and accuracy by exploring the interactions between discriminative and generative networks. We propose a generative diffusion-based self-supervised monocular depth estimation algorithm guided by discriminative networks and incorporate a depth interaction constraint. We utilize discriminative networks to optimize image-guided information for the denoising process within the diffusion model. This approach seamlessly combines great robustness with high accuracy. Additionally, to reduce the impact of low texture regions on the reprojection photometric loss, we design a texture-aware discriminatory mask module. This module strengthens the constraint capability of the photometric consistency. We conduct experiments on the KITTI and Make3D datasets. The results demonstrate that our method successfully balances accuracy and robustness. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. |
| 关键词 | Generative adversarial networks Image denoising Semi-supervised learning Critical tasks Depth Estimation Diffusion model Discriminative networks Estimation algorithm High-accuracy Modeling approach Monocular depth estimation Photometrics Real-world scenario |
| 会议名称 | 7th Chinese Conference on Pattern Recognition and Computer Vision, PRCV 2024 |
| 出版地 | 152 BEACH ROAD, #21-01/04 GATEWAY EAST, SINGAPORE, 189721, SINGAPORE |
| 会议地点 | Urumqi, China |
| 会议日期 | October 18, 2024 - October 20, 2024 |
| URL | 查看原文 |
| 收录类别 | EI ; CPCI-S |
| 语种 | 英语 |
| 资助项目 | National Science and Technology Major Project from Minister of Science and Technology, China[2018AAA0103100] ; National Natural Science Foundation of China[62303441] ; Youth Innovation Promotion Association, Chinese Academy of Sciences[2021233] ; Shanghai Academic Research Leader[22XD1424500] |
| WOS研究方向 | Computer Science ; Radiology, Nuclear Medicine & Medical Imaging |
| WOS类目 | Computer Science, Artificial Intelligence ; Computer Science, Theory & Methods ; Radiology, Nuclear Medicine & Medical Imaging |
| WOS记录号 | WOS:001416888200023 |
| 出版者 | Springer Science and Business Media Deutschland GmbH |
| EI入藏号 | 20244717397750 |
| EI主题词 | Self-supervised learning |
| EISSN | 1611-3349 |
| EI分类号 | 1101.2 ; 1101.2.1 ; 1106.3.1 ; 716.1 Information Theory and Signal Processing |
| 原始文献类型 | Conference article (CA) |
| 文献类型 | 会议论文 |
| 条目标识符 | https://kms.shanghaitech.edu.cn/handle/2MSLDSTB/455185 |
| 专题 | 信息科学与技术学院 信息科学与技术学院_特聘教授组_张晓林组 信息科学与技术学院_硕士生 |
| 通讯作者 | Zhu, Dongchen |
| 作者单位 | 1.Bionic Vision System Laboratory, Shanghai Institute of Microsystem and Information Technology, Chinese Academy of Sciences, Shanghai; 200050, China; 2.School of Information Science and Technology, ShanghaiTech University, Shanghai; 201210, China; 3.University of Chinese Academy of Sciences, Beijing; 100049, China; 4.University of Science and Technology of China, Anhui, Hefei; 230027, China |
| 第一作者单位 | 信息科学与技术学院 |
| 推荐引用方式 GB/T 7714 | Liu, Runze,Zhang, Guanghui,Zhu, Dongchen,et al. Discriminative-Guided Diffusion-Based Self-supervised Monocular Depth Estimation[C]. 152 BEACH ROAD, #21-01/04 GATEWAY EAST, SINGAPORE, 189721, SINGAPORE:Springer Science and Business Media Deutschland GmbH,2025:328-342. |
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