Improving Monocular Depth Estimation Through Knowledge Distillation: Better Visual Quality and Efficiency
This paper introduces a novel knowledge distillation (KD) framework for monocular depth estimation (MDE), incorporating dynamic weight adaptation to address critical challenges. The proposed approach effectively mitigates visual limitations, including blurred object boundaries and discontinuous arti...
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10818481/ |
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| author | Chang Yeop Lee Dong Ju Kim Young Joo Suh Do Kyung Hwang |
| author_facet | Chang Yeop Lee Dong Ju Kim Young Joo Suh Do Kyung Hwang |
| author_sort | Chang Yeop Lee |
| collection | DOAJ |
| description | This paper introduces a novel knowledge distillation (KD) framework for monocular depth estimation (MDE), incorporating dynamic weight adaptation to address critical challenges. The proposed approach effectively mitigates visual limitations, including blurred object boundaries and discontinuous artifacts, while preserving quantitative performance. Beyond addressing these visual challenges, the innovative KD framework reduces the complexity of depth information, thereby significantly enhancing computational efficiency. To validate the effectiveness of the proposed framework, extensive comparative evaluations were performed using state-of-the-art models, including AdaBins, LocalBins, BinsFormer, PixelFormer, and ZoeDepth. These evaluations were conducted on benchmark datasets, including NYU Depth V2 and SUN RGB-D for indoor environments and KITTI for outdoor scenarios, to ensure a rigorous and comprehensive assessment of robustness and generalization capabilities. The results demonstrate that the proposed KD framework outperforms existing methods in visual quality across all datasets while achieving notable computational benefits, including a 15.45% reduction in Floating Point Operations (FLOPs) for the LocalBins model and 7.72% for the ZoeDepth model. |
| format | Article |
| id | doaj-art-e9695410d9af4c0998801f8fbafcdb3b |
| institution | OA Journals |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-e9695410d9af4c0998801f8fbafcdb3b2025-08-20T02:26:28ZengIEEEIEEE Access2169-35362025-01-01132763278210.1109/ACCESS.2024.352391210818481Improving Monocular Depth Estimation Through Knowledge Distillation: Better Visual Quality and EfficiencyChang Yeop Lee0https://orcid.org/0009-0003-0607-9901Dong Ju Kim1https://orcid.org/0009-0009-6950-4200Young Joo Suh2https://orcid.org/0000-0001-7208-1709Do Kyung Hwang3https://orcid.org/0000-0003-4271-5672Institute of Artificial Intelligence, Pohang University of Science and Technology, Pohang-si, South KoreaInstitute of Artificial Intelligence, Pohang University of Science and Technology, Pohang-si, South KoreaInstitute of Artificial Intelligence, Pohang University of Science and Technology, Pohang-si, South KoreaKorea Institute of Robotics and Technology Convergence, Pohang-si, South KoreaThis paper introduces a novel knowledge distillation (KD) framework for monocular depth estimation (MDE), incorporating dynamic weight adaptation to address critical challenges. The proposed approach effectively mitigates visual limitations, including blurred object boundaries and discontinuous artifacts, while preserving quantitative performance. Beyond addressing these visual challenges, the innovative KD framework reduces the complexity of depth information, thereby significantly enhancing computational efficiency. To validate the effectiveness of the proposed framework, extensive comparative evaluations were performed using state-of-the-art models, including AdaBins, LocalBins, BinsFormer, PixelFormer, and ZoeDepth. These evaluations were conducted on benchmark datasets, including NYU Depth V2 and SUN RGB-D for indoor environments and KITTI for outdoor scenarios, to ensure a rigorous and comprehensive assessment of robustness and generalization capabilities. The results demonstrate that the proposed KD framework outperforms existing methods in visual quality across all datasets while achieving notable computational benefits, including a 15.45% reduction in Floating Point Operations (FLOPs) for the LocalBins model and 7.72% for the ZoeDepth model.https://ieeexplore.ieee.org/document/10818481/Deep learningimage segmentationcomputational complexityknowledge transfer |
| spellingShingle | Chang Yeop Lee Dong Ju Kim Young Joo Suh Do Kyung Hwang Improving Monocular Depth Estimation Through Knowledge Distillation: Better Visual Quality and Efficiency IEEE Access Deep learning image segmentation computational complexity knowledge transfer |
| title | Improving Monocular Depth Estimation Through Knowledge Distillation: Better Visual Quality and Efficiency |
| title_full | Improving Monocular Depth Estimation Through Knowledge Distillation: Better Visual Quality and Efficiency |
| title_fullStr | Improving Monocular Depth Estimation Through Knowledge Distillation: Better Visual Quality and Efficiency |
| title_full_unstemmed | Improving Monocular Depth Estimation Through Knowledge Distillation: Better Visual Quality and Efficiency |
| title_short | Improving Monocular Depth Estimation Through Knowledge Distillation: Better Visual Quality and Efficiency |
| title_sort | improving monocular depth estimation through knowledge distillation better visual quality and efficiency |
| topic | Deep learning image segmentation computational complexity knowledge transfer |
| url | https://ieeexplore.ieee.org/document/10818481/ |
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