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| Hauptverfasser: | , , , , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| Schlagworte: | |
| Online-Zugang: | https://arxiv.org/abs/2508.19555 |
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| _version_ | 1866916920810274816 |
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| author | Zhang, Yu-Wei Han, Tongju Gao, Lipeng Wei, Mingqiang Liu, Hui Li, Changbao Zhang, Caiming |
| author_facet | Zhang, Yu-Wei Han, Tongju Gao, Lipeng Wei, Mingqiang Liu, Hui Li, Changbao Zhang, Caiming |
| contents | This paper presents MonoRelief V2, an end-to-end model designed for directly recovering 2.5D reliefs from single images under complex material and illumination variations. In contrast to its predecessor, MonoRelief V1 [1], which was solely trained on synthetic data, MonoRelief V2 incorporates real data to achieve improved robustness, accuracy and efficiency. To overcome the challenge of acquiring large-scale real-world dataset, we generate approximately 15,000 pseudo real images using a text-to-image generative model, and derive corresponding depth pseudo-labels through fusion of depth and normal predictions. Furthermore, we construct a small-scale real-world dataset (800 samples) via multi-view reconstruction and detail refinement. MonoRelief V2 is then progressively trained on the pseudo-real and real-world datasets. Comprehensive experiments demonstrate its state-of-the-art performance both in depth and normal predictions, highlighting its strong potential for a range of downstream applications. Code is at: https://github.com/glp1001/MonoreliefV2. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_19555 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | MonoRelief V2: Leveraging Real Data for High-Fidelity Monocular Relief Recovery Zhang, Yu-Wei Han, Tongju Gao, Lipeng Wei, Mingqiang Liu, Hui Li, Changbao Zhang, Caiming Computer Vision and Pattern Recognition This paper presents MonoRelief V2, an end-to-end model designed for directly recovering 2.5D reliefs from single images under complex material and illumination variations. In contrast to its predecessor, MonoRelief V1 [1], which was solely trained on synthetic data, MonoRelief V2 incorporates real data to achieve improved robustness, accuracy and efficiency. To overcome the challenge of acquiring large-scale real-world dataset, we generate approximately 15,000 pseudo real images using a text-to-image generative model, and derive corresponding depth pseudo-labels through fusion of depth and normal predictions. Furthermore, we construct a small-scale real-world dataset (800 samples) via multi-view reconstruction and detail refinement. MonoRelief V2 is then progressively trained on the pseudo-real and real-world datasets. Comprehensive experiments demonstrate its state-of-the-art performance both in depth and normal predictions, highlighting its strong potential for a range of downstream applications. Code is at: https://github.com/glp1001/MonoreliefV2. |
| title | MonoRelief V2: Leveraging Real Data for High-Fidelity Monocular Relief Recovery |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2508.19555 |