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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2510.01640 |
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| _version_ | 1866914071553507328 |
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| author | Zhao, Yifan Li, Liangchen Zhou, Yuqi Wang, Kai Liang, Yan Zhang, Juyong |
| author_facet | Zhao, Yifan Li, Liangchen Zhou, Yuqi Wang, Kai Liang, Yan Zhang, Juyong |
| contents | Macro lens has the advantages of high resolution and large magnification, and 3D modeling of small and detailed objects can provide richer information. However, defocus blur in macrophotography is a long-standing problem that heavily hinders the clear imaging of the captured objects and high-quality 3D reconstruction of them. Traditional image deblurring methods require a large number of images and annotations, and there is currently no multi-view 3D reconstruction method for macrophotography. In this work, we propose a joint deblurring and 3D reconstruction method for macrophotography. Starting from multi-view blurry images captured, we jointly optimize the clear 3D model of the object and the defocus blur kernel of each pixel. The entire framework adopts a differentiable rendering method to self-supervise the optimization of the 3D model and the defocus blur kernel. Extensive experiments show that from a small number of multi-view images, our proposed method can not only achieve high-quality image deblurring but also recover high-fidelity 3D appearance. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_01640 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Joint Deblurring and 3D Reconstruction for Macrophotography Zhao, Yifan Li, Liangchen Zhou, Yuqi Wang, Kai Liang, Yan Zhang, Juyong Computer Vision and Pattern Recognition Macro lens has the advantages of high resolution and large magnification, and 3D modeling of small and detailed objects can provide richer information. However, defocus blur in macrophotography is a long-standing problem that heavily hinders the clear imaging of the captured objects and high-quality 3D reconstruction of them. Traditional image deblurring methods require a large number of images and annotations, and there is currently no multi-view 3D reconstruction method for macrophotography. In this work, we propose a joint deblurring and 3D reconstruction method for macrophotography. Starting from multi-view blurry images captured, we jointly optimize the clear 3D model of the object and the defocus blur kernel of each pixel. The entire framework adopts a differentiable rendering method to self-supervise the optimization of the 3D model and the defocus blur kernel. Extensive experiments show that from a small number of multi-view images, our proposed method can not only achieve high-quality image deblurring but also recover high-fidelity 3D appearance. |
| title | Joint Deblurring and 3D Reconstruction for Macrophotography |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2510.01640 |