Joint Deblurring and 3D Reconstruction for Macrophotography

• URL: http://arxiv.org/abs/2510.01640v1
• Date: Thu, 02 Oct 2025 03:43:05 GMT
• Title: Joint Deblurring and 3D Reconstruction for Macrophotography
• Authors: Yifan Zhao, Liangchen Li, Yuqi Zhou, Kai Wang, Yan Liang, Juyong Zhang,
• Abstract summary: We propose a joint deblurring and 3D reconstruction method for macrophotography.<n>We jointly optimize the clear 3D model of the object and the defocus blur kernel of each pixel.<n>Our proposed method can not only achieve high-quality image deblurring but also recover high-fidelity 3D appearance.
• Score: 30.941639453575476
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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