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.
@article{arxiv.2510.01640,
title = {Joint Deblurring and 3D Reconstruction for Macrophotography},
author = {Yifan Zhao and Liangchen Li and Yuqi Zhou and Kai Wang and Yan Liang and Juyong Zhang},
journal= {arXiv preprint arXiv:2510.01640},
year = {2025}
}
Comments
Accepted to Pacific Graphics 2025. To be published in Computer Graphics Forum