SDFDiff: Differentiable Rendering of Signed Distance Fields for 3D Shape Optimization
Abstract
We propose SDFDiff, a novel approach for image-based shape optimization using differentiable rendering of 3D shapes represented by signed distance functions (SDFs). Compared to other representations, SDFs have the advantage that they can represent shapes with arbitrary topology, and that they guarantee watertight surfaces. We apply our approach to the problem of multi-view 3D reconstruction, where we achieve high reconstruction quality and can capture complex topology of 3D objects. In addition, we employ a multi-resolution strategy to obtain a robust optimization algorithm. We further demonstrate that our SDF-based differentiable renderer can be integrated with deep learning models, which opens up options for learning approaches on 3D objects without 3D supervision. In particular, we apply our method to single-view 3D reconstruction and achieve state-of-the-art results.
Cite
Text
Jiang et al. "SDFDiff: Differentiable Rendering of Signed Distance Fields for 3D Shape Optimization." Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2020. doi:10.1109/CVPR42600.2020.00133Markdown
[Jiang et al. "SDFDiff: Differentiable Rendering of Signed Distance Fields for 3D Shape Optimization." Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2020.](https://mlanthology.org/cvpr/2020/jiang2020cvpr-sdfdiff/) doi:10.1109/CVPR42600.2020.00133BibTeX
@inproceedings{jiang2020cvpr-sdfdiff,
title = {{SDFDiff: Differentiable Rendering of Signed Distance Fields for 3D Shape Optimization}},
author = {Jiang, Yue and Ji, Dantong and Han, Zhizhong and Zwicker, Matthias},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
year = {2020},
doi = {10.1109/CVPR42600.2020.00133},
url = {https://mlanthology.org/cvpr/2020/jiang2020cvpr-sdfdiff/}
}