MeshUDF: Fast and Differentiable Meshing of Unsigned Distance Field Networks
Abstract
Unsigned Distance Fields (UDFs) can be used to represent non-watertight surfaces. However, current approaches to converting them into explicit meshes tend to either be expensive or to degrade the accuracy. Here, we extend the marching cube algorithm to handle UDFs, both fast and accurately. Moreover, our approach to surface extraction is differentiable, which is key to using pretrained UDF networks to fit sparse data.
Cite
Text
Guillard et al. "MeshUDF: Fast and Differentiable Meshing of Unsigned Distance Field Networks." Proceedings of the European Conference on Computer Vision (ECCV), 2022. doi:10.1007/978-3-031-20062-5_33Markdown
[Guillard et al. "MeshUDF: Fast and Differentiable Meshing of Unsigned Distance Field Networks." Proceedings of the European Conference on Computer Vision (ECCV), 2022.](https://mlanthology.org/eccv/2022/guillard2022eccv-meshudf/) doi:10.1007/978-3-031-20062-5_33BibTeX
@inproceedings{guillard2022eccv-meshudf,
title = {{MeshUDF: Fast and Differentiable Meshing of Unsigned Distance Field Networks}},
author = {Guillard, Benoît and Stella, Federico and Fua, Pascal},
booktitle = {Proceedings of the European Conference on Computer Vision (ECCV)},
year = {2022},
doi = {10.1007/978-3-031-20062-5_33},
url = {https://mlanthology.org/eccv/2022/guillard2022eccv-meshudf/}
}