VoronoiNet : General Functional Approximators with Local Support

Francis Williams, Jérôme Parent-Lévesque, Derek Nowrouzezahrai, Daniele Panozzo, Kwang Moo Yi, Andrea Tagliasacchi

CVPRW 2020 pp. 1069-1073

doi:10.1109/CVPRW50498.2020.00140 /cvprw/2020/williams2020cvprw-voronoinet/

Abstract

Voronoi diagrams are highly compact representations that are used in various Graphics applications. In this work, we show how to embed a differentiable version of it – via a novel deep architecture – into a generative deep network. By doing so, we achieve a highly compact latent embedding that is able to provide much more detailed reconstructions, both in 2D and 3D, for various shapes. In this tech report, we introduce our representation and present a set of preliminary results comparing it with recently proposed implicit occupancy networks.

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Cite

Text

Williams et al. "VoronoiNet : General Functional Approximators with Local Support." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2020. doi:10.1109/CVPRW50498.2020.00140

Markdown

[Williams et al. "VoronoiNet : General Functional Approximators with Local Support." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2020.](https://mlanthology.org/cvprw/2020/williams2020cvprw-voronoinet/) doi:10.1109/CVPRW50498.2020.00140

BibTeX

@inproceedings{williams2020cvprw-voronoinet,
  title     = {{VoronoiNet : General Functional Approximators with Local Support}},
  author    = {Williams, Francis and Parent-Lévesque, Jérôme and Nowrouzezahrai, Derek and Panozzo, Daniele and Yi, Kwang Moo and Tagliasacchi, Andrea},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops},
  year      = {2020},
  pages     = {1069-1073},
  doi       = {10.1109/CVPRW50498.2020.00140},
  url       = {https://mlanthology.org/cvprw/2020/williams2020cvprw-voronoinet/}
}