-
VoronoiNet: General Functional Approximators With Local Support
AbstractVoronoi 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.
Related Material
[pdf][bibtex]@InProceedings{Williams_2020_CVPR_Workshops,
author = {Williams, Francis and Parent-Levesque, Jerome and Nowrouzezahrai, Derek and Panozzo, Daniele and Yi, Kwang Moo and Tagliasacchi, Andrea},
title = {VoronoiNet: General Functional Approximators With Local Support},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops},
month = {June},
year = {2020}
}
These CVPR 2020 workshop papers are the Open Access versions, provided by the Computer Vision Foundation.
Except for the watermark, they are identical to the accepted versions; the final published version of the proceedings is available on IEEE Xplore.
Except for the watermark, they are identical to the accepted versions; the final published version of the proceedings is available on IEEE Xplore.
This material is presented to ensure timely dissemination of scholarly and technical work.
Copyright and all rights therein are retained by authors or by other copyright holders.
All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright.