Variational Point Encoding Deformation for Dental Modeling
Abstract
We introduce VF-Net, a probabilistic extension of FoldingNet, for learning representations of point cloud data. VF-Net overcomes the limitations of existing models by incorporating a 1-to-1 mapping between input and output points. By eliminating the need for Chamfer distance optimization, this approach enables the development of a fully probabilistic model. We demonstrate that VF-Net outperforms other models in dental reconstruction tasks, including shape completion and tooth wear simulation. The learned latent representations exhibit robustness and enable meaningful interpolation between dental scans.
Cite
Text
Ye et al. "Variational Point Encoding Deformation for Dental Modeling." ICML 2023 Workshops: SPIGM, 2023.Markdown
[Ye et al. "Variational Point Encoding Deformation for Dental Modeling." ICML 2023 Workshops: SPIGM, 2023.](https://mlanthology.org/icmlw/2023/ye2023icmlw-variational/)BibTeX
@inproceedings{ye2023icmlw-variational,
title = {{Variational Point Encoding Deformation for Dental Modeling}},
author = {Ye, Johan Ziruo and Ørkild, Thomas and Søndergard, Peter Lempel and Hauberg, Søren},
booktitle = {ICML 2023 Workshops: SPIGM},
year = {2023},
url = {https://mlanthology.org/icmlw/2023/ye2023icmlw-variational/}
}