Variational Point Encoding Deformation for Dental Modeling

Johan Ziruo Ye, Thomas Ørkild, Peter Lempel Søndergard, Søren Hauberg

Keywords: Point cloud, Variational Autoencoder, Representation learning, point cloud reconstruction, shape completion

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.

Submission Number: 44