Variational Point Encoding Deformation for Dental Modeling

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.