Variational Point Encoding Deformation for Dental Modeling

Digital dentistry has made significant advancements in recent years, yet numerous challenges remain to be addressed. In this study, we release a new extensive dataset of tooth meshes to encourage further research. Additionally, we propose Variational FoldingNet (VF-Net), which extends FoldingNet to enable probabilistic learning of point cloud representations. A key challenge in existing latent variable models for point clouds is the lack of a 1-to-1 mapping between input points and output points. Instead, they must rely on optimizing Chamfer distances, a metric that does not have a normalized distributional counterpart, preventing its usage in probabilistic models. We demonstrate that explicit minimization of Chamfer distances can be replaced by a suitable encoder, which allows us to increase computational efficiency while simplifying the probabilistic extension. Our experimental findings present empirical evidence demonstrating the superior performance of VF-Net over existing models in terms of dental scan reconstruction and extrapolation. Additionally, our investigation highlights the robustness of VF-Net's latent representations. These results underscore the promising prospects of VF-Net as an effective and reliable method for point cloud reconstruction and analysis.