Autoencoder-Based Reconstruction and Restoration of 3D Dental Objects

Abstract

This paper presents an exploration of autoencoders for 3D teeth reconstruction and completion, a crucial area in digital dentistry aimed at enhancing the efficiency of dental restoration and reconstruction. Accurate reconstruction of dental geometries is essential for developing personalized treatment plans and improving patient outcomes. However, most current approaches still rely on 2D imaging methods and often fall short of capturing the full complexity of tooth structures. In this study, we present a deep learning-based solution that effectively reconstructs 3D tooth models using point cloud representations. The results show that our method improve the accuracy of 3D tooth reconstruction and completion, as demonstrated by the results presented in these experiments. Our results have advancing digital dentistry techniques by providing a new methodology to utilize modern machine learning capabilities to enhance dental model reconstruction, which could lead to better treatment options, including the restorative treatments and the fabrication of customized dental prosthetics.