3D generation of dental crown bottoms using context learning

The generation of valid and realistic dental crown bottoms plays a central role in dentistry, as dental crown bottoms are the first point of contact between a tooth preparation and its crown. Every tooth is different, and the retention of the crown bottom heavily depends on how well it fits the preparation while conserving essential properties for ceramic adhesion and smoothness. From this, the generation of the crown bottom becomes a difficult task that only qualified individuals such as dental technicians can complete. Standard geometric modelling techniques such as Computer-Aided Design (CAD) software programs have since been used for this purpose, providing a reliable basis for the generation of dental crown bottoms. Conversely, recent improvements in deep learning have presented new avenues in shape generation tasks that allow for personalized shapes to be created in a short period of time based on learned context. Starting from a set of preparation shapes, this project seeks to compare the efficacy of automatic geometric techniques to deep learning methods in the framework of dental crown bottom shape generation. Results show that deep learning methods such as GANs demand no human manipulation and provide similar visual results to the geometric model on unseen cases in an unsupervised manner. Our code is available at https://github.com/ImaneChafi/C.B.GEN and https://github.com/ImaneChafi/Prep-GAN