Dental Restoration Using A Multi-Resolution Deep Learning Approach

Computer assisted design software is currently used by technicians to design dental crowns. However, this process involves manual adjustments that are time consuming and lead to great variability in quality of the design since they depend on the technician’s experience. We developed a fully automatic approach that learns from natural teeth in dental scans using 3D conditional shape completion. Our work extends depth map-based approaches to generate crown shapes in 3D directly. Using a Generative Adversarial Network (GAN), our deep learning model is able to generate patient-specific point clouds of teeth starting from normalized incomplete point clouds. The model generates a crown's outer surface that looks realistic, with a mean Chamfer Distance (CD) of 0.55 millimiter when compared to real teeth.