RGB oralscan video-based orthodontic treatment monitoring

Abstract

Orthodontic treatment monitoring involves using current images and previous 3D models to estimate the relative position of individual teeth before and after orthodontic treatment. This process differs from image-based object 6D pose estimation due to the gingiva deformation and varying pose offsets for each tooth during treatment. Motivated by the fact that the poses of molars remain relatively fixed in implicit orthodontics, we design an approach that employs multiview pose evaluation and bidirectional temporal propagation for jaw pose estimation and then employs an iteration-based method for tooth alignment. To handle changes in tooth appearance or location with weak texture across frames, we also introduce an instance propagation module that leverages positional and semantic information to explore instance relations in the temporal domain. We evaluated the performance of our approach using both the Shining3D tooth pose dataset and the Aoralscan3 tooth registration dataset. Our experimental results demonstrate remarkable accuracy improvements compared with existing methods.