Bits2Bites: Intra-oral Scans Occlusal Classification

Lorenzo Borghi, Luca Lumetti, Francesca Cremonini, Federico Rizzo, Costantino Grana, Luca Lombardo, Federico Bolelli

Keywords: Intra-oral Scans, Medical Imaging, Dental Occlusion, 3D Point Cloud

TL;DR: The first publicly available dataset of intra-oral scans designed specifically for occlusal classification.

Abstract: We introduce Bits2Bites, the first publicly available dataset for occlusal classification from intra-oral scans, comprising 200 paired upper and lower dental arches annotated across multiple clinically relevant dimensions (sagittal, vertical, transverse, and midline relationships). Leveraging this resource, we propose a multi-task learning benchmark that jointly predicts five occlusal traits from raw 3D point clouds using state-of-the-art point-based neural architectures. Our approach includes extensive ablation studies assessing the benefits of multi-task learning against single-task baselines, as well as the impact of automatically-predicted anatomical landmarks as input features. Results demonstrate the feasibility of directly inferring comprehensive occlusion information from unstructured 3D data, achieving promising performance across all tasks. Our entire dataset, code, and pretrained models are publicly released to foster further research in automated orthodontic diagnosis.

Submission Number: 7