HTCNet: Hierarchical Point-Graph Tooth Point Cloud Completion With Image Assistance

With the growing prominence of digital dentistry, high-quality and cost-effective three-dimensional (3D) intra-oral scanned (IOS) tooth data has become essential in various dental applications. However, limited by sensor resolution and occlusion, the acquired tooth point clouds often suffer from sparsity and incompleteness, resulting in missing regions and insufficient 3D detail. In this paper, we propose a Hierarchical Tooth Completion Network(HTCNet), a novel image-assistance framework to integrate geometry and structure learning from 2D images and 3D point clouds for 3D tooth completion. It employs a dual-stream-based hierarchical feature extraction, utilizing a 2D stream for extracting image features and a 3D stream for processing point clouds. Additionally, we introduce 3DTeethSegX for evaluating image-assistance tooth completion to address the lack of consideration for the comprehensive situation in the current dataset. Extensive experiments on two tooth completion datasets demonstrate the superiority and robustness of HTCNet, showcasing its potential to generate high-resolution 3D tooth data from low-resolution, cost-effective sensors with the assistance of 2D images. To the best of our knowledge, it is the first to use image-assistance techniques to significantly improve the accuracy and effectiveness of 3D tooth completion. Source code will be available at: https://github.com/labiip/HTCNet.