X2Teeth: 3D Teeth Reconstruction from a Single Panoramic Radiograph
This addresses a problem in dental diagnosis and clinical operations by enabling whole-cavity reconstruction from a single X-ray, though it is incremental as it builds on existing reconstruction techniques.
The paper tackles 3D teeth reconstruction from a single panoramic radiograph, a task not previously explored, and achieves a reconstruction IoU of 0.681, significantly outperforming baseline methods by 1.71x and 1.52x.
3D teeth reconstruction from X-ray is important for dental diagnosis and many clinical operations. However, no existing work has explored the reconstruction of teeth for a whole cavity from a single panoramic radiograph. Different from single object reconstruction from photos, this task has the unique challenge of constructing multiple objects at high resolutions. To conquer this task, we develop a novel ConvNet X2Teeth that decomposes the task into teeth localization and single-shape estimation. We also introduce a patch-based training strategy, such that X2Teeth can be end-to-end trained for optimal performance. Extensive experiments show that our method can successfully estimate the 3D structure of the cavity and reflect the details for each tooth. Moreover, X2Teeth achieves a reconstruction IoU of 0.681, which significantly outperforms the encoder-decoder method by $1.71X and the retrieval-based method by $1.52X. Our method can also be promising for other multi-anatomy 3D reconstruction tasks.