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Panoramic radiography is a widely used imaging modality in dental practiceand research. However, it only provides flattened 2D images, which limits thedetailed assessment of dental structures. In this paper, we propose Occudent, aframework for 3D teeth reconstruction from panoramic radiographs using neuralimplicit functions, which, to the best of our knowledge, is the first work todo so. For a given point in 3D space, the implicit function estimates whetherthe point is occupied by a tooth, and thus implicitly determines the boundariesof 3D tooth shapes. Firstly, Occudent applies multi-label segmentation to theinput panoramic radiograph. Next, tooth shape embeddings as well as tooth classembeddings are generated from the segmentation outputs, which are fed to thereconstruction network. A novel module called Conditional eXcitation (CX) isproposed in order to effectively incorporate the combined shape and classembeddings into the implicit function. The performance of Occudent is evaluatedusing both quantitative and qualitative measures. Importantly, Occudent istrained and validated with actual panoramic radiographs as input, distinct fromrecent works which used synthesized images. Experiments demonstrate thesuperiority of Occudent over state-of-the-art methods.

3D Teeth Reconstruction from Panoramic Radiographs using Neural Implicit Functions