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

本文提出了 Occudent，一种利用神经隐式函数从全景射线照片中进行三维牙齿重建的框架，通过多标签分割和重建网络实现了在 3D 空间中隐式确定牙齿边界，并在实验证明 Occudent 相比最先进的方法具有优越性。