3D Teeth Reconstruction from Panoramic Radiographs using Neural Implicit
  Functions
Sihwa Park Seongjun Kim In-Seok Song Seung Jun Baek
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.