  Panoramic radiography (Panoramic X-ray, PX) is a widely used imaging modality
for dental examination. However, PX only provides a flattened 2D image, lacking
in a 3D view of the oral structure. In this paper, we propose NeBLa (Neural
Beer-Lambert) to estimate 3D oral structures from real-world PX. NeBLa tackles
full 3D reconstruction for varying subjects (patients) where each
reconstruction is based only on a single panoramic image. We create an
intermediate representation called simulated PX (SimPX) from 3D Cone-beam
computed tomography (CBCT) data based on the Beer-Lambert law of X-ray
rendering and rotational principles of PX imaging. SimPX aims at not only
truthfully simulating PX, but also facilitates the reverting process back to 3D
data. We propose a novel neural model based on ray tracing which exploits both
global and local input features to convert SimPX to 3D output. At inference, a
real PX image is translated to a SimPX-style image with semantic
regularization, and the translated image is processed by generation module to
produce high-quality outputs. Experiments show that NeBLa outperforms prior
state-of-the-art in reconstruction tasks both quantitatively and qualitatively.
Unlike prior methods, NeBLa does not require any prior information such as the
shape of dental arches, nor the matched PX-CBCT dataset for training, which is
difficult to obtain in clinical practice. Our code is available at
https://github.com/sihwa-park/nebla.
