  Cone beam computed tomography (CBCT) has been widely used in clinical
practice, especially in dental clinics, while the radiation dose of X-rays when
capturing has been a long concern in CBCT imaging. Several research works have
been proposed to reconstruct high-quality CBCT images from sparse-view 2D
projections, but the current state-of-the-arts suffer from artifacts and the
lack of fine details. In this paper, we propose SNAF for sparse-view CBCT
reconstruction by learning the neural attenuation fields, where we have
invented a novel view augmentation strategy to overcome the challenges
introduced by insufficient data from sparse input views. Our approach achieves
superior performance in terms of high reconstruction quality (30+ PSNR) with
only 20 input views (25 times fewer than clinical collections), which
outperforms the state-of-the-arts. We have further conducted comprehensive
experiments and ablation analysis to validate the effectiveness of our
approach.
