Automatic Tooth Arrangement with Joint Features of Point and Mesh
  Representations via Diffusion Probabilistic Models
Changsong Lei Mengfei Xia Shaofeng Wang Yaqian Liang Ran Yi Yuhui Wen Yongjin Liu
Tooth arrangement is a crucial step in orthodontics treatment, in which
aligning teeth could improve overall well-being, enhance facial aesthetics, and
boost self-confidence. To improve the efficiency of tooth arrangement and
minimize errors associated with unreasonable designs by inexperienced
practitioners, some deep learning-based tooth arrangement methods have been
proposed. Currently, most existing approaches employ MLPs to model the
nonlinear relationship between tooth features and transformation matrices to
achieve tooth arrangement automatically. However, the limited datasets (which
to our knowledge, have not been made public) collected from clinical practice
constrain the applicability of existing methods, making them inadequate for
addressing diverse malocclusion issues. To address this challenge, we propose a
general tooth arrangement neural network based on the diffusion probabilistic
model. Conditioned on the features extracted from the dental model, the
diffusion probabilistic model can learn the distribution of teeth
transformation matrices from malocclusion to normal occlusion by gradually
denoising from a random variable, thus more adeptly managing real orthodontic
data. To take full advantage of effective features, we exploit both mesh and
point cloud representations by designing different encoding networks to extract
the tooth (local) and jaw (global) features, respectively. In addition to
traditional metrics ADD, PA-ADD, CSA, and ME_{rot}, we propose a new evaluation
metric based on dental arch curves to judge whether the generated teeth meet
the individual normal occlusion. Experimental results demonstrate that our
proposed method achieves state-of-the-art tooth alignment results and
satisfactory occlusal relationships between dental arches. We will publish the
code and dataset.