Orthodontics focuses on rectifying misaligned teeth (i.e., malocclusions),
affecting both masticatory function and aesthetics. However, orthodontic
treatment often involves complex, lengthy procedures. As such, generating a 2D
photograph depicting aligned teeth prior to orthodontic treatment is crucial
for effective dentist-patient communication and, more importantly, for
encouraging patients to accept orthodontic intervention. In this paper, we
propose a 3D structure-guided tooth alignment network that takes 2D photographs
as input (e.g., photos captured by smartphones) and aligns the teeth within the
2D image space to generate an orthodontic comparison photograph featuring
aesthetically pleasing, aligned teeth. Notably, while the process operates
within a 2D image space, our method employs 3D intra-oral scanning models
collected in clinics to learn about orthodontic treatment, i.e., projecting the
pre- and post-orthodontic 3D tooth structures onto 2D tooth contours, followed
by a diffusion model to learn the mapping relationship. Ultimately, the aligned
tooth contours are leveraged to guide the generation of a 2D photograph with
aesthetically pleasing, aligned teeth and realistic textures. We evaluate our
network on various facial photographs, demonstrating its exceptional
performance and strong applicability within the orthodontic industry.

我们提出了一种基于三维结构引导的牙齿对齐网络，在二维图像空间内对齐牙齿，生成具有美观度和真实纹理的二维照片，以促进正畸过程中的牙医与患者之间的沟通。

基于三维结构引导的二维照片牙齿调整网络

3D Structure-guided Network for Tooth Alignment in 2D Photograph

Accurate localization of cephalometric landmarks holds great importance in
the fields of orthodontics and orthognathics due to its potential for
automating key point labeling. In the context of landmark detection,
particularly in cephalometrics, it has been observed that existing methods
often lack standardized pipelines and well-designed bias reduction processes,
which significantly impact their performance. In this paper, we revisit a
related task, human pose estimation (HPE), which shares numerous similarities
with cephalometric landmark detection (CLD), and emphasize the potential for
transferring techniques from the former field to benefit the latter. Motivated
by this insight, we have developed a robust and adaptable benchmark based on
the well-established HPE codebase known as MMPose. This benchmark can serve as
a dependable baseline for achieving exceptional CLD performance. Furthermore,
we introduce an upscaling design within the framework to further enhance
performance. This enhancement involves the incorporation of a lightweight and
efficient super-resolution module, which generates heatmap predictions on
high-resolution features and leads to further performance refinement,
benefiting from its ability to reduce quantization bias. In the MICCAI
CLDetection2023 challenge, our method achieves 1st place ranking on three
metrics and 3rd place on the remaining one. The code for our method is
available at this https URL

通过研究人体姿势估计（HPE）和头颅测量检测（CLD）之间的相似性，本文提出一种可靠的用于头颅测量检测性能改进的基准方法，并通过引入超分辨率模块和量化偏差的减少以进一步提升性能，在 MICCAI CLDetection2023 挑战中取得了显著的成绩。