Shape modeling is a challenging task with many potential applications in
computer vision and medical imaging. There are many shape modeling methods in
the literature, each with its advantages and applications. However, many shape
modeling methods have difficulties handling shapes that have missing pieces or
outliers. In this regard, this paper introduces shape denoising, a fundamental
problem in shape modeling that lies at the core of many computer vision and
medical imaging applications and has not received enough attention in the
literature. The paper introduces six types of noise that can be used to perturb
shapes as well as an objective measure for the noise level and for comparing
methods on their shape denoising capabilities. Finally, the paper evaluates
seven methods capable of accomplishing this task, of which six are based on
deep learning, including some generative models.

本研究论文介绍了形状建模中的形状去噪问题，该问题对计算机视觉和医学成像应用具有核心意义，并且在文献中尚未得到足够的关注。该论文介绍了六种形状扰动噪声类型以及用于评估方法的噪声水平和形状去噪能力的客观度量标准。最后，论文评估了七种能够完成这一任务的方法，其中六种基于深度学习，包括一些生成模型。