Mesh denoising is a fundamental problem in digital geometry processing. It seeks to remove surface noise, while preserving surface intrinsic signals as accurately as possible. While the traditional wisdom has been built upon specialized priors to smooth surfaces, learning-based approaches are making their debut with great success in generalization and automation. In this work, we provide a comprehensive review of the advances in mesh denoising, containing both traditional geometric approaches and recent learning-based methods. First, to familiarize readers with the denoising tasks, we summarize four common issues in mesh denoising. We then provide two categorizations of the existing denoising methods. Furthermore, three important categories, including optimization-, filter-, and data-driven-based techniques, are introduced and analyzed in detail, respectively. Both qualitative and quantitative comparisons are illustrated, to demonstrate the effectiveness of the state-of-the-art denoising methods. Finally, potential directions of future work are pointed out to solve the common problems of these approaches. A mesh denoising benchmark is also built in this work, and future researchers will easily and conveniently evaluate their methods with the state-of-the-art approaches.

翻译：在数字几何处理过程中,Mesh拆卸是一个根本问题。它力求消除表面噪音,同时尽可能准确地保存表面内在信号。传统智慧是建立在平滑表面的专门前科之上的,而以学习为基础的方法则在一般化和自动化方面取得巨大成功。在这项工作中,我们全面审查了网状拆卸方面的进展,包括传统的几何方法和最近的学习方法。首先,为了使读者熟悉解析任务,我们总结了四个共同的问题。然后,我们提供了现有除去方法的两种分类。此外,还引进并详细分析了三大重要类别,包括优化、过滤和数据驱动技术。说明了定性和定量比较,以显示最新除尘方法的有效性。最后,指出未来工作的潜在方向,以解决这些方法的共同问题。在这项工作中也建立了网状拆卸基准,未来的研究人员将方便地、方便地用最新方法评估其方法。