Intelligent mesh generation (IMG) refers to a technique to generate mesh by machine learning, which is a relatively new and promising research field. Within its short life span, IMG has greatly expanded the generalizability and practicality of mesh generation techniques and brought many breakthroughs and potential possibilities for mesh generation. However, there is a lack of surveys focusing on IMG methods covering recent works. In this paper, we are committed to a systematic and comprehensive survey describing the contemporary IMG landscape. Focusing on 110 preliminary IMG methods, we conducted an in-depth analysis and evaluation from multiple perspectives, including the core technique and application scope of the algorithm, agent learning goals, data types, targeting challenges, advantages and limitations. With the aim of literature collection and classification based on content extraction, we propose three different taxonomies from three views of key technique, output mesh unit element, and applicable input data types. Finally, we highlight some promising future research directions and challenges in IMG. To maximize the convenience of readers, a project page of IMG is provided at \url{https://github.com/xzb030/IMG_Survey}.
翻译:智能网格生成(IMG)是指通过机器学习生成网格的技术,这是一个相对较新和有希望的研究领域,在短短的生命周期内,IMG大大扩大了网格生成技术的普及性和实用性,为网格生成带来了许多突破和潜在的可能性,然而,缺乏侧重于涵盖近期工程的IMG方法的调查;在本文件中,我们致力于进行系统和全面的调查,描述IMG的当代景观;侧重于110个初步的IMG方法,我们从多种角度进行了深入的分析和评价,包括算法的核心技术和应用范围、代理学习目标、数据类型、针对挑战、优势和局限性。为了根据内容提取的文献收集和分类,我们建议从关键技术、产出网格元素和适用的输入数据类型三种观点中进行三个不同的分类。最后,我们强调IMG中一些有希望的未来研究方向和挑战。为了最大限度地方便读者,IMG的项目网页可在以下网站提供:https://github.com/xzb030/IMG_Suvey}。