Intelligent Mesh Generation (IMG) represents a novel and promising field of research, utilizing machine learning techniques to generate meshes. Despite its relative infancy, IMG has significantly broadened the adaptability and practicality of mesh generation techniques, delivering numerous breakthroughs and unveiling potential future pathways. However, a noticeable void exists in the contemporary literature concerning comprehensive surveys of IMG methods. This paper endeavors to fill this gap by providing a systematic and thorough survey of the current IMG landscape. With a focus on 113 preliminary IMG methods, we undertake a meticulous analysis from various angles, encompassing core algorithm techniques and their application scope, agent learning objectives, data types, targeted challenges, as well as advantages and limitations. We have curated and categorized the literature, proposing three unique taxonomies based on key techniques, output mesh unit elements, and relevant input data types. This paper also underscores several promising future research directions and challenges in IMG. To augment reader accessibility, a dedicated IMG project page is available at \url{https://github.com/xzb030/IMG_Survey}.

翻译：智能网格生成（IMG）是一个新颖且前景广阔的研究领域，利用机器学习技术生成网格。尽管仍处于相对早期阶段，IMG已显著拓展了网格生成技术的适应性和实用性，取得了多项突破并揭示了潜在的发展路径。然而，当前文献中缺乏对IMG方法的系统性综述，这一空白亟待填补。本文旨在通过系统而全面的综述来弥补这一不足。我们聚焦于113种代表性IMG方法，从核心算法技术及其应用范围、智能体学习目标、数据类型、所针对的挑战以及优势与局限性等多个角度进行了详尽分析。我们整理并分类了相关文献，基于关键技术、输出网格单元类型以及相关输入数据类型提出了三种独特的分类体系。本文还指出了IMG领域若干有前景的未来研究方向与挑战。为方便读者查阅，我们在\url{https://github.com/xzb030/IMG_Survey}上提供了专用的IMG项目页面。