Intelligent mesh generation (IMG) refers to a technique for generating mesh by machine learning, which is a relatively new and promising research field. Within its short lifespan, IMG has greatly expanded the generalizability and practicality of mesh generation techniques, achieved many breakthroughs and created potential possibilities for mesh generation. However, there is a lack of surveys that focus on IMG methods in recent works. In this paper, we are committed to a systematic and comprehensive survey that describes the contemporary IMG landscape. Focusing on 113 preliminary IMG methods, we conducted an in-depth analysis 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: key techniques, output mesh unit elements, and applicable input data types. 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）是指利用机器学习技术生成网格的方法，这是一个相对新颖且前景广阔的研究领域。在其短暂的发展历程中，IMG极大地拓展了网格生成技术的泛化能力和实用性，取得了多项突破性进展，并为网格生成创造了潜在可能。然而，近年来缺乏专门针对IMG方法的综述性研究。本文致力于呈现一幅系统全面的当代IMG研究图景。聚焦于113种基础IMG方法，我们从多个维度进行了深入分析，包括算法的核心技术及应用范围、智能体学习目标、数据类型、针对挑战、优势与局限性。基于文献收集与内容分类的目的，我们提出了三种不同视角的分类体系：关键技术、输出网格单元类型及适用输入数据类型。同时，我们指出了IMG领域若干前景广阔的未来研究方向与挑战。为最大程度方便读者，本文相关项目页面已发布于\url{https://github.com/xzb030/IMG_Survey}。