What's the Situation with Intelligent Mesh Generation: A Survey and Perspectives
It provides a foundational resource for researchers in computational geometry and machine learning by filling a gap in literature, though it is incremental as a survey paper.
This paper addresses the lack of comprehensive surveys in Intelligent Mesh Generation (IMG) by systematically analyzing 113 methods, categorizing them into taxonomies based on techniques, output elements, and input data types, and identifying future research directions.
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}.