What's the Situation with Intelligent Mesh Generation: A Survey and Perspectives

TL;DR

This survey comprehensively reviews the emerging field of Intelligent Mesh Generation, analyzing 113 methods, categorizing techniques, and highlighting future research directions in machine learning-based mesh generation.

Contribution

It provides the first systematic survey of IMG, categorizing methods and proposing taxonomies, thus filling a significant gap in the literature.

Findings

Analyzed 113 IMG methods across various techniques and applications.

Proposed three taxonomies based on techniques, mesh units, and data types.

Identified key challenges and promising future research directions.

Abstract

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}.