用自回归模型直接生成拓扑优良的四边形网格，打破传统三角形转四边形的局限。
QuadGPT: Native Quadrilateral Mesh Generation with Autoregressive Models
- 将四边形网格生成建模为序列预测，统一处理三角与四边形混合拓扑。
- 在几何精度和拓扑质量上显著优于现有三角转四边形方法。
- 适合需要高质量结构化3D资产的工业设计与游戏开发人员。
四边形主导的网格生成是专业3D内容创作的核心。然而，现有生成模型通常先生成三角网格，再通过特定规则合并为四边形，导致拓扑质量差。本文提出QuadGPT，首个端到端的自回归四边形网格生成框架。该方法将生成任务建模为序列预测，引入两项关键创新：统一的标记化方法以处理三角与四边形混合拓扑，以及针对生成质量优化的专用强化学习微调方法tDPO。大量实验表明，QuadGPT在几何精度与拓扑质量上均显著超越以往的三角转四边形流程。本工作建立了原生四边形网格生成的新基准，展示了大规模自回归模型结合拓扑感知强化学习微调在构建结构化3D资产方面的强大能力。
原文摘要 · Abstract (English)
The generation of quadrilateral-dominant meshes is a cornerstone of professional 3D content creation. However, existing generative models generate quad meshes by first generating triangle meshes and then merging triangles into quadrilaterals with some specific rules, which typically produces quad meshes with poor topology. In this paper, we introduce QuadGPT, the first autoregressive framework for generating quadrilateral meshes in an end-to-end manner. QuadGPT formulates this as a sequence prediction paradigm, distinguished by two key innovations: a unified tokenization method to handle mixed topologies of triangles and quadrilaterals, and a specialized Reinforcement Learning fine-tuning method tDPO for better generation quality. Extensive experiments demonstrate that QuadGPT significantly surpasses previous triangle-to-quad conversion pipelines in both geometric accuracy and topological quality. Our work establishes a new benchmark for native quad-mesh generation and showcases the power of combining large-scale autoregressive models with topology-aware RL refinement for creating structured 3D assets.
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