arXiv:2605.16813cs.GRcs.CV2026-05

用点关联生成可直接用于生产的细长四边形网格。

QuadLink: Autoregressive Quad-Dominant Mesh Generation via Point-Relation Learning

论文配图:QuadLink: Autoregressive Quad-Dominant Mesh Generation via Point-Relation Learning
图 1 · 摘自论文原文
  • 通过点与面心的关联建模,分三步生成结构化多边形网格。
  • 相比基线方法,生成网格更稀疏、方向性更强且拓扑质量更高。
  • 适合需要高质量四边形网格的工业级3D建模场景。

从点云生成适用于生产环境的四边形主导网格是现代3D内容创作的核心挑战。现有方法通常只能生成纯三角形或各向同性密度的四边形网格,难以处理各向异性需求。本文提出QuadLink,一个三阶段统一框架,通过点-面心关联生成结构化多边形面。该方法将网格生成建模为混合中心条件的顶点链接模型:先预测统一的锚点(顶点与面心),再学习中心条件下的链接关系,最后以四边形优先策略组装面,并通过鲁棒几何验证确保质量。为此,我们引入了三角形转四边形算子(Tri-to-Quad Operator),通过全局合并选择将艺术化三角网格转换为四边形主导训练数据。大量实验表明,QuadLink能从点云生成生产级四边形主导网格,在几何保真度和拓扑质量上优于先前基线。该方法天然支持混合多边形拓扑,无需架构调整即可推广至任意n边形网格。

原文摘要 · Abstract (English)

The generation of production-ready quad-dominant meshes is a cornerstone of modern 3D content creation. Generating anisotropic quad-dominant meshes from point clouds is challenging, as existing methods are typically limited to producing either pure triangular meshes or pure quadrilateral meshes with isotropic densities. In this paper, we present QuadLink, a unified framework consisting of three stages for quad-dominant mesh generation by linking points into structured faces. QuadLink formulates polygonal mesh generation as a hybrid centroid-conditioned vertex linking model: it first predicts a unified set of anchors (vertices and face centroids), then learns centroid-conditioned links that associate vertices with face centroids, and finally assembles polygonal faces with a quad-first strategy guided by robust geometric verification strategies. This link-based formulation enables efficient generation of sparse and anisotropic quad-dominant meshes with coherent edge flow and meanwhile supporting hybrid polygonal topology. To construct training data for this model, we further introduce a Tri-to-Quad Operator that converts artistic triangle meshes into quad-dominant training data via global merge selection. Extensive experiments show that QuadLink produces production-ready quad-dominant meshes from point clouds and achieves improved geometric fidelity and topological quality compared to prior baselines. Our method natively supports hybrid polygonal topology, generalizing to arbitrary n-gon meshes without architectural changes.

网格生成四边形主导点云处理3D建模

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