QuadLink: 通过点关系学习的自回归四边形主导网格生成
QuadLink: Autoregressive Quad-Dominant Mesh Generation via Point-Relation Learning
AI总结：
提出QuadLink框架，通过将点云链接成结构化面片，以自回归方式生成各向异性的四边形主导网格，实现高几何保真度和拓扑质量。
摘要：
生成可用于生产的四边形主导网格是现代3D内容创作的基石。从点云生成各向异性的四边形主导网格具有挑战性，因为现有方法通常局限于生成纯三角形网格或具有各向同性密度的纯四边形网格。在本文中，我们提出QuadLink，一个由三个阶段组成的统一框架，通过将点链接成结构化面片来生成四边形主导网格。QuadLink将多边形网格生成公式化为混合质心条件顶点链接模型：它首先预测一组统一的锚点（顶点和面质心），然后学习将顶点与面质心关联的质心条件链接，最后通过鲁棒的几何验证策略引导的四边形优先策略组装多边形面。这种基于链接的公式能够高效生成具有连贯边流的稀疏各向异性四边形主导网格，同时支持混合多边形拓扑。为了构建该模型的训练数据，我们进一步引入三角到四边形算子，通过全局合并选择将艺术三角形网格转换为四边形主导训练数据。大量实验表明，QuadLink从点云生成可用于生产的四边形主导网格，与先前基线相比，实现了更高的几何保真度和拓扑质量。我们的方法原生支持混合多边形拓扑，无需架构更改即可推广到任意n边形网格。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
