High-quality quadrilateral mesh generation is a fundamental challenge in computer graphics. Traditional optimization-based methods are often constrained by the topological quality of input meshes and suffer from severe efficiency bottlenecks, frequently becoming computationally prohibitive when handling high-resolution models. While emerging learning-based approaches offer greater flexibility, they primarily focus on cross-field prediction, often resulting in the loss of critical structural layouts and a lack of editability. In this paper, we propose TopGen, a robust and efficient learning-based framework that mimics professional manual modeling workflows by simultaneously predicting structural layouts and cross-fields. By processing input triangular meshes through point cloud sampling and a shape encoder, TopGen is inherently robust to non-manifold geometries and low-quality initial topologies. We introduce a dual-query decoder using edge-based and face-based sampling points as queries to perform structural line classification and cross-field regression in parallel. This integrated approach explicitly extracts the geometric skeleton while concurrently capturing orientation fields. Such synergy ensures the preservation of geometric integrity and provides an intuitive, editable foundation for subsequent quadrilateral remeshing. To support this framework, we also introduce a large-scale quadrilateral mesh dataset, TopGen-220K, featuring high-quality paired data comprising raw triangular meshes, structural layouts, cross-fields, and their corresponding quad meshes. Experimental results demonstrate that TopGen significantly outperforms existing state-of-the-art methods in both geometric fidelity and topological edge flow rationality.

翻译：高质量四边形网格生成是计算机图形学中的一个基础性挑战。传统基于优化的方法常受限于输入网格的拓扑质量，并存在严重的效率瓶颈，在处理高分辨率模型时计算成本往往过高。尽管新兴的基于学习的方法提供了更高的灵活性，但其主要聚焦于交叉场预测，常导致关键结构布局的丢失且缺乏可编辑性。本文提出TopGen——一个鲁棒且高效的基于学习框架，通过同步预测结构化布局与交叉场，模拟专业人工建模工作流。通过对输入三角网格进行点云采样与形状编码器处理，TopGen对非流形几何与低质量初始拓扑具有天然鲁棒性。我们引入一种双查询解码器，以基于边的采样点和基于面的采样点作为查询，并行执行结构线分类与交叉场回归。这种集成方法在捕获方向场的同时显式提取几何骨架，其协同作用确保了几何完整性的保持，并为后续四边形重网格化提供了直观可编辑的基础。为支撑该框架，我们还构建了大规模四边形网格数据集TopGen-220K，其中包含由原始三角网格、结构化布局、交叉场及其对应四边形网格构成的高质量配对数据。实验结果表明，TopGen在几何保真度与拓扑边流合理性方面均显著优于现有最先进方法。