3D shapes from scanning, reconstruction, or AI-generated content often lack simple quad mesh layouts -- critical for efficient editing and modeling. Existing quad-remeshing techniques typically produce complex layouts with irregular loops, leading to tedious manual cleanup and extensive algorithm tuning. We introduce SQuadGen, a diffusion-based generative framework that leverages Chart Distance Fields (CDF) to synthesize simple quad layouts on 3D shapes. Our approach addresses two key challenges: (1) the discrete nature of mesh connectivity, which hinders learning, and (2) the scarcity of large-scale datasets with simple quad meshes. To overcome the first, we propose CDF, a continuous surface-based representation enabling effective learning and synthesis of quad layouts. To address the second, we define loop-aware simplicity metrics and construct a large-scale dataset of high-quality quad layouts recovered from public 3D repositories through a robust quad-recovery pipeline. Extensive evaluations across diverse 3D inputs show that SQuadGen consistently outperforms existing methods, producing robust, artist-friendly simple quad layouts.

翻译：来自扫描、重建或AI生成内容的3D形状通常缺乏简单的四边形网格布局——而这对于高效编辑和建模至关重要。现有的四边形重网格技术通常生成带有不规则回路的复杂布局，导致繁琐的手动清理和大量的算法调参。我们提出SQuadGen，一种基于扩散的生成框架，利用图表距离场（CDF）在3D形状上合成简单的四边形布局。我们的方法解决了两个关键挑战：（1）网格连接的离散性质限制了学习能力；（2）缺乏包含简单四边形网格的大规模数据集。针对第一个挑战，我们提出CDF（图表距离场），一种连续的基于表面的表示方法，能够有效学习和合成四边形布局。针对第二个挑战，我们定义了回路感知的简洁性度量，并通过稳健的四边形恢复流程从公开3D数据集中提取高质量的四边布局，构建大规模数据集。在多种3D输入上的广泛评估表明，SQuadGen始终优于现有方法，生成稳健且对艺术家友好的简单四边形布局。