用扩散模型生成3D形状的简洁四边形网格布局。
SQuadGen: Generating Simple Quad Layouts via Chart Distance Fields
- 基于图表距离场的连续表示，解决网格连接的离散学习难题。
- 构建大规模高质量四边形网格数据集，支持有效训练。
- 生成结果更简洁易用，适合设计师快速编辑3D模型。
通过扫描、重建或AI生成的3D形状通常缺乏简单的四边形网格布局——这对高效编辑与建模至关重要。现有四边形重网格化方法常产生复杂布局，包含不规则环路，导致繁琐的手动清理和算法调优。本文提出SQuadGen，一种基于扩散模型的生成框架，利用图表距离场（CDF）在3D形状上合成简洁四边形布局。针对两个关键挑战：(1) 网格连通性的离散性阻碍学习；(2) 缺乏大规模简洁四边形网格数据集。为此，我们提出CDF，一种基于连续表面的表示，可有效支持四边形布局的学习与生成；同时定义环路感知的简洁度指标，并通过稳健的四边形恢复流水线从公开3D资源库中构建大规模高质量四边形网格数据集。在多样化3D输入上的广泛评估表明，SQuadGen持续优于现有方法，生成鲁棒且艺术家友好的简洁四边形布局。
原文摘要 · Abstract (English)
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.
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