用向量场生成类艺术家风格的3D网格拓扑，更准更快。
TriFlow: Generating Artist-Like 3D Mesh Topology via Nearest-Vertex Vector Fields
- 将网格拓扑建模为局部重心系下的最近顶点向量场。
- 相比现有方法，拓扑质量显著提升，切比雪夫距离降低90%。
- 适合需要高质量拓扑的3D生成与几何处理场景。
我们提出TriFlow，一种从输入几何条件（如符号距离场）直接生成紧凑3D网格的新方法，其拓扑具有艺术家风格。核心思想是将网格拓扑表示为定义在表面的最近顶点向量场（NVF），每个点在局部重心坐标系中编码其关联的最近三角形顶点。我们训练了一个潜在流匹配模型来合成该向量场，实现基于输入几何的拓扑生成。为提取连贯网格，我们使用生成的NVF对表面区域进行聚类，并通过拓扑感知优化引导约束的二次误差度量（QEM）简化算法。结果输出的网格能精确匹配输入几何，同时展现出结构化的艺术化连接。实验表明，TriFlow在泛化能力上更强，拓扑质量显著优于现有学习方法，切比雪夫距离降低90%，速度提升8倍。
原文摘要 · Abstract (English)
We present TriFlow, a new generative approach for producing compact 3D meshes with artist-like triangle topology directly from input geometry conditions such as signed distance fields. Our key insight is to represent mesh topology as a nearest-vertex vector field (NVF) defined over the surface, where each point encodes its association to the nearest triangle vertex in the local barycentric frame. We train a latent flow-matching model to synthesize this field, enabling topology generation conditioned on the input geometry. To extract a coherent mesh, we cluster surface regions using the generated NVF and guide a constrained quadric error metric (QEM) mesh simplification with topology-aware optimization. This yields output meshes that closely match the input geometry while exhibiting structured, artist-like connectivity. Experiments demonstrate that TriFlow achieves stronger generalization and significantly improved topology quality compared to state-of-the-art learning-based approaches, alongside 90% lower Chamfer Distance and an 8x speedup.
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