用无符号对偶轮廓法生成高质量3D网格，兼顾复杂结构与精细表面。
GenUDC: High Quality 3D Mesh Generation with Unsigned Dual Contouring Representation
- 采用无符号对偶轮廓表示，分离面与顶点信息以避免变形网格歧义
- 两阶段生成：先粗略生成面部结构，再细化顶点位置恢复细节
- 在复杂结构和真实表面重建上优于现有方法，适合高精度3D建模应用
生成具有复杂结构和真实表面的高质量网格是3D生成模型的核心目标。现有方法通常使用序列数据或可变形四面体网格进行网格生成，但序列方法因内存限制难以生成多面体复杂结构，而基于可变形四面体网格的方法如MeshDiffusion因网格固有的歧义性无法恢复真实表面。本文提出GenUDC框架，利用无符号对偶轮廓（UDC）作为网格表示，将网格离散化到规则网格中并分为面与顶点两部分，从而同时恢复复杂结构与精细细节。该表示实现了与网格的一一对应，解决了歧义问题。此外，GenUDC采用两阶段、从粗到精的生成流程：先生成面部分构建粗略形状，再生成顶点部分细化为详细形状。大量实验验证了UDC作为网格表示的优势及GenUDC在网格生成中的优越性能。代码与训练模型已开源于https://github.com/TrepangCat/GenUDC。
原文摘要 · Abstract (English)
Generating high-quality meshes with complex structures and realistic surfaces is the primary goal of 3D generative models. Existing methods typically employ sequence data or deformable tetrahedral grids for mesh generation. However, sequence-based methods have difficulty producing complex structures with many faces due to memory limits. The deformable tetrahedral grid-based method MeshDiffusion fails to recover realistic surfaces due to the inherent ambiguity in deformable grids. We propose the GenUDC framework to address these challenges by leveraging the Unsigned Dual Contouring (UDC) as the mesh representation. UDC discretizes a mesh in a regular grid and divides it into the face and vertex parts, recovering both complex structures and fine details. As a result, the one-to-one mapping between UDC and mesh resolves the ambiguity problem. In addition, GenUDC adopts a two-stage, coarse-to-fine generative process for 3D mesh generation. It first generates the face part as a rough shape and then the vertex part to craft a detailed shape. Extensive evaluations demonstrate the superiority of UDC as a mesh representation and the favorable performance of GenUDC in mesh generation. The code and trained models are available at https://github.com/TrepangCat/GenUDC.
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