GenUDC：基于无符号对偶轮廓表示的高质量三维网格生成
GenUDC: High Quality 3D Mesh Generation with Unsigned Dual Contouring Representation
AI总结：
GenUDC提出以无符号对偶轮廓（UDC）作为网格表示，通过面部分与顶点部分的两阶段由粗到细生成过程，解决现有方法在复杂结构和逼真表面恢复上的不足，实现高质量三维网格生成。
摘要：
生成具有复杂结构和逼真表面的高质量网格是三维生成模型的主要目标。现有方法通常采用序列数据或可变形四面体网格进行网格生成。然而，基于序列的方法由于内存限制，难以生成具有大量面片的复杂结构。基于可变形四面体网格的方法MeshDiffusion由于可变形网格中固有的歧义性，无法恢复逼真的表面。我们提出GenUDC框架来应对这些挑战，利用无符号对偶轮廓（Unsigned Dual Contouring，UDC）作为网格表示。UDC在规则网格中离散化网格，并将其分为面部分和顶点部分，从而同时恢复复杂结构和精细细节。因此，UDC与网格之间的一对一映射解决了歧义问题。此外，GenUDC采用两阶段的、由粗到细的生成过程进行三维网格生成。它首先生成面部分作为粗略形状，然后生成顶点部分以构建精细形状。大量评估证明了UDC作为网格表示的优越性以及GenUDC在网格生成中的良好性能。代码和训练模型可在https://github.com/TrepangCat/GenUDC获取。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
