In this paper, we propose a new method, called DoubleCoverUDF, for extracting the zero level-set from unsigned distance fields (UDFs). DoubleCoverUDF takes a learned UDF and a user-specified parameter $r$ (a small positive real number) as input and extracts an iso-surface with an iso-value $r$ using the conventional marching cubes algorithm. We show that the computed iso-surface is the boundary of the $r$-offset volume of the target zero level-set $S$, which is an orientable manifold, regardless of the topology of $S$. Next, the algorithm computes a covering map to project the boundary mesh onto $S$, preserving the mesh's topology and avoiding folding. If $S$ is an orientable manifold surface, our algorithm separates the double-layered mesh into a single layer using a robust minimum-cut post-processing step. Otherwise, it keeps the double-layered mesh as the output. We validate our algorithm by reconstructing 3D surfaces of open models and demonstrate its efficacy and effectiveness on synthetic models and benchmark datasets. Our experimental results confirm that our method is robust and produces meshes with better quality in terms of both visual evaluation and quantitative measures than existing UDF-based methods. The source code is available at https://github.com/jjjkkyz/DCUDF.

本文提出了一种名为DoubleCoverUDF的新方法，用于从无符号距离场（UDF）中提取零水平集。该方法通过使用标准的Marching Cubes算法，使用学习得到的UDF和用户指定的参数$r$提取具有等值$r$的等值面。实验证明，计算得到的等值面是目标零水平集$S$的$r$-偏移体的边界，而不管$S$的拓扑结构。接下来，算法通过计算一个覆盖映射将边界网格投影到$S$上，保持网格的拓扑结构并避免折叠。如果$S$是一个可定向流形表面，我们的算法可以通过鲁棒的最小割后处理步骤将双层网格分割成单层。通过对开放模型的三维表面重建以及对合成模型和基准数据集的实验证明了我们算法的有效性和效果。实验结果表明，与现有的基于UDF的方法相比，我们的方法具有更好的视觉评估和定量指标。源代码可以在此https URL找到。

基于双覆盖的无符号距离场鲁棒零级集提取