标题： MIND：基于UDFs的非流形曲面重建材料界面生成 显示英文标题

标题： MIND: Material Interface Generation from UDFs for Non-Manifold Surface Reconstruction

摘要： 无符号距离场（UDFs）由于能够表示具有任意拓扑的形状，在3D深度学习中得到了广泛应用。 尽管先前的研究主要集中在从点云或多视角图像中学习UDFs，但从中提取网格仍然具有挑战性，因为所学习的场很少能达到精确的零距离。 一种常见的解决方法是从UDFs局部重建符号距离场（SDFs），以通过Marching Cubes算法实现表面提取。 然而，这通常会引入拓扑伪影，例如孔洞或虚假组件。 此外，局部SDFs本质上无法表示非流形几何结构，在这种情况下会导致完全失败。 为了解决这一差距，我们提出了MIND（来自非流形距离场的材料界面），这是一种新颖的算法，可以直接从UDFs生成材料界面，从而从全局角度实现非流形网格提取。 我们的方法的核心在于从UDF推导出有意义的空间分区，其中目标表面作为不同区域之间的界面出现。 我们首先计算一个双符号局部场来区分流形补丁的两侧，然后将其扩展为一个多标签全局场，能够分离非流形结构的所有侧。 通过结合这个多标签场和输入的UDF，我们构建了材料界面，可以通过多标签Marching Cubes算法支持非流形网格提取。 在来自不同数据源的UDFs上的广泛实验表明，包括点云重建、多视角重建和中轴变换，证明我们的方法稳健地处理复杂的非流形表面，并显著优于现有方法。 显示英文摘要

摘要： Unsigned distance fields (UDFs) are widely used in 3D deep learning due to their ability to represent shapes with arbitrary topology. While prior work has largely focused on learning UDFs from point clouds or multi-view images, extracting meshes from UDFs remains challenging, as the learned fields rarely attain exact zero distances. A common workaround is to reconstruct signed distance fields (SDFs) locally from UDFs to enable surface extraction via Marching Cubes. However, this often introduces topological artifacts such as holes or spurious components. Moreover, local SDFs are inherently incapable of representing non-manifold geometry, leading to complete failure in such cases. To address this gap, we propose MIND (Material Interface from Non-manifold Distance fields), a novel algorithm for generating material interfaces directly from UDFs, enabling non-manifold mesh extraction from a global perspective. The core of our method lies in deriving a meaningful spatial partitioning from the UDF, where the target surface emerges as the interface between distinct regions. We begin by computing a two-signed local field to distinguish the two sides of manifold patches, and then extend this to a multi-labeled global field capable of separating all sides of a non-manifold structure. By combining this multi-labeled field with the input UDF, we construct material interfaces that support non-manifold mesh extraction via a multi-labeled Marching Cubes algorithm. Extensive experiments on UDFs generated from diverse data sources, including point cloud reconstruction, multi-view reconstruction, and medial axis transforms, demonstrate that our approach robustly handles complex non-manifold surfaces and significantly outperforms existing methods.