While Signed Distance Fields (SDF) are well-established for modeling watertight surfaces, Unsigned Distance Fields (UDF) broaden the scope to include open surfaces and models with complex inner structures. Despite their flexibility, UDFs encounter significant challenges in high-fidelity 3D reconstruction, such as non-differentiability at the zero level set, difficulty in achieving the exact zero value, numerous local minima, vanishing gradients, and oscillating gradient directions near the zero level set. To address these challenges, we propose Details Enhanced UDF (DEUDF) learning that integrates normal alignment and the SIREN network for capturing fine geometric details, adaptively weighted Eikonal constraints to address vanishing gradients near the target surface, unconditioned MLP-based UDF representation to relax non-negativity constraints, and a UDF-tailored method for extracting iso-surface with non-constant iso-values. These strategies collectively stabilize the learning process from unoriented point clouds and enhance the accuracy of UDFs. Our computational results demonstrate that DEUDF outperforms existing UDF learning methods in both accuracy and the quality of reconstructed surfaces. We will make the source code publicly available.

我们提出了一种Details Enhanced UDF (DEUDF)学习方法，通过集成法线对齐和SIREN网络来捕获精细的几何细节，自适应加权的等距约束方法来解决目标表面附近梯度消失的问题，基于MLP的UDF表示方法来放宽非负约束，以及一种专门针对UDF的非常量等值面提取方法，这些策略共同稳定了从未定向点云中进行学习的过程并提高了UDF的准确性。我们的计算结果表明，DEUDF在准确性和重建曲面质量方面优于现有的UDF学习方法。我们将公开源代码。

无符号距离场学习中的细节增强用于高保真度3D表面重建