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。