  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.
