Neural implicit representation of geometric shapes has witnessed considerable
advancements in recent years. However, common distance field based implicit
representations, specifically signed distance field (SDF) for watertight shapes
or unsigned distance field (UDF) for arbitrary shapes, routinely suffer from
degradation of reconstruction accuracy when converting to explicit surface
points and meshes. In this paper, we introduce a novel neural implicit
representation based on unsigned orthogonal distance fields (UODFs). In UODFs,
the minimal unsigned distance from any spatial point to the shape surface is
defined solely in one orthogonal direction, contrasting with the
multi-directional determination made by SDF and UDF. Consequently, every point
in the 3D UODFs can directly access its closest surface points along three
orthogonal directions. This distinctive feature leverages the accurate
reconstruction of surface points without interpolation errors. We verify the
effectiveness of UODFs through a range of reconstruction examples, extending
from simple watertight or non-watertight shapes to complex shapes that include
hollows, internal or assembling structures.

基于无符号正交距离场的新型神经隐式表示法可实现准确重构曲面点而无需插值误差。