HSDF: Hybrid Sign and Distance Field for Modeling Surfaces with Arbitrary Topologies

. Abstract Neural implicit function based on signed distance field (SDF) has achieved impressive progress in reconstructing 3D models with high fidelity. However, such approaches can only represent closed surfaces. Recent works based on unsigned distance function (UDF) are proposed to handle both watertight and open surfaces. Nonetheless, as UDF is signless, its direct output is limited to the point cloud, which imposes an additional challenge on extracting high-quality meshes from discrete points. To address this challenge, we present a novel neural implicit representation coded HSDF, which is a hybrid of signed and unsigned distance fields. In particular, HSDF is able to represent arbitrary topologies containing both closed and open surfaces while being compatible with existing iso-surface extraction techniques for easy field-to-mesh conversion. In addition to predicting a UDF, we propose to learn an additional sign field. Unlike traditional SDF, HSDF is able to locate the surface of interest before level surface extraction by generating surface points following NDF [15]. We are then able to obtain open surfaces via an adaptive meshing approach that only instantiates regions containing surfaces into a polygon