In recent years, neural distance functions trained via volumetric ray marching
have been widely adopted for multi-view 3D reconstruction. These methods, however, apply the ray marching
procedure for the entire scene volume, leading to reduced sampling efficiency and, as a result, lower
reconstruction quality in the areas of high-frequency details. In this work, we address this problem via
joint training of the implicit function and our new coarse sphere-based surface reconstruction. We use the
coarse representation to efficiently exclude the empty volume of the scene from the volumetric ray
marching procedure without additional forward passes of the neural surface network, which leads to an
increased fidelity of the reconstructions compared to the base systems. We evaluate our approach by
incorporating it into the training procedures of several implicit surface modeling methods and observe
uniform improvements across both synthetic and real-world datasets.
