  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. Our
codebase can be accessed via the project page:
https://andreeadogaru.github.io/SphereGuided
