  Obtaining high-quality 3D reconstructions of room-scale scenes is of
paramount importance for upcoming applications in AR or VR. These range from
mixed reality applications for teleconferencing, virtual measuring, virtual
room planing, to robotic applications. While current volume-based view
synthesis methods that use neural radiance fields (NeRFs) show promising
results in reproducing the appearance of an object or scene, they do not
reconstruct an actual surface. The volumetric representation of the surface
based on densities leads to artifacts when a surface is extracted using
Marching Cubes, since during optimization, densities are accumulated along the
ray and are not used at a single sample point in isolation. Instead of this
volumetric representation of the surface, we propose to represent the surface
using an implicit function (truncated signed distance function). We show how to
incorporate this representation in the NeRF framework, and extend it to use
depth measurements from a commodity RGB-D sensor, such as a Kinect. In
addition, we propose a pose and camera refinement technique which improves the
overall reconstruction quality. In contrast to concurrent work on integrating
depth priors in NeRF which concentrates on novel view synthesis, our approach
is able to reconstruct high-quality, metrical 3D reconstructions.
