  In this work, we explore how to leverage the success of implicit novel view
synthesis methods for surface reconstruction. Methods which learn a neural
radiance field have shown amazing image synthesis results, but the underlying
geometry representation is only a coarse approximation of the real geometry. We
demonstrate how depth measurements can be incorporated into the radiance field
formulation to produce more detailed and complete reconstruction results than
using methods based on either color or depth data alone. In contrast to a
density field as the underlying geometry representation, we propose to learn a
deep neural network which stores a truncated signed distance field. Using this
representation, we show that one can still leverage differentiable volume
rendering to estimate color values of the observed images during training to
compute a reconstruction loss. This is beneficial for learning the signed
distance field in regions with missing depth measurements. Furthermore, we
correct misalignment errors of the camera, improving the overall reconstruction
quality. In several experiments, we showcase our method and compare to existing
works on classical RGB-D fusion and learned representations.
