  In recent years, neural implicit surface reconstruction methods have become
popular for multi-view 3D reconstruction. In contrast to traditional multi-view
stereo methods, these approaches tend to produce smoother and more complete
reconstructions due to the inductive smoothness bias of neural networks.
State-of-the-art neural implicit methods allow for high-quality reconstructions
of simple scenes from many input views. Yet, their performance drops
significantly for larger and more complex scenes and scenes captured from
sparse viewpoints. This is caused primarily by the inherent ambiguity in the
RGB reconstruction loss that does not provide enough constraints, in particular
in less-observed and textureless areas. Motivated by recent advances in the
area of monocular geometry prediction, we systematically explore the utility
these cues provide for improving neural implicit surface reconstruction. We
demonstrate that depth and normal cues, predicted by general-purpose monocular
estimators, significantly improve reconstruction quality and optimization time.
Further, we analyse and investigate multiple design choices for representing
neural implicit surfaces, ranging from monolithic MLP models over single-grid
to multi-resolution grid representations. We observe that geometric monocular
priors improve performance both for small-scale single-object as well as
large-scale multi-object scenes, independent of the choice of representation.
