  Neural radiance fields (NeRFs) have enabled high fidelity 3D reconstruction
from multiple 2D input views. However, a well-known drawback of NeRFs is the
less-than-ideal performance under a small number of views, due to insufficient
constraints enforced by volumetric rendering. To address this issue, we
introduce SCADE, a novel technique that improves NeRF reconstruction quality on
sparse, unconstrained input views for in-the-wild indoor scenes. To constrain
NeRF reconstruction, we leverage geometric priors in the form of per-view depth
estimates produced with state-of-the-art monocular depth estimation models,
which can generalize across scenes. A key challenge is that monocular depth
estimation is an ill-posed problem, with inherent ambiguities. To handle this
issue, we propose a new method that learns to predict, for each view, a
continuous, multimodal distribution of depth estimates using conditional
Implicit Maximum Likelihood Estimation (cIMLE). In order to disambiguate
exploiting multiple views, we introduce an original space carving loss that
guides the NeRF representation to fuse multiple hypothesized depth maps from
each view and distill from them a common geometry that is consistent with all
views. Experiments show that our approach enables higher fidelity novel view
synthesis from sparse views. Our project page can be found at
https://scade-spacecarving-nerfs.github.io .
