  Neural radiance fields (NeRF) encode a scene into a neural representation
that enables photo-realistic rendering of novel views. However, a successful
reconstruction from RGB images requires a large number of input views taken
under static conditions - typically up to a few hundred images for room-size
scenes. Our method aims to synthesize novel views of whole rooms from an order
of magnitude fewer images. To this end, we leverage dense depth priors in order
to constrain the NeRF optimization. First, we take advantage of the sparse
depth data that is freely available from the structure from motion (SfM)
preprocessing step used to estimate camera poses. Second, we use depth
completion to convert these sparse points into dense depth maps and uncertainty
estimates, which are used to guide NeRF optimization. Our method enables
data-efficient novel view synthesis on challenging indoor scenes, using as few
as 18 images for an entire scene.
