  A commonly observed failure mode of Neural Radiance Field (NeRF) is fitting
incorrect geometries when given an insufficient number of input views. One
potential reason is that standard volumetric rendering does not enforce the
constraint that most of a scene's geometry consist of empty space and opaque
surfaces. We formalize the above assumption through DS-NeRF (Depth-supervised
Neural Radiance Fields), a loss for learning radiance fields that takes
advantage of readily-available depth supervision. We leverage the fact that
current NeRF pipelines require images with known camera poses that are
typically estimated by running structure-from-motion (SFM). Crucially, SFM also
produces sparse 3D points that can be used as "free" depth supervision during
training: we add a loss to encourage the distribution of a ray's terminating
depth matches a given 3D keypoint, incorporating depth uncertainty. DS-NeRF can
render better images given fewer training views while training 2-3x faster.
Further, we show that our loss is compatible with other recently proposed NeRF
methods, demonstrating that depth is a cheap and easily digestible supervisory
signal. And finally, we find that DS-NeRF can support other types of depth
supervision such as scanned depth sensors and RGB-D reconstruction outputs.
