  Neural radiance fields (NeRF) have achieved impressive performances in view
synthesis by encoding neural representations of a scene. However, NeRFs require
hundreds of images per scene to synthesize photo-realistic novel views.
Training them on sparse input views leads to overfitting and incorrect scene
depth estimation resulting in artifacts in the rendered novel views. Sparse
input NeRFs were recently regularized by providing dense depth estimated from
pre-trained networks as supervision, to achieve improved performance over
sparse depth constraints. However, we find that such depth priors may be
inaccurate due to generalization issues. Instead, we hypothesize that the
visibility of pixels in different input views can be more reliably estimated to
provide dense supervision. In this regard, we compute a visibility prior
through the use of plane sweep volumes, which does not require any
pre-training. By regularizing the NeRF training with the visibility prior, we
successfully train the NeRF with few input views. We reformulate the NeRF to
also directly output the visibility of a 3D point from a given viewpoint to
reduce the training time with the visibility constraint. On multiple datasets,
our model outperforms the competing sparse input NeRF models including those
that use learned priors. The source code for our model can be found on our
project page:
https://nagabhushansn95.github.io/publications/2023/ViP-NeRF.html.
