  Neural Radiance Fields (NeRF) show impressive performance for the
photorealistic free-view rendering of scenes. However, NeRFs require dense
sampling of images in the given scene, and their performance degrades
significantly when only a sparse set of views are available. Researchers have
found that supervising the depth estimated by the NeRF helps train it
effectively with fewer views. The depth supervision is obtained either using
classical approaches or neural networks pre-trained on a large dataset. While
the former may provide only sparse supervision, the latter may suffer from
generalization issues. As opposed to the earlier approaches, we seek to learn
the depth supervision by designing augmented models and training them along
with the NeRF. We design augmented models that encourage simpler solutions by
exploring the role of positional encoding and view-dependent radiance in
training the few-shot NeRF. The depth estimated by these simpler models is used
to supervise the NeRF depth estimates. Since the augmented models can be
inaccurate in certain regions, we design a mechanism to choose only reliable
depth estimates for supervision. Finally, we add a consistency loss between the
coarse and fine multi-layer perceptrons of the NeRF to ensure better
utilization of hierarchical sampling. We achieve state-of-the-art
view-synthesis performance on two popular datasets by employing the above
regularizations. The source code for our model can be found on our project
page: https://nagabhushansn95.github.io/publications/2023/SimpleNeRF.html
