Neural Radiance Fields (NeRF) show impressive performance in photo-realistic
free-view rendering of scenes. Recent improvements on the NeRF such as TensoRF
and ZipNeRF employ explicit models for faster optimization and rendering, as
compared to the NeRF that employs an implicit representation. However, both
implicit and explicit radiance fields require dense sampling of images in the
given scene. Their performance degrades significantly when only a sparse set of
views is available. Researchers find that supervising the depth estimated by a
radiance field 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 main radiance field. Further,
we aim to design a framework of regularizations that can work across different
implicit and explicit radiance fields. We observe that certain features of
these radiance field models overfit to the observed images in the sparse-input
scenario. Our key finding is that reducing the capability of the radiance
fields with respect to positional encoding, the number of decomposed tensor
components or the size of the hash table, constrains the model to learn simpler
solutions, which estimate better depth in certain regions. By designing
augmented models based on such reduced capabilities, we obtain better depth
supervision for the main radiance field. We achieve state-of-the-art
view-synthesis performance with sparse input views on popular datasets
containing forward-facing and 360$^\circ$ scenes by employing the above
regularizations.

通过设计较为简化的能力增强模型，我们观察到较好的深度监督效果，进而提高稀疏视图下的主要辐射场的视图合成性能。