  Recently, Neural Radiance Fields (NeRF) have emerged as a potent method for
synthesizing novel views from a dense set of images. Despite its impressive
performance, NeRF is plagued by its necessity for numerous calibrated views and
its accuracy diminishes significantly in a few-shot setting. To address this
challenge, we propose Self-NeRF, a self-evolved NeRF that iteratively refines
the radiance fields with very few number of input views, without incorporating
additional priors. Basically, we train our model under the supervision of
reference and unseen views simultaneously in an iterative procedure. In each
iteration, we label unseen views with the predicted colors or warped pixels
generated by the model from the preceding iteration. However, these expanded
pseudo-views are afflicted by imprecision in color and warping artifacts, which
degrades the performance of NeRF. To alleviate this issue, we construct an
uncertainty-aware NeRF with specialized embeddings. Some techniques such as
cone entropy regularization are further utilized to leverage the pseudo-views
in the most efficient manner. Through experiments under various settings, we
verified that our Self-NeRF is robust to input with uncertainty and surpasses
existing methods when trained on limited training data.
