  We present an information-theoretic regularization technique for few-shot
novel view synthesis based on neural implicit representation. The proposed
approach minimizes potential reconstruction inconsistency that happens due to
insufficient viewpoints by imposing the entropy constraint of the density in
each ray. In addition, to alleviate the potential degenerate issue when all
training images are acquired from almost redundant viewpoints, we further
incorporate the spatially smoothness constraint into the estimated images by
restricting information gains from a pair of rays with slightly different
viewpoints. The main idea of our algorithm is to make reconstructed scenes
compact along individual rays and consistent across rays in the neighborhood.
The proposed regularizers can be plugged into most of existing neural volume
rendering techniques based on NeRF in a straightforward way. Despite its
simplicity, we achieve consistently improved performance compared to existing
neural view synthesis methods by large margins on multiple standard benchmarks.
