  Neural Radiance Field (NeRF) has shown impressive results in novel view
synthesis, particularly in Virtual Reality (VR) and Augmented Reality (AR),
thanks to its ability to represent scenes continuously. However, when just a
few input view images are available, NeRF tends to overfit the given views and
thus make the estimated depths of pixels share almost the same value. Unlike
previous methods that conduct regularization by introducing complex priors or
additional supervisions, we propose a simple yet effective method that
explicitly builds depth-aware consistency across input views to tackle this
challenge. Our key insight is that by forcing the same spatial points to be
sampled repeatedly in different input views, we are able to strengthen the
interactions between views and therefore alleviate the overfitting problem. To
achieve this, we build the neural networks on layered representations
(\textit{i.e.}, multiplane images), and the sampling point can thus be
resampled on multiple discrete planes. Furthermore, to regularize the unseen
target views, we constrain the rendered colors and depths from different input
views to be the same. Although simple, extensive experiments demonstrate that
our proposed method can achieve better synthesis quality over state-of-the-art
methods.
