  Neural Radiance Fields (NeRF) have been proposed for photorealistic novel
view rendering. However, it requires many different views of one scene for
training. Moreover, it has poor generalizations to new scenes and requires
retraining or fine-tuning on each scene. In this paper, we develop a new NeRF
model for novel view synthesis using only a single image as input. We propose
to combine the (coarse) planar rendering and the (fine) volume rendering to
achieve higher rendering quality and better generalizations. We also design a
depth teacher net that predicts dense pseudo depth maps to supervise the joint
rendering mechanism and boost the learning of consistent 3D geometry. We
evaluate our method on three challenging datasets. It outperforms
state-of-the-art single-view NeRFs by achieving 5$\sim$20\% improvements in
PSNR and reducing 20$\sim$50\% of the errors in the depth rendering. It also
shows excellent generalization abilities to unseen data without the need to
fine-tune on each new scene.
