  We present NeRF-SR, a solution for high-resolution (HR) novel view synthesis
with mostly low-resolution (LR) inputs. Our method is built upon Neural
Radiance Fields (NeRF) that predicts per-point density and color with a
multi-layer perceptron. While producing images at arbitrary scales, NeRF
struggles with resolutions that go beyond observed images. Our key insight is
that NeRF benefits from 3D consistency, which means an observed pixel absorbs
information from nearby views. We first exploit it by a supersampling strategy
that shoots multiple rays at each image pixel, which further enforces
multi-view constraint at a sub-pixel level. Then, we show that NeRF-SR can
further boost the performance of supersampling by a refinement network that
leverages the estimated depth at hand to hallucinate details from related
patches on only one HR reference image. Experiment results demonstrate that
NeRF-SR generates high-quality results for novel view synthesis at HR on both
synthetic and real-world datasets without any external information.
