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 has a local prior, which means predictions of a 3D point can be propagated in the nearby region and remain accurate. We first exploit it by a super-sampling strategy that shoots multiple rays at each image pixel, which enforces multi-view constraint at a sub-pixel level. Then, we show that NeRF-SR can further boost the performance of super-sampling by a refinement network that leverages the estimated depth at hand to hallucinate details from related patches on an 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.

翻译：我们提出NeRF-SR,这是高分辨率(HR)新观点合成的解决方案,大部分是低分辨率(LR)输入。我们的方法是建立在神经辐射场上,用多层光谱预测每点密度和颜色。在任意制作图像时,NERF与超出观察图像的分辨率搏斗。我们的关键洞察力是NERF有一个本地的先行,这意味着对3D点的预测可以在附近区域传播并保持准确性。我们首先利用超抽样战略,对每个图像像素进行多射线拍摄,在次像素水平上执行多视限制。然后,我们表明NERF-SR可以通过一个精细网络进一步提高超光谱取样的性能,通过一个精细化网络,将估计的手边深度用于有关HR参考图像上的相片段的致癌细节。实验结果表明,NRF-SR产生高质量的结果,用于在合成和真实世界数据集的HR新视图合成。