NeRF-SR: High-Quality Neural Radiance Fields using Super-Sampling

• URL: http://arxiv.org/abs/2112.01759v1
• Date: Fri, 3 Dec 2021 07:33:47 GMT
• Title: NeRF-SR: High-Quality Neural Radiance Fields using Super-Sampling
• Authors: Chen Wang, Xian Wu, Yuan-Chen Guo, Song-Hai Zhang, Yu-Wing Tai, Shi-Min Hu
• Abstract summary: 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.
• Score: 82.99453001445478
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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