NeRF-SR: High-Quality Neural Radiance Fields using Supersampling (2112.01759v3)

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 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.

• The paper introduces a supersampling strategy that refines neural scene representations by shooting multiple rays per pixel to capture sub-pixel details.
• The patch-based refinement network uses high-resolution references and depth data to align and enhance low-res outputs, improving image fidelity.
• Experimental results on synthetic and real-world datasets demonstrate higher PSNR, SSIM, and lower LPIPS scores compared to baseline methods.

NeRF-SR: High Quality Neural Radiance Fields using Supersampling

This paper presents NeRF-SR, a method that significantly enhances the resolution and quality of novel view synthesis using Neural Radiance Fields (NeRF) when supplied with mostly low-resolution inputs. NeRF is a currently leading approach in novel view synthesis, using a multi-layer perceptron (MLP) to predict the per-point density and color for each 3D scene. Despite its ability to produce photorealistic images at the resolution of input training data, NeRF faces challenges when required to accurately synthesize views at higher resolutions than those observed during training.

Methodology and Contributions

The paper makes two major contributions to the task of super-resolution in NeRF:

1. Supersampling Strategy: The authors introduce a supersampling technique which capitalizes on the inherent 3D consistency present in NeRF's multi-view setting. By supersampling, the method is capable of both refining the neural scene representation and providing high-resolution renderings. This is achieved through shooting multiple rays per image pixel, thereby ensuring sub-pixel level constraints which help capture finer details across different views, ultimately improving the rendered output resolution and quality.
2. Patch-Based Refinement Network: To further enhance the synthesized image quality, especially when high-resolution imagery is scarce, the paper introduces a refinement network. This network leverages existing high-resolution references alongside depth information to infer finer details from related image patches. The refinement network effectively aligns and augments the low-resolution renderings to provide enhanced high-frequency details, resulting in images closer to high-resolution ground-truth references.

The method successfully exhibits superior performance over several envisioned baselines. These baselines range from naïve approaches like bilinear upsampling of NeRF outputs (NeRF-Bi) to more sophisticated image super-resolution techniques applied to NeRF outputs, like Liif and SwinIR. Quantitative evaluations demonstrate that NeRF-SR achieves higher PSNR and SSIM values and lower LPIPS scores compared to these baselines, indicating better quality and perceptual fidelity in the generated views.

Experimental Validation

Extensive experiments were conducted on both synthetic and real-world datasets (such as the LLFF and Blender datasets). The results consistently highlight the improvements in visual quality and sharpness of the synthesized high-resolution images by NeRF-SR compared to both the baseline NeRF and alternative super-resolution methods. Notably, the paper also provides insights into super-resolution rendering under varying conditions, such as differences in sampling characteristics between training and inference phases.

Practical and Theoretical Implications

NeRF-SR provides a substantial leap towards generating high-quality photorealistic images from low-resolution inputs using neural rendering. The discussed approaches have vast implications in fields requiring image synthesis and virtual reality (VR) applications, where high-resolution and consistent visual quality are paramount. Moreover, this work opens avenues for further research in enhancing neural scene representations with minimal input fidelity and the exploration of geometric consistency in higher dimensions.

Future Directions

The research identifies intriguing possibilities for future exploration, including the potential for a more generalized framework that can accommodate dynamic scenes or incorporate temporal consistency across frames in a video setting. A further investigation into adaptive sampling techniques or neural network architectures that inherently support multi-scale processing may extend the scalability and efficiency of the presented approach.

In summary, NeRF-SR pushes the boundaries of neural scene representation and rendering by addressing one of NeRF's critical limitations—resolution scaling—and sets the groundwork for ongoing advancements in high-fidelity neural rendering capabilities.