The neural radiance field (NeRF) achieved remarkable success in modeling 3D scenes and synthesizing high-fidelity novel views. However, existing NeRF-based methods focus more on the make full use of the image resolution to generate novel views, but less considering the generation of details under the limited input resolution. In analogy to the extensive usage of image super-resolution, NeRF super-resolution is an effective way to generate the high-resolution implicit representation of 3D scenes and holds great potential applications. Up to now, such an important topic is still under-explored. In this paper, we propose a NeRF super-resolution method, named Super-NeRF, to generate high-resolution NeRF from only low-resolution inputs. Given multi-view low-resolution images, Super-NeRF constructs a consistency-controlling super-resolution module to generate view-consistent high-resolution details for NeRF. Specifically, an optimizable latent code is introduced for each low-resolution input image to control the 2D super-resolution images to converge to the view-consistent output. The latent codes of each low-resolution image are optimized synergistically with the target Super-NeRF representation to fully utilize the view consistency constraint inherent in NeRF construction. We verify the effectiveness of Super-NeRF on synthetic, real-world, and AI-generated NeRF datasets. Super-NeRF achieves state-of-the-art NeRF super-resolution performance on high-resolution detail generation and cross-view consistency.

翻译：神经辐射场在三维场景建模与高保真新视角合成方面取得了显著成功。然而，现有基于NeRF的方法主要关注充分利用图像分辨率生成新视角，较少考虑在有限输入分辨率下的细节生成。类比于图像超分辨率的广泛应用，NeRF超分辨率是生成三维场景高分辨率隐式表示的有效途径，具有广阔的应用前景。迄今为止，这一重要课题仍未被充分探索。本文提出名为Super-NeRF的NeRF超分辨率方法，仅需低分辨率输入即可生成高分辨率NeRF。给定多视角低分辨率图像，Super-NeRF构建一致性控制超分辨率模块，为NeRF生成视角一致的高分辨率细节。具体而言，为每张低分辨率输入图像引入可优化的隐编码，以控制二维超分辨率图像收敛至视角一致输出。通过协同优化各低分辨率图像的隐编码与目标Super-NeRF表示，充分利用NeRF构建中固有的视角一致性约束。我们在合成、真实世界及AI生成的NeRF数据集上验证了Super-NeRF的有效性。Super-NeRF在高分辨率细节生成与跨视角一致性方面达到了最先进的NeRF超分辨率性能。