Recent work on Neural Radiance Fields (NeRF) exploits multi-view 3D consistency, achieving impressive results in 3D scene modeling and high-fidelity novel-view synthesis. However, there are limitations. First, existing methods assume enough high-quality images are available for training the NeRF model, ignoring real-world image degradation. Second, previous methods struggle with ambiguity in the training set due to unmodeled inconsistencies among different views. In this work, we present RustNeRF for real-world high-quality NeRF. To improve NeRF's robustness under real-world inputs, we train a 3D-aware preprocessing network that incorporates real-world degradation modeling. We propose a novel implicit multi-view guidance to address information loss during image degradation and restoration. Extensive experiments demonstrate RustNeRF's advantages over existing approaches under real-world degradation. The code will be released.

翻译：近期关于神经辐射场（NeRF）的研究利用多视图三维一致性，在三维场景建模和高保真新视角合成中取得了显著成果。然而，这些方法存在局限性：首先，现有方法假设有足够的高质量图像可用于训练NeRF模型，忽视了真实世界中的图像退化；其次，由于不同视角间未被建模的不一致性，先前的方法在处理训练集中的歧义时面临挑战。本文提出RustNeRF以实现真实世界的高质量NeRF。为提升NeRF在真实输入下的鲁棒性，我们训练了一个融合真实退化建模的三维感知预处理网络，并创新性地提出隐式多视图引导机制，以解决图像退化与复原过程中的信息损失问题。大量实验表明，在真实退化条件下，RustNeRF相较于现有方法具有显著优势。代码将开源。