Neural radiance fields (NeRF) have shown great success in novel view synthesis. However, recovering high-quality details from real-world scenes is still challenging for the existing NeRF-based approaches, due to the potential imperfect calibration information and scene representation inaccuracy. Even with high-quality training frames, the synthetic novel views produced by NeRF models still suffer from notable rendering artifacts, such as noise and blur. To address this, we propose NeRFLiX, a general NeRF-agnostic restorer paradigm that learns a degradation-driven inter-viewpoint mixer. Specially, we design a NeRF-style degradation modeling approach and construct large-scale training data, enabling the possibility of effectively removing NeRF-native rendering artifacts for deep neural networks. Moreover, beyond the degradation removal, we propose an inter-viewpoint aggregation framework that fuses highly related high-quality training images, pushing the performance of cutting-edge NeRF models to entirely new levels and producing highly photo-realistic synthetic views. Based on this paradigm, we further present NeRFLiX++ with a stronger two-stage NeRF degradation simulator and a faster inter-viewpoint mixer, achieving superior performance with significantly improved computational efficiency. Notably, NeRFLiX++ is capable of restoring photo-realistic ultra-high-resolution outputs from noisy low-resolution NeRF-rendered views. Extensive experiments demonstrate the excellent restoration ability of NeRFLiX++ on various novel view synthesis benchmarks.

翻译：神经辐射场（NeRF）在新视角合成方面取得了巨大成功。然而，由于潜在的标定信息不完善和场景表示不精确，现有基于NeRF的方法在从真实场景中恢复高质量细节方面仍面临挑战。即使使用高质量的训练帧，NeRF模型生成的合成新视角仍存在显著的渲染伪影，如噪声和模糊。为解决这一问题，我们提出NeRFLiX，一种通用的NeRF无关修复范式，它学习一个退化驱动的视角间混合器。具体而言，我们设计了一种NeRF风格的退化建模方法，并构建了大规模训练数据，使得深度神经网络能够有效去除NeRF原生渲染伪影成为可能。此外，除了去除退化外，我们还提出了一种视角间聚合框架，该框架融合高度相关的训练图像，将前沿NeRF模型的性能提升至全新水平，并生成高逼真度的合成视角。基于该范式，我们进一步提出了NeRFLiX++，它配备更强的两阶段NeRF退化模拟器和更快的视角间混合器，在显著提高计算效率的同时实现了卓越性能。值得注意的是，NeRFLiX++能够从有噪声的低分辨率NeRF渲染视图中恢复出逼真的超高分辨率输出。大量实验表明，NeRFLiX++在各种新视角合成基准测试中具有出色的修复能力。