Abstract
Neural Radiance Fields (NeRF) achieve photorealistic novel view synthesis but become costly when high-resolution (HR) rendering is required, as HR outputs demand dense sampling and higher-capacity models. Moreover, naively super-resolving per-view renderings in 2D often breaks multi-view consistency. We propose Generalizable NGP-SR, a 3D-aware super-resolution framework that reconstructs an HR radiance field directly from low-resolution (LR) posed images. Built on Neural Graphics Primitives (NGP), NGP-SR conditions radiance prediction on 3D coordinates and learned local texture tokens, enabling recovery of high-frequency details within the radiance field and producing view-consistent HR novel views without external HR references or post-hoc 2D upsampling. Importantly, our model is generalizable: once trained, it can be applied to unseen scenes and rendered from novel viewpoints without per-scene optimization. Experiments on multiple datasets show that NGP-SR consistently improves both reconstruction quality and runtime efficiency over prior NeRF-based super-resolution methods, offering a practical solution for scalable high-resolution novel view synthesis.
Abstract (translated)
神经辐射场（NeRF）可实现照片级真实感的新视角合成，但当需要高分辨率（HR）渲染时，其计算成本高昂，因为高分辨率输出需要密集采样和更高容量的模型。此外，简单地以2D方式对单视角渲染结果进行超分辨率处理，往往会破坏多视角一致性。我们提出了通用型NGP-SR，这是一个三维感知的超分辨率框架，可直接从低分辨率（LR）的位姿图像中重建高分辨率辐射场。基于神经图形基元（NGP），NGP-SR将辐射预测条件化于三维坐标与学习得到的局部纹理标记，从而在辐射场内恢复高频细节，并生成视角一致的高分辨率新视图，而无需外部高分辨率参考或事后的2D上采样。重要的是，我们的模型具有通用性：训练完成后，可应用于未见场景并从新视角进行渲染，无需针对每个场景进行优化。在多个数据集上的实验表明，NGP-SR在重建质量和运行效率上均持续优于先前的基于NeRF的超分辨率方法，为可扩展的高分辨率新视角合成提供了实用解决方案。
URL
https://arxiv.org/abs/2603.20128
