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.

翻译：神经辐射场（NeRF）可实现逼真的新视角合成，但高分辨率渲染需密集采样与高容量模型，导致计算成本高昂。此外，在二维空间中对逐视角渲染进行简单超分辨率常会破坏多视角一致性。我们提出通用型NGP-SR——一种直接利用低分辨率位姿图像重建高分辨率辐射场的三维感知超分辨率框架。该框架基于神经图形基元（NGP），通过将辐射预测与三维坐标及学习到的局部纹理令牌相结合，可在辐射场中恢复高频细节，无需外部高分辨率参考或事后二维上采样即可生成视角一致的高分辨率新视角。关键在于，本模型具备泛化性：训练完成后可直接应用于未见场景，无需逐场景优化即可从新视角渲染。多组数据集实验表明，相较于现有基于NeRF的超分辨率方法，NGP-SR在重建质量与运行时效率上均实现持续提升，为可扩展的高分辨率新视角合成提供了实用方案。