3D-aware超分框架，无需优化即可通用生成高清新视角图像
Generalizable NGP-SR: Generalizable Neural Radiance Fields Super-Resolution via Neural Graph Primitives
- 基于神经图元，用3D坐标和局部纹理特征重建高清辐射场
- 在多个数据集上实现更高画质与更快推理速度，优于现有方法
- 训练后可直接用于未见场景，无需逐场景调优
神经辐射场（NeRF）虽能实现逼真的新视角合成，但高分辨率（HR）渲染需密集采样和高容量模型，成本高昂。单纯在2D对每视角进行超分常破坏多视角一致性。本文提出通用型NGP-SR，一种3D感知的超分辨率框架，直接从低分辨率（LR）带姿态图像重建HR辐射场。基于神经图形原语（NGP），该框架以3D坐标和学习到的局部纹理标记为条件，恢复辐射场中的高频细节，生成视点一致的高清新视角图像，且无需外部高清参考或后续2D上采样。重要的是，模型具备通用性：训练完成后可直接应用于未见场景，并从新视角渲染，无需逐场景优化。在多个数据集上的实验表明，相较于已有基于NeRF的超分方法，NGP-SR在重建质量与运行效率方面均有稳定提升，为可扩展的高分辨率新视角合成提供了实用方案。
原文摘要 · Abstract (English)
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.
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