NeRF-based methods reconstruct 3D scenes by building a radiance field with implicit or explicit representations. While NeRF-based methods can perform novel view synthesis (NVS) at arbitrary scale, the performance in high-resolution novel view synthesis (HRNVS) with low-resolution (LR) optimization often results in oversmoothing. On the other hand, single-image super-resolution (SR) aims to enhance LR images to HR counterparts but lacks multi-view consistency. To address these challenges, we propose Arbitrary-Scale Super-Resolution NeRF (ASSR-NeRF), a novel framework for super-resolution novel view synthesis (SRNVS). We propose an attention-based VoxelGridSR model to directly perform 3D super-resolution (SR) on the optimized volume. Our model is trained on diverse scenes to ensure generalizability. For unseen scenes trained with LR views, we then can directly apply our VoxelGridSR to further refine the volume and achieve multi-view consistent SR. We demonstrate quantitative and qualitatively that the proposed method achieves significant performance in SRNVS.

翻译：基于NeRF的方法通过构建具有隐式或显式表示的辐射场来重建三维场景。虽然基于NeRF的方法能够在任意尺度下进行新视角合成，但在低分辨率优化条件下进行高分辨率新视角合成的性能往往会导致过度平滑。另一方面，单图像超分辨率旨在将低分辨率图像增强为高分辨率图像，但缺乏多视角一致性。为了应对这些挑战，我们提出了任意尺度超分辨率NeRF，这是一种用于超分辨率新视角合成的新颖框架。我们提出了一种基于注意力的VoxelGridSR模型，直接在优化后的体数据上进行三维超分辨率。我们的模型在多样化场景上进行训练以确保泛化能力。对于使用低分辨率视图训练的未见场景，我们可以直接应用我们的VoxelGridSR来进一步细化体数据并实现多视角一致的超分辨率。我们通过定量和定性分析证明，所提方法在超分辨率新视角合成中取得了显著的性能。