3D Gaussian Splatting (3DGS) enables high-quality novel view synthesis, motivating interest in generating higher-resolution renders than those available during training. A natural strategy is to apply super-resolution (SR) to low-resolution (LR) input views, but independently enhancing each image introduces multi-view inconsistencies, leading to blurry renders. Prior methods attempt to mitigate these inconsistencies through learned neural components, temporally consistent video priors, or joint optimization on LR and SR views, but all uniformly apply SR across every image. In contrast, our key insight is that close-up LR views may contain high-frequency information for regions also captured in more distant views and that we can use the camera pose relative to scene geometry to inform where to add SR content. Building on this insight, we propose SplatSuRe, a method that selectively applies SR content only in undersampled regions lacking high-frequency supervision, yielding sharper and more consistent results. Across Tanks & Temples, Deep Blending, and Mip-NeRF 360, our approach surpasses baselines in both fidelity and perceptual quality. Notably, our gains are most significant in localized foreground regions where higher detail is desired.

翻译：三维高斯泼溅（3DGS）使高质量新视角合成成为可能，激发了生成比训练时更高分辨率渲染的需求。一种自然策略是对低分辨率输入视角应用超分辨率，但独立增强每幅图像会导致多视角不一致，进而产生模糊渲染。先前方法尝试通过学习型神经组件、时间一致性视频先验或低分辨率与超分辨率视角联合优化来缓解这些问题，但所有方法均对所有图像统一应用超分辨率。相比之下，我们的核心洞察在于：近景低分辨率视角可能包含场景中同样被较远视角捕获区域的高频信息，且可借助相机相对于场景几何的位姿来决定何处需补充超分辨率内容。基于此洞察，我们提出SplatSuRe方法，仅在缺乏高频监督的欠采样区域选择性地应用超分辨率内容，从而生成更锐利且更一致的渲染结果。在Tanks & Temples、Deep Blending和Mip-NeRF 360数据集上，本方法在保真度和感知质量方面均超越基线。值得注意的是，我们的增益在需要更高细节的局部前景区域最为显著。