Abstract
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 from 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.
Abstract (translated)
3D高斯点阵（3D Gaussian Splatting，简称3DGS）能够实现高质量的新视角合成，这激发了人们对生成比训练时更高分辨率渲染图的兴趣。一种自然的策略是将超分辨率技术应用于低分辨率输入视图，但独立地增强每一张图像会引入多视图不一致性，从而导致模糊的渲染效果。先前的方法试图通过学习神经网络组件、时间上一致的视频先验知识或同时对低分辨率和高分辨率视图进行联合优化来缓解这些不一致性问题，但所有这些方法都统一地在每一幅图像中应用超分辨率技术。 相比之下，我们的关键见解是：近距离的低分辨率视角可能包含高频信息，而这些信息同样可以在更远距离的视角中找到。我们可以通过相机相对于场景几何的位置来确定在哪里增加超分辨率内容。基于这一见解，我们提出了SplatSuRe方法，该方法仅在缺少高频监督的信息不足区域选择性地应用超分辨率内容，从而产生更加清晰和一致的结果。 我们的方法在Tanks & Temples、Deep Blending 和 Mip-NeRF 360 数据集上均超越了基线，在保真度和感知质量方面表现更佳。特别值得注意的是，我们在前景局部区域取得了最大的收益，这些地方需要更高的细节水平。
URL
https://arxiv.org/abs/2512.02172
