Abstract
3D super-resolution (3DSR) aims to reconstruct high-resolution (HR) 3D scenes from low-resolution (LR) multi-view images. Existing methods rely on dense LR inputs and per-scene optimization, which restricts the high-frequency priors for constructing HR 3D Gaussian Splatting (3DGS) to those inherited from pretrained 2D super-resolution (2DSR) models. This severely limits reconstruction fidelity, cross-scene generalization, and real-time usability. We propose to reformulate 3DSR as a direct feed-forward mapping from sparse LR views to HR 3DGS representations, enabling the model to autonomously learn 3D-specific high-frequency geometry and appearance from large-scale, multi-scene data. This fundamentally changes how 3DSR acquires high-frequency knowledge and enables robust generalization to unseen scenes. Specifically, we introduce SR3R, a feed-forward framework that directly predicts HR 3DGS representations from sparse LR views via the learned mapping network. To further enhance reconstruction fidelity, we introduce Gaussian offset learning and feature refinement, which stabilize reconstruction and sharpen high-frequency details. SR3R is plug-and-play and can be paired with any feed-forward 3DGS reconstruction backbone: the backbone provides an LR 3DGS scaffold, and SR3R upscales it to an HR 3DGS. Extensive experiments across three 3D benchmarks demonstrate that SR3R surpasses state-of-the-art (SOTA) 3DSR methods and achieves strong zero-shot generalization, even outperforming SOTA per-scene optimization methods on unseen scenes.
Abstract (translated)
3D超分辨率（3DSR）的目标是从低分辨率（LR）的多视角图像中重建高分辨率（HR）的3D场景。现有的方法依赖于密集的低分辨率输入和针对每个场景进行优化，这限制了构建高分辨率3D高斯点集（3DGS）时只能使用从预训练的2D超分辨率（2DSR）模型继承来的高频先验知识。这种情况严重限制了重建的保真度、跨场景泛化能力以及实时可用性。 我们提出了一种将3DSR重新构想为一种直接从前向馈送稀疏低分辨率视图到高分辨率3DGS表示的方法，这种方法使模型能够自主地从大规模多场景数据中学习特定于3D的高频几何和外观特征。这从根本上改变了获取高频知识的方式，并且使得对未见过的场景进行稳健泛化成为可能。 具体而言，我们引入了SR3R，这是一种前向馈送框架，通过所学映射网络直接从前向馈送稀疏低分辨率视图预测高分辨率3DGS表示。为了进一步提高重建保真度，我们提出了高斯偏移学习和特征细化技术，这些技术稳定了重建过程，并增强了高频细节。 SR3R是一种即插即用的框架，可以与任何前向馈送3DGS重建骨干网配合使用：该骨架提供低分辨率的3DGS基础结构，而SR3R将其提升到高分辨率的3DGS。在三个3D基准测试上的广泛实验表明，SR3R超过了最先进的（SOTA）3DSR方法，并实现了强大的零样本泛化性能，在未见过场景中甚至超越了最先进的针对每个场景优化的方法。
URL
https://arxiv.org/abs/2602.24020
