Abstract
Scenes reconstructed by 3D Gaussian Splatting (3DGS) trained on low-resolution (LR) images are unsuitable for high-resolution (HR) rendering. Consequently, a 3DGS super-resolution (SR) method is needed to bridge LR inputs and HR rendering. Early 3DGS SR methods rely on single-image SR networks, which lack cross-view consistency and fail to fuse complementary information across views. More recent video-based SR approaches attempt to address this limitation but require strictly sequential frames, limiting their applicability to unstructured multi-view datasets. In this work, we introduce Multi-View Consistent 3D Gaussian Splatting Super-Resolution (MVGSR), a framework that focuses on integrating multi-view information for 3DGS rendering with high-frequency details and enhanced consistency. We first propose an Auxiliary View Selection Method based on camera poses, making our method adaptable for arbitrarily organized multi-view datasets without the need of temporal continuity or data reordering. Furthermore, we introduce, for the first time, an epipolar-constrained multi-view attention mechanism into 3DGS SR, which serves as the core of our proposed multi-view SR network. This design enables the model to selectively aggregate consistent information from auxiliary views, enhancing the geometric consistency and detail fidelity of 3DGS representations. Extensive experiments demonstrate that our method achieves state-of-the-art performance on both object-centric and scene-level 3DGS SR benchmarks.
Abstract (translated)
使用低分辨率（LR）图像训练的3D高斯点云重建方法（3D Gaussian Splatting，简称3DGS）所重构的场景对于高清渲染来说是不合适的。因此需要一种3DGS超分辨技术来连接低分辨率输入和高质量的高清渲染。早期的3DGS超分辨技术主要依赖于单图像超分辨网络，这些技术缺乏视图间的一致性，并且无法融合来自不同视角的互补信息。最近一些基于视频序列的技术试图解决这一问题，但要求严格的顺序帧输入，限制了其在非结构化多视图数据集中的应用。 本文中我们引入了一种新的方法：多视角一致3D高斯点云超分辨技术（MVGSR），该框架旨在整合多视角信息以提高基于3DGS渲染的高频细节和一致性。首先，我们提出一种基于摄像机姿态的辅助视图选择方法，使我们的方法能够适应任意组织化的多视角数据集，并且无需时间连续性或数据重新排序。 此外，我们首次在3DGS超分辨技术中引入了符合基线约束的多视角注意力机制，这是我们的多视角超分辨网络的核心设计。这种设计使得模型可以有选择地从辅助视图中聚合一致的信息，从而增强了3DGS表示的几何一致性与细节准确性。 大量的实验表明，在面向对象和场景级别的基于3DGS的超分辨率基准测试上，该方法达到了最先进的性能表现。
URL
https://arxiv.org/abs/2512.15048
