Learning-based image matching critically depends on large-scale, diverse, and geometrically accurate training data. 3D Gaussian Splatting (3DGS) enables photorealistic novel-view synthesis and thus is attractive for data generation. However, its geometric inaccuracies and biased depth rendering currently prevent robust correspondence labeling. To address this, we introduce MatchGS, the first framework designed to systematically correct and leverage 3DGS for robust, zero-shot image matching. Our approach is twofold: (1) a geometrically-faithful data generation pipeline that refines 3DGS geometry to produce highly precise correspondence labels, enabling the synthesis of a vast and diverse range of viewpoints without compromising rendering fidelity; and (2) a 2D-3D representation alignment strategy that infuses 3DGS' explicit 3D knowledge into the 2D matcher, guiding 2D semi-dense matchers to learn viewpoint-invariant 3D representations. Our generated ground-truth correspondences reduce the epipolar error by up to 40 times compared to existing datasets, enable supervision under extreme viewpoint changes, and provide self-supervisory signals through Gaussian attributes. Consequently, state-of-the-art matchers trained solely on our data achieve significant zero-shot performance gains on public benchmarks, with improvements of up to 17.7%. Our work demonstrates that with proper geometric refinement, 3DGS can serve as a scalable, high-fidelity, and structurally-rich data source, paving the way for a new generation of robust zero-shot image matchers.

翻译：基于学习的图像匹配关键依赖于大规模、多样化且几何精确的训练数据。三维高斯泼溅（3DGS）能够实现逼真的新视角合成，因此在数据生成方面具有吸引力。然而，其几何不精确性和有偏的深度渲染目前阻碍了鲁棒的对应关系标注。为解决这一问题，我们提出了MatchGS，这是首个旨在系统性地校正并利用3DGS实现鲁棒零样本图像匹配的框架。我们的方法包括两个方面：（1）一个几何忠实的数据生成流程，通过精炼3DGS几何结构来生成高精度的对应关系标签，从而在不牺牲渲染保真度的前提下合成大量多样化的视角；（2）一种2D-3D表示对齐策略，将3DGS的显式三维知识注入到二维匹配器中，引导二维半稠密匹配器学习视角不变的三维表示。与现有数据集相比，我们生成的真实对应关系将极线误差降低了高达40倍，能够在极端视角变化下提供监督，并通过高斯属性提供自监督信号。因此，仅使用我们数据训练的最先进匹配器在公共基准测试中实现了显著的零样本性能提升，改进幅度高达17.7%。我们的工作表明，通过适当的几何精炼，3DGS可以作为一个可扩展、高保真且结构丰富的数据源，为新一代鲁棒零样本图像匹配器铺平道路。