Local feature matching has long been a fundamental component of 3D vision systems such as Structure-from-Motion (SfM), yet progress has lagged behind the rapid advances of modern data-driven approaches. The newer approaches, such as feed-forward reconstruction models, have benefited extensively from scaling dataset sizes, whereas local feature matching models are still only trained on a few mid-sized datasets. In this paper, we revisit local feature matching from a data-driven perspective. In our approach, which we call LoMa, we combine large and diverse data mixtures, modern training recipes, scaled model capacity, and scaled compute, resulting in remarkable gains in performance. Since current standard benchmarks mainly rely on collecting sparse views from successful 3D reconstructions, the evaluation of progress in feature matching has been limited to relatively easy image pairs. To address the resulting saturation of benchmarks, we collect 1000 highly challenging image pairs from internet data into a new dataset called HardMatch. Ground truth correspondences for HardMatch are obtained via manual annotation by the authors. In our extensive benchmarking suite, we find that LoMa makes outstanding progress across the board, outperforming the state-of-the-art method ALIKED+LightGlue by +18.6 mAA on HardMatch, +29.5 mAA on WxBS, +21.4 (1m, 10$^\circ$) on InLoc, +24.2 AUC on RUBIK, and +12.4 mAA on IMC 2022. We release our code and models publicly at https://github.com/davnords/LoMa.

翻译：局部特征匹配长期以来是三维视觉系统（如运动恢复结构SfM）的基础组成部分，但其进展落后于现代数据驱动方法的快速发展。较新的方法（如前馈重建模型）从扩展数据集规模中获益良多，而局部特征匹配模型仍然仅基于少数中等规模数据集进行训练。本文从数据驱动视角重新审视局部特征匹配。在我们提出的方法LoMa中，我们融合了大规模多样化数据混合、现代训练策略、扩展模型容量以及扩展计算资源，从而在性能上实现了显著提升。由于当前标准基准主要依赖从成功三维重建中收集的稀疏视角，特征匹配进展的评估局限于相对简单的图像对。为应对由此导致的基准饱和问题，我们从互联网数据中收集了1000个极具挑战性的图像对，构建了新数据集HardMatch。HardMatch的真实匹配通过作者手动标注获得。在广泛的基准测试套件中，我们发现LoMa在各任务上均取得了突出进展：在HardMatch上比最先进的ALIKED+LightGlue方法提升+18.6 mAA，在WxBS上提升+29.5 mAA，在InLoc上提升+21.4（1m，10°），在RUBIK上提升+24.2 AUC，在IMC 2022上提升+12.4 mAA。我们在https://github.com/davnords/LoMa 公开了代码和模型。