Finding matching keypoints between images is a core problem in 3D computer vision. However, modern matchers struggle with large in-plane rotations. A straightforward mitigation is to learn rotation invariance via data augmentation. However, it remains unclear at which stage rotation invariance should be incorporated. In this paper, we study this in the context of a modern sparse matching pipeline. We perform extensive experiments by training on a large collection of 3D vision datasets and evaluating on popular image matching benchmarks. Surprisingly, we find that incorporating rotation invariance already in the descriptor yields similar performance to handling it in the matcher. However, rotation invariance is achieved earlier in the matcher when it is learned in the descriptor, allowing for a faster rotation-invariant matcher. Further, we find that enforcing rotation invariance does not hurt upright performance when trained at scale. Finally, we study the emergence of rotation invariance through scale and find that increasing the training data size substantially improves generalization to rotated images. We release two matchers robust to in-plane rotations that achieve state-of-the-art performance on e.g. multi-modal (WxBS), extreme (HardMatch), and satellite image matching (SatAst). Code is available at https://github.com/davnords/loma.

翻译：寻找图像之间的匹配关键点是三维计算机视觉中的核心问题。然而，现代匹配器在处理大范围平面内旋转时存在困难。一种直接的缓解方法是通过数据增强学习旋转不变性。但尚不清楚应在哪个阶段引入旋转不变性。本文在现代稀疏匹配流水线的背景下研究这一问题。我们通过在大量三维视觉数据集上训练，并在流行的图像匹配基准上进行评估，开展了广泛实验。令人惊讶的是，我们发现仅在描述符中引入旋转不变性就能达到与在匹配器中处理旋转相当的性能。然而，当在描述符中学习旋转不变性时，旋转不变性在匹配器中更早实现，从而可以构建更快的旋转不变匹配器。此外，我们发现大规模训练时强制旋转不变性不会损害直立性能。最后，我们通过尺度研究旋转不变性的出现，发现增加训练数据量能显著提升对旋转图像的泛化能力。我们发布了两种对平面内旋转鲁棒的匹配器，在多模态（WxBS）、极端（HardMatch）和卫星图像匹配（SatAst）等任务上达到了最先进性能。代码见 https://github.com/davnords/loma。