We propose AffineGlue, a method for joint two-view feature matching and robust estimation that reduces the combinatorial complexity of the problem by employing single-point minimal solvers. AffineGlue selects potential matches from one-to-many correspondences to estimate minimal models. Guided matching is then used to find matches consistent with the model, suffering less from the ambiguities of one-to-one matches. Moreover, we derive a new minimal solver for homography estimation, requiring only a single affine correspondence (AC) and a gravity prior. Furthermore, we train a neural network to reject ACs that are unlikely to lead to a good model. AffineGlue is superior to the SOTA on real-world datasets, even when assuming that the gravity direction points downwards. On PhotoTourism, the AUC@10{\deg} score is improved by 6.6 points compared to the SOTA. On ScanNet, AffineGlue makes SuperPoint and SuperGlue achieve similar accuracy as the detector-free LoFTR.

翻译：我们提出AffineGlue，一种用于联合两视图特征匹配与鲁棒估计的方法，该方法通过采用单点最小求解器来降低问题的组合复杂度。AffineGlue从一对多对应关系中选取潜在匹配以估计最小模型，随后利用引导匹配找出与模型一致的匹配，从而减轻一对一匹配中模糊性的影响。此外，我们推导出一种新的用于单应性估计的最小求解器，该求解器仅需一个仿射对应（AC）和重力先验。同时，我们训练了一个神经网络来剔除那些不太可能导向良好模型的AC。在真实世界数据集上，即便假设重力方向向下，AffineGlue的性能仍优于当前最优方法。在PhotoTourism数据集上，与SOTA相比，AUC@10°分数提升了6.6个百分点。在ScanNet数据集上，AffineGlue使SuperPoint和SuperGlue达到了与无检测器方法LoFTR相当的精度。