We address the problem of determining correspondences between two images in agreement with a geometric model such as an affine, homography or thin-plate spline transformation, and estimating its parameters. The contributions of this work are three-fold. First, we propose a convolutional neural network architecture for geometric matching. The architecture is based on three main components that mimic the standard steps of feature extraction, matching and simultaneous inlier detection and model parameter estimation, while being trainable end-to-end. Second, we demonstrate that the network parameters can be trained from synthetically generated imagery without the need for manual annotation and that our matching layer significantly increases generalization capabilities to never seen before images. Finally, we show that the same model can perform both instance-level and category-level matching giving state-of-the-art results on the challenging PF, TSS and Caltech-101 datasets.

中文翻译：

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用于几何匹配的卷积神经网络架构

我们解决了确定与仿射，单应性或薄板样条变换等几何模型一致的两个图像之间的对应关系并估计其参数的问题。这项工作的贡献是三方面的。首先，我们提出了用于几何匹配的卷积神经网络体系结构。该体系结构基于三个主要组件，这些组件模仿了特征提取，匹配以及同时进行的异常检测和模型参数估计的标准步骤，同时可端到端地进行训练。其次，我们证明了可以从合成生成的图像中训练网络参数，而无需人工注释，并且我们的匹配层显着提高了泛化能力，使图像从未出现过。最后，