Recent studies of two-view correspondence learning usually establish an
end-to-end network to jointly predict correspondence reliability and relative
pose. We improve such a framework from two aspects. First, we propose a Local
Feature Consensus (LFC) plugin block to augment the features of existing
models. Given a correspondence feature, the block augments its neighboring
features with mutual neighborhood consensus and aggregates them to produce an
enhanced feature. As inliers obey a uniform cross-view transformation and share
more consistent learned features than outliers, feature consensus strengthens
inlier correlation and suppresses outlier distraction, which makes output
features more discriminative for classifying inliers/outliers. Second, existing
approaches supervise network training with the ground truth correspondences and
essential matrix projecting one image to the other for an input image pair,
without considering the information from the reverse mapping. We extend
existing models to a Siamese network with a reciprocal loss that exploits the
supervision of mutual projection, which considerably promotes the matching
performance without introducing additional model parameters. Building upon
MSA-Net, we implement the two proposals and experimentally achieve
state-of-the-art performance on benchmark datasets.

在近期两视图对应关系学习的研究中，我们从两个方面改进了现有框架。首先，我们提出了局部特征共识 (LFC) 插件块来增强现有模型的特征。其次，我们将现有模型扩展为具备相互损失的孪生网络，充分利用互相投影的监督信息，极大提高了匹配性能，而不引入额外的模型参数。基于 MSA-Net，我们实现了这两个改进，并在基准数据集上取得了最先进的性能。