  Correspondence pruning aims to establish reliable correspondences between two
related images and recover relative camera motion. Existing approaches often
employ a progressive strategy to handle the local and global contexts, with a
prominent emphasis on transitioning from local to global, resulting in the
neglect of interactions between different contexts. To tackle this issue, we
propose a parallel context learning strategy that involves acquiring bilateral
consensus for the two-view correspondence pruning task. In our approach, we
design a distinctive self-attention block to capture global context and
parallel process it with the established local context learning module, which
enables us to simultaneously capture both local and global consensuses. By
combining these local and global consensuses, we derive the required bilateral
consensus. We also design a recalibration block, reducing the influence of
erroneous consensus information and enhancing the robustness of the model. The
culmination of our efforts is the Bilateral Consensus Learning Network
(BCLNet), which efficiently estimates camera pose and identifies inliers (true
correspondences). Extensive experiments results demonstrate that our network
not only surpasses state-of-the-art methods on benchmark datasets but also
showcases robust generalization abilities across various feature extraction
techniques. Noteworthily, BCLNet obtains 3.98\% mAP5$^{\circ}$ gains over the
second best method on unknown outdoor dataset, and obviously accelerates model
training speed. The source code will be available at:
https://github.com/guobaoxiao/BCLNet.
