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　twoview　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　significant　improvement　gains　over　the　second　best　method　on　unknown　outdoor　dataset,　and　obviously　accelerates　model　training　speed.　Copyright　©　2024,　Association　for　the　Advancement　of　Artificial　Intelligence.