Correspondence　pruning　aims　to　remove　false　correspondences　(outliers)　from　an　initial　putative　correspondence　set.　This　process　holds　significant　importance　and　serves　as　a　fundamental　step　in　various　applications　within　the　fields　of　remote　sensing　and　photogrammetry.　The　presence　of　noise,　illumination　changes,　and　small　overlaps　in　remote　sensing　images　frequently　result　in　a　substantial　number　of　outliers　within　the　initial　set,　thereby　rendering　the　correspondence　pruning　notably　challenging.　Although　the　spatial　consensus　of　correspondences　has　been　widely　used　to　determine　the　correctness　of　each　correspondence,　achieving　uniform　consensus　can　be　challenging　due　to　the　uneven　distribution　of　correspondences.　Existing　works　have　mainly　focused　on　either　local　or　global　consensus,　with　a　very　small　perspective　or　large　perspective,　respectively.　They　often　ignore　the　moderate　perspective　between　local　and　global　consensus,　called　group　consensus,　which　serves　as　a　buffering　organization　from　local　to　global　consensus,　hence　leading　to　insufficient　correspondence　consensus　aggregation.　To　address　this　issue,　we　propose　a　multi-granularity　consensus　network　(MGCNet)　to　achieve　consensus　across　regions　of　different　scales,　which　leverages　local,　group,　and　global　consensus　to　accomplish　robust　and　accurate　correspondence　pruning.　Specifically,　we　introduce　a　GroupGCN　module　that　randomly　divides　the　initial　correspondences　into　several　groups　and　then　focuses　on　group　consensus　and　acts　as　a　buffer　organization　from　local　to　global　consensus.　Additionally,　we　propose　a　Multi-level　Local　Feature　Aggregation　Module　that　adapts　to　the　size　of　the　local　neighborhood　to　capture　local　consensus　and　a　Multi-order　Global　Feature　Module　to　enhance　the　richness　of　the　global　consensus.　Experimental　results　demonstrate　that　MGCNet　outperforms　state-of-the-art　methods　on　various　tasks,　highlighting　the　superiority　and　great　generalization　of　our　method.　In　particular,　we　achieve　3.95%　and　8.5%　mAP5　degrees　improvement　without　RANSAC　on　the　YFCC100M　dataset　in　known　and　unknown　scenes　for　pose　estimation,　compared　to　the　second-best　models　(MSA-LFC　and　CLNet).　Source　code:　https://github.com/1211193023/MGCNet.