Correspondence　pruning　aims　to　filter　out　false　correspondences　(a.k.a.　outliers)　from　the　initial　feature　correspondence　set,　which　is　pivotal　to　matching-based　vision　tasks,　such　as　image　registration.　To　solve　this　problem,　most　existing　learning-based　methods　typically　use　a　multilayer　perceptron　framework　and　several　well-designed　modules　to　capture　local　and　global　contexts.　However,　few　studies　have　explored　how　local　and　global　consensuses　interact　to　form　cohesive　feature　representations.　This　paper　proposes　a　novel　framework　called　TransMatch,　which　leverages　the　full　power　of　Transformer　structure　to　extract　richer　features　and　facilitate　progressive　local　and　global　consensus　learning.　In　addition　to　enhancing　feature　learning,　Transformer　is　used　as　a　powerful　tool　to　connect　the　above　two　consensuses.　Benefiting　from　Transformer,　our　TransMatch　is　surprisingly　effective　for　differentiating　correspondences.　Experimental　results　on　correspondence　pruning　and　camera　pose　estimation　demonstrate　that　the　proposed　TransMatch　outperforms　other　state-of-the-art　methods　by　a　large　margin.　The　code　will　be　available　at　https://github.　com/lyz8023lyp/TransMatch/.