In　this　article,　we　propose　a　novel　pyramid　transformer　network　(PT-Net)　for　feature　matching　problems.　Recent　studies　have　used　the　dense　motion　field　to　transform　unordered　correspondences　into　ordered　motion　vectors　and　have　used　convolutional　neural　networks　(CNNs)　to　extract　deep　features.　However,　the　limited　receptive　field　of　CNNs　restricts　the　ability　of　the　network　to　capture　global　information　within　the　motion　field.　To　tackle　this　limitation,　we　devise　a　pyramid　transformer　(PT)　block　to　enhance　the　models　ability　to　extract　both　local　and　global　information　from　the　motion　field,　which　fuses　multiscale　motion　field　information　by　constructing　a　pyramid-structured　motion　field.　Furthermore,　to　alleviate　the　high　memory　demands　of　spatial　attention　in　the　transformer,　we　introduce　dilated　sparse　attention　(DSA),　a　novel　attention　block　that　reduces　the　computational　difficulty　of　multihead　self-attention　(MHSA)　through　regular　interval　sampling　and　deconvolution　operations　and　focuses　on　the　essential　regions　to　establish　long-range　dependencies　between　the　correct　motion　vectors.　The　proposed　PT-Net　is　effective　in　inferring　the　probabilities　of　correspondences　belonging　to　either　inliers　or　outliers,　while　simultaneously　estimating　the　essential　matrix.　Extensive　experiments　demonstrate　that　PT-Net　network　outperforms　state-of-the-art　methods　for　outlier　removal　tasks　and　camera　pose　estimation　on　different　datasets,　including　YFCC100M　and　SUN3D.　The　code　is　available　at　https://github.com/gongzhepeng/PT-Net.