Point　cloud　upsampling　is　used　to　densify　the　sparse　set　of　points　collected　by　3D　sensors,　which　is　widely　used　in　the　field　of　robotics.　In　this　paper,　a　two-stage　method　is　used　to　generate　upsampling　point　cloud.　In　the　first　stage,　the　rough　upsampling　point　cloud　is　generated,　and　in　the　second　stage,　the　rough　point　cloud　is　refined　to　obtain　high-quality　point　cloud.　The　generation　of　high　quality　point　cloud　heavily　relies　on　feature　extractors.　In　the　first　stage,　we　introduce　a　transformer　model　to　improve　the　feature　extraction　effect,　which　helps　to　fully　extract　the　features　of　different　location　points.　A　Refinement　Unit　is　proposed　in　the　second　stage　to　improve　the　rough　upsampling　point　cloud　generated　in　the　first　stage.　The　Refinement　Unit　is　based　on　another　transformer　model　in　which　the　relative　position　coding　function　improves　the　coordinates　of　the　deviation　points　generated　in　the　first　stage.　We　evaluate　our　approach　on　synthetic　data　sets.　Experimental　results　show　that　the　performance　of　this　method　is　better　than　other　methods.