Point　clouds　captured　by　3D　scanning　are　usually　sparse　and　noisy.　Reconstructing　a　high-resolution　3D　model　of　an　object　is　a　challenging　task　in　computer　vision.　Recent　point　cloud　upsampling　approaches　aim　to　generate　a　dense　point　set,　while　achieving　both　distribution　uniformity　and　proximity-to-surface　directly　via　an　end-to-end　network.　Although　dense　reconstruction　from　low　to　high　resolution　can　be　realized　by　using　these　techniques,　it　lacks　abundant　details　for　dense　outputs.　In　this　work,　we　propose　a　coarse-to-fine　network　PUGL-Net　for　point　cloud　reconstruction　that　first　predicts　a　coarse　high-resolution　point　cloud　via　a　global　dense　reconstruction　module　and　then　increases　the　details　by　aggregating　local　point　features.　On　the　one　hand,　a　transformer-based　mechanism　is　designed　in　the　global　dense　reconstruction　module.　It　aggregates　residual　learning　in　a　self-attention　scheme　for　effective　global　feature　extraction.　On　the　other　hand,　the　coordinate　offset　of　points　is　learned　in　a　local　refinement　module.　It　further　refines　the　coarse　points　by　aggregating　KNN　features.　Evaluated　through　extensive　quantitative　and　qualitative　evaluation　on　synthetic　data　set,　the　proposed　coarse-to-fine　architecture　generates　point　clouds　that　are　accurate,　uniform　and　dense,　it　outperforms　most　existing　state-of-the-art　point　cloud　reconstruction　works.