As　a　fundamental　task　in　various　application　scenarios,　including　autonomous　driving　and　mobile　robotic　systems,　3D　object　detection　has　received　extensive　attention　from　researchers　in　both　academia　and　industry.　However,　due　to　the　working　principle　of　LiDAR　and　external　factors　such　as　occlusion,　the　collected　point　cloud　of　the　object　is　usually　sparse　and　incomplete,　which　affects　the　performance　of　3D　object　detector.　In　this　letter,　a　Structure　Completion　and　Density　Awareness　Network　(SCDA-Net)　is　proposed　for　3D　object　detection　from　point　clouds.　Specifically,　a　structure　completion　module　is　designed　to　predict　dense　shapes　of　complete　point　clouds　by　leveraging　sequence　transduction　ability　of　the　transformer　architecture.　Furthermore,　we　propose　a　density-aware　voxel　RoI　pooling　strategy　to　introduce　density　features　that　reflect　the　state　information　of　the　original　objects　in　refinement　stage.　By　restoring　the　complete　structure　of　the　objects　and　considering　the　true　distribution　of　the　points　in　raw　point　cloud,　the　proposed　method　achieves　more　accurate　feature　extraction　and　scene　perception.　Extensive　experimental　results　on　the　KITTI　and　Waymo　datasets　demonstrate　the　effectiveness　of　the　proposed　SCDA-Net.