Recovering　the　geometry　of　an　object　from　a　single　depth　image　is　an　interesting　yet　challenging　problem.　While　the　recently　proposed　learning　based　approaches　have　demonstrated　promising　performance,　they　tend　to　produce　unfaithful　and　incomplete　3D　shape.　In　this　paper,　we　propose　Latent　Feature-Aware　and　Local　Structure-Preserving　Network　(LALP-Net)　for　completing　the　full　3D　shape　from　a　single　depth　view　of　an　object,　which　consists　of　a　generator　and　a　discriminator.　In　the　generator,　we　introduce　Latent　Feature-Aware　(LFA)　to　learn　a　latent　representation　from　the　encoded　input　for　a　decoder　generating　the　accurate　and　complete　3D　shape.　LFA　can　be　taken　as　a　plug-and-play　component　to　upgrade　existing　networks.　In　the　discriminator,　we　combine　a　Local　Structure　Preservation　(LSP)　module　regarding　visible　regions　and　a　Global　Structure　Prediction　(GSP)　module　regarding　entire　regions　for　faithful　reconstruction.　Experimental　results　on　both　synthetic　and　real-world　datasets　show　that　our　LALP-Net　outperforms　the　state-of-the-art　methods　by　a　large　margin.