Although　deep　networks　based　methods　outperform　traditional　3D　reconstruction　methods　which　require　multiocular　images　or　class　labels　to　recover　the　full　3D　geometry,　they　may　produce　incomplete　recovery　and　unfaithful　reconstruction　when　facing　occluded　parts　of　3D　objects.　To　address　these　issues,　we　propose　Depth-preserving　Latent　Generative　Adversarial　Network　(DLGAN)　which　consists　of　3D　Encoder-Decoder　based　GAN　(EDGAN,　serving　as　a　generator　and　a　discriminator)　and　Extreme　Learning　Machine　(ELM,　serving　as　a　classifier)　for　3D　reconstruction　from　a　monocular　depth　image　of　an　object.　Firstly,　EDGAN　decodes　a　latent　vector　from　the　2.5D　voxel　grid　representation　of　an　input　image,　and　generates　the　initial　3D　occupancy　grid　under　common　GAN　losses,　a　latent　vector　loss　and　a　depth　loss.　For　the　latent　vector　loss,　we　design　3D　deep　AutoEncoder　(AE)　to　learn　a　target　latent　vector　from　ground　truth　3D　voxel　grid　and　utilize　the　vector　to　penalize　the　latent　vector　encoded　from　the　input　2.5D　data.　For　the　depth　loss,　we　utilize　the　input　2.5D　data　to　penalize　the　initial　3D　voxel　grid　from　2.5D　views.　Afterwards,　ELM　transforms　float　values　of　the　initial　3D　voxel　grid　to　binary　values　under　a　binary　reconstruction　loss.　Experimental　results　show　that　DLGAN　not　only　outperforms　several　state-of-the-art　methods　by　a　large　margin　on　both　a　synthetic　dataset　and　a　real-world　dataset,　but　also　predicts　more　occluded　parts　of　3D　objects　accurately　without　class　labels.