Although　deep　networks-based　3D　reconstruction　methods　can　recover　the　3D　geometry　given　few　inputs,　they　may　produce　unfaithful　reconstruction　when　predicting　occluded　parts　of　3D　objects.　To　address　the　Decoder-Based　Generator　(EDGen)　and　Voxel-Point　Embedding　Network-Based　Discriminator　(VPDis)　for　3D　reconstruction　from　a　monocular　depth　image　of　an　object.　Firstly,　EDGen　decodes　the　features　from　the　2.5D　voxel　grid　representation　of　an　input　depth　image　and　generates　the　3D　occupancy　grid　under　GAN　losses　and　a　sampling　point　loss.　The　sampling　loss　can　improve　the　accuracy　of　predicted　points　with　high　uncertainty.　VPDis　helps　reconstruct　the　details　under　voxel　and　point　adversarial　losses,　respectively.　Experimental　results　show　that　DEGAN　not　only　outperforms　several　state-of-the-art　methods　on　both　public　ModelNet　and　ShapeNet　datasets　but　also　predicts　more　reliable　occluded/missing　parts　of　3D　objects.