Although　prior　methods　have　achieved　promising　performance　for　recovering　the　3D　geometry　from　a　single　depth　image,　they　tend　to　produce　incomplete　3D　shapes　with　noise.　To　this　end,　we　propose　Multi-Scale　Latent　Feature-Aware　Network　(MLANet)　to　recover　the　full　3D　voxel　grid　from　a　single　depth　view　of　an　object.　MLANet　logically　represents　a　3D　voxel　grid　as　visible　voxels,　occluded　voxels　and　non-object　voxels,　and　aims　to　the　reconstruction　of　the　latter　two.　Thus　MLANet　first　introduces　Multi-Scale　Latent　Feature-Aware　(MLFA)　based　AutoEncoder　(MLFA-AE)　and　a　logical　partition　module　to　predict　an　occluded　voxel　grid　(OccVoxGd)　and　a　non-object　voxel　grid　(NonVoxGd)　from　the　visible　voxel　grid　(VisVoxGd)　corresponding　to　the　input.　MLANet　then　introduces　MLFA　based　Generative　Adversarial　Network　(MLFA-GAN)　to　refine　the　OccVoxGd　and　the　NonVoxGd,　and　combines　them　with　the　VisVoxGd　to　generate　a　target　3D　occupancy　grid.　MLFA　shows　a　strong　ability　of　learning　multi-scale　features　of　an　object　effectively　and　can　be　considered　as　a　plug-and-play　component　to　promote　existing　networks.　The　logical　partition　helps　suppress　NonVoxGd　noise　and　improve　OccVoxGd　accuracy　under　adversarial　constraints.　Experimental　studies　on　both　synthetic　and　real-world　data　show　that　MLANet　outperforms　the　state-of-the-art　methods,　and　especially　reconstructs　unseen　object　categories　with　a　higher　accuracy.　©　2023　Elsevier　B.V.