Previous　3D　object　reconstruction　methods　from　2D　images　involve　two　issues:　the　lack　of　in-depth　exploration　of　the　prior　knowledge　of　3D　shapes,　and　the　difficulty　of　dealing　with　the　serious　occluded　parts.　Inspired　by　human＇s　perception　on　real-world　objects　which　is　composed　of　an　overall　impression　(known　as　shape　impression)　and　an　enhanced　cognition,　we　propose　a　deep　network　(denoted　by　DASI)　to　learn　the　Domain　Adaptive　Shape　Impression　for　3D　reconstruction　from　arbitrary　view　images.　DASI　consists　of　two　modules:　shape　reconstruction　module　and　shape　refinement　module.　The　former　module　reconstructs　a　coarse　volume　by　learning　a　domain　adaptive　shape　impression　as　embedding　in　image-based　reconstruction.　We　first　leverage　3D　objects　to　learn　a　shape　impression　being　associated　with　prior　knowledge　of　3D　objects.　To　attain　consensus　on　shape　impression　from　2D　images,　we　regard　the　3D　shape　and　the　2D　image　as　two　different　domains.　By　adapting　the　two　domains,　the　shape　impression　learned　from　3D　objects　is　transferred　to　2D　images　and　guides　the　images-based　reconstruction.　The　latter　module　refines　the　objects　by　modeling　the　whole　3D　volume　to　local　3D　patches　and　exploring　their　intrinsic　geometry　relationships.　Quantitative　and　qualitative　experimental　results　on　two　benchmark　datasets　demonstrate　that　DASI　outperforms　several　state-of-the-arts　for　3D　reconstruction　from　single　and　multi-view　2D　images.