Single-view　3D　reconstruction　is　a　fundamental　operation　in　computer　vision.　Although　significant　progress　has　been　made　by　learning-based　approaches,　it　remains　a　challenge　that　the　reconstructed　mesh　is　usually　coarse　since　the　geometric　prior　is　ignored.　In　this　paper,　we　propose　a　geometric　prior　based　graph　convolution　neural　network　model　(named　G2CNN)　for　single-view　3D　reconstruction　with　Loop　subdivision.　G2CNN　is　a　data-driven　deep　neural　network　(DNN)　with　the　geometry　knowledge.　To　make　the　reconstructed　results　with　abundant　geometric　details,　we　generate　shapes　with　a　coarse-to-fine　strategy　and　utilize　the　Gaussian　curvature　loss　as　a　geometric　supervision.　Furthermore,　to　produce　the　physically　accurate　3D　geometry,　the　mesh　subdivision　module　is　designed　with　Loop　subdivision　to　exploit　the　vertex　localizations　and　connectivity,　which　can　refine　and　smooth　the　mesh　surface.　Experimental　results　on　both　synthesized　data　and　real　data　demonstrate　the　effectiveness　of　our　method　in　terms　of　both　subjective　and　objective　quality.　©　2023　IEEE.