In　this　paper,　a　monocular　3D　dense　reconstruction　technique　based　on　optical　flow　feedback　is　proposed　to　achieve　fast　and　accurate　3D　stereoscopic　modeling　in　the　real　scene.　Corresponding　pixel　pairs　are　robustly　matched　by　inter-frame　optical　flow　fields,　and　the　five-point　algorithm　is　employed　to　determine　relative　pose　of　the　moving　camera,　based　on　which　sparse　point　cloud　is　generated　and　initial　coarse　mesh　is　built.　In　the　proposed　method,　multi-view　reconstruction　is　implemented　from　perspective　of　vision　method　on　motion　analysis.　The　reconstruction　model　is　fed　back　to　the　reconstruction　process,　and　the　model　is　deformed　by　utilizing　the　bias-driven　of　each　view.　The　coarse　and　inaccurate　original　mesh　surface　is　adjusted　to　the　exact　surface　through　a　dense　non-rigid　deformation.　Under　the　Compute　Unified　Device　Architecture　(CUDA),　the　reconstruction　algorithm　is　optimized　in　parallel　mode　by　using　the　Graphic　Processing　Unit　(GPU)　hardware,　and　real-time　performance　of　the　reconstruction　algorithm　is　significantly　improved.　The　experimental　results　obtained　in　realistic　indoor　scenario　demonstrate　the　effectiveness　and　accuracy　of　the　proposed　algorithm.