A　monocular　vision　based　three　dimensional　(3D)　dense　scene　reconstruction　technique　is　presented　to　achieve　fast　and　accurate　3D　stereoscopic　modeling　in　the　real　environment.　This　proposed　approach　localizes　accurately　a　free　moving　camera　in　the　framework　of　parallel　tracking　and　mapping　(PTAM)　algorithm.　Based　on　this　self-localization,　a　variational　depth　map　estimation　model　is　established　by　using　a　bundle　of　image　around　the　keyframe.　Discrete　depth　space　sampling　strategy　is　proposed　to　initialize　the　variational　depth　map　model　and　primal　dual　algorithm　is　presented　to　optimize　the　model　afterward.　Subsequently,　the　final　3D　scene　model　can　be　estimated　by　integrating　the　projective　camera　imaging　model.　Under　the　compute　unified　device　architecture　(CUDA),　the　algorithm　is　optimized　in　parallel　mode　by　using　the　graphic　processing　unit　(GPU)　hardware,　and　its　real-time　performance　is　significantly　improved.　The　experimental　results　conducted　in　realistic　scenario　demonstrats　the　feasibility　and　effectiveness　of　the　proposed　algorithm.