Depth　estimation　from　a　single　image　plays　an　important　role　in　3D　scene　perception.　Owing　to　the　development　of　deep　convolutional　neural　networks　(CNNs),　monocular　depth　estimation　models　have　achieved　a　large　number　of　exciting　results.　However,　the　requirement　for　the　manual　per-pixel　labeled　dataset　(ground　truth)　limits　the　application　of　these　supervised　methods.　Basing　on　a　geometric　constraint　between　the　consecutive　stereo　images,　we　propose　an　unsupervised　method　to　infer　the　scene　structure.　We　train　the　model　with　consecutive　stereo　images　as　input　while　only　a　single　image　is　required　at　test　time.　In　contrast　to　previous　works,　this　paper　presents　an　adaptive　loss　function　to　tackle　the　regions　which　are　non-overlapping　between　consecutive　images.　Moreover,　by　exploiting　the　pixels＇　discontinuity　in　the　edge　region　and　the　continuity　in　the　non-edge　region　of　a　depth　image,　we　propose　a　novel　depth　smoothness　loss　to　improve　the　accuracy　of　the　model.　In　addition,　as　an　auxiliary　task,　our　model　also　obtains　the　camera　motion　between　consecutive　images.　Experimental　results　on　the　KITTI　and　Cityscapes　datasets　show　that　our　model　outperforms　other　unsupervised　frameworks　and　some　supervised　frameworks.