CNN　has　proved　powerful　in　many　tasks,　including　single　image　inpainting.　The　paper　presents　an　end-to-end　network　for　stereoscopic　image　inpainting.　The　proposed　network　is　composed　of　two　encoders　for　independent　feature　extraction　of　a　pair　of　stereo　images　with　missing　regions,　a　feature　fusion　module　for　stereo　coherent　structure　prediction,　and　two　decoders　to　generate　a　pair　of　completed　images.　In　order　to　train　the　model,　besides　a　reconstruction　and　an　adversarial　loss　for　content　recovery,　a　local　consistency　loss　is　defined　to　constrain　stereo　coherent　detail　prediction.　Moreover,　we　present　a　transfer-learning　based　training　strategy　to　solve　the　issue　of　stereoscopic　data　scarcity.　To　the　best　of　our　knowledge,　we　are　the　first　to　solve　the　stereoscopic　inpainting　problem　in　the　framework　of　CNN.　Compared　to　traditional　stereoscopic　inpainting　and　available　CNN-based　single　image　inpainting　(repairing　stereo　views　one　by　one)　methods,　our　network　generates　results　of　higher　image　quality　and　stereo　consistency.