Many　successful　models　of　saliency　have　been　proposed　to　detect　salient　regions　for　2D　images.　Because　stereopsis,　with　its　distinctive　depth　information,　influences　human　viewing,　it　is　necessary　for　stereoscopic　saliency　detection　to　consider　depth　information　as　an　additional　cue.　In　this　paper,　we　propose　a　3D　stereoscopic　saliency　model　based　on　both　contrast　and　depth-guided-background　prior.　First,　a　depth-guided-background　prior　is　specifically　detected　from　a　disparity　map　apart　from　the　conventional　prior,　assuming　boundary　super-pixels　as　background.　Then,　saliency　based　on　disparity　with　the　help　of　the　proposed　prior　is　proposed　to　prioritize　the　contrasts　among　super-pixels.　In　addition,　a　scheme　to　combine　the　contrast　of　disparity　and　the　contrast　of　color　is　presented.　Finally,　2D　spatial　dissimilarity　features　are　further　employed　to　refine　the　saliency　map.　Experimental　results　on　the　PSU　stereo　saliency　benchmark　dataset　(SSB)　show　that　the　proposed　method　performs　better　than　existing　saliency　models.　(c)　2017　Elsevier　B.V.　All　rights　reserved.