The　paper　presents　an　approach　to　cutting　out　the　same　target　object　from　a　pair　of　stereo　images　interactively.　With　this　approach,　a　user　labels　parts　of　the　object　and　background　in　either　of　the　images　with　strokes.　The　approach　generates　a　segmentation　result　immediately.　In　case　it　is　not　satisfying,　the　result　can　be　improved　by　interactively　drawing　more　strokes,　or　using　an　alternative　interaction　way　called　adding　corresponding　points,　which　is　first　presented　in　this　paper.　The　proposed　segmentation　approach　is　capable　of　providing　feedback　fast　after　each　interaction.　The　fast　computation　is　performed　in　the　framework　of　graph　cut.　First,　the　labeled　parts　are　used　to　learn　foreground　and　background　color　models.　Next,　an　energy　function　is　built　by　formulating　the　similarities　between　unlabeled　pixels　and　the　foreground/background　color　models,　color　difference　between　neighbor　pixels,　and　stereo　correspondences　obtained　by　SIFT　feature　matching.　At　last,　graph　cut　is　utilized　to　find　the　optimum　of　the　energy　function　and　obtain　a　segmentation　result.　Different　from　state-of-the-art　methods,　our　segmentation　approach　formulates　sparse　correspondences　rather　than　dense　matches　as　stereo　constraints　in　the　energy　function.　Experimental　results　demonstrate　that　our　method　is　faster　in　computation.　In　the　meanwhile,　it　generates　comparable　results　with　state-of-the-art　methods.