Scene　understanding　in　3-D　point　clouds　requires　to　annotate　points　manually　at　the　model　training　stage.　To　reduce　manual　efforts　in　labeling　points,　this　letter　focuses　on　proposing　an　efficient　method　to　implement　semiautomatic　segmentation　of　3-D　objects　in　3-D　point　clouds.　Specifically,　to　handle　point　clouds　with　high　point　density,　supervoxels　are　treated　as　basic　operating　units　during　the　object　segmentation　procedure.　To　obtain　the　valuable　boundaries　for　guiding　3-D　object　segmentation,　we　propose　to　filter　meaningless　boundaries　obtained　by　a　traditional　boundary　detection　method.　Once　valuable　boundaries　are　obtained,　we　propose　a　boundary-aware　Markov　random　field　(MRF)　model　to　consider　the　object-boundary　constraint　into　generating　the　boundary-preserved　segmentation　results.　Extensive　experiments　on　two　data　sets　show　the　effectiveness　of　our　proposed　framework　on　segmenting　3-D　objects　from　point　cloud　scenes.