Due　to　the　advantages　of　3D　point　clouds　over　2D　optical　images,　the　related　researches　on　scene　understanding　in　3D　point　clouds　have　been　increasingly　attracting　wide　attention　from　academy　and　industry.　However,　many　3D　scene　understanding　methods　largely　require　abundant　supervised　information　for　training　a　data-driven　model.　The　acquisition　of　such　supervised　information　relies　on　manual　annotations　which　are　laborious　and　arduous.　Therefore,　to　mitigate　such　manual　efforts　for　annotating　training　samples,　this　paper　studies　a　unified　neural　network　to　segment　3D　objects　out　of　point　clouds　interactively.　Particularly,　to　improve　the　segmentation　performance　on　the　accurate　object　segmentation,　the　boundary　information　of　3D　objects　in　point　clouds　are　encoded　as　a　boundary　energy　term　in　the　Markov　Random　Field　(MRF)　model.　Moreover,　the　MRF　model　with　the　boundary　energy　term　is　naturally　integrated　with　the　Graphical　Neural　Network　(GNN)　to　obtain　a　compact　representation　for　generating　the　boundary-preserved　3D　objects.　The　proposed　method　is　evaluated　on　two　point　clouds　datasets　obtained　from　different　types　of　laser　scanning　systems,　i.e.　terrestrial　laser　scanning　system　and　mobile　laser　scanning　system.　Comparative　experiments　show　that　the　proposed　method　is　superior　and　effective　in　3D　objects　segmentation　in　different　point-cloud　scenarios.