Recently,　3D　deep　learning　technologies　require　a　large　amount　of　supervised　3D　point-cloud　data　to　learn　statistical　models　for　various　ITS-related　tasks,　e.g.　object　classification,　object　detection,　object　segmentation,　etc.　However,　manually　annotating　3D　point-cloud　data　is　time-consuming　and　labor-intensive.　Therefore,　this　paper　aims　at　co-locating　3D　objects　from　mobile　LiDAR　point　clouds　without　any　help　of　supervised　training　data.　To　realize　it,　we　propose　a　new　framework　to　implement　3D　object　co-localization　for　automatically　extracting　the　objects　of　the　same　category　from　different　point-cloud　scenes.　Specifically,　to　search　and　exploit　the　co-information　from　objects　in　different　point-cloud　scenes,　we　formulate　a　3D　object　co-localization　problem　as　a　maximal　subgraph　matching　problem.　During　the　graph　construction　procedure,　to　handle　the　inconsistent　representation　of　objects　in　different　scenes,　we　propose　a　multi-scale　clustering　method　to　represent　objects　by　a　pyramid　structure.　In　addition,　because　the　maximal　subgraph　matching　problem　is　NP-hard,　we　propose　a　stochastic　search　algorithm　to　generate　the　co-localization　results.　Extensive　experiments　on　the　point-cloud　data　collected　by　the　Reigl　VMX450　mobile　LiDAR　system　demonstrate　the　promising　performance　of　the　proposed　framework.