Traffic　facilities　extraction　is　of　vital　importance　to　various　applications　such　as　intelligent　transportation　systems,　infrastructures　inventory　and　city　management　related　applications.　Mobile　laser　scanning　(MLS)　systems　provide　a　new　technique　to　capture　and　update　traffic　facilities　information.　However,　classifying　raw　MLS　point　clouds　into　semantic　objects　is　still　one　of　the　most　challenging　and　important　issues.　In　this　study,　we　separate　the　raw　off-ground　point　clouds　into　individual　segments　and　explore　an　object-based　Deep　Belief　Network　(DBN)　architecture　to　detect　roadside　traffic　facilities　(trees,　cars,　and　traffic　poles)　with　limited　labeled　samples.　To　deal　with　various　roadside　traffic　objects　with　different　types,　sizes,　orientations　and　levels　of　incompleteness,　we　develop　a　simple　and　general　multi-view　feature　descriptor　to　characterize　the　global　feature　of　individual　objects　and　extend　the　quantity　of　the　training　samples.　Extensive　experiments　are　employed　to　evaluate　the　validities　of　the　proposed　algorithm　with　six　test　datasets　acquired　by　different　MLS　Systems.　Four　accuracy　evaluation　metrics　precision,　recall,　quality　and　Fscore　of　trees,　cars　and　traffic　poles　on　the　selected　MLS　datasets　achieve　(96.08%,　97.61%,　93.86%,　96.81%),　(97.55%,　94.10%,　91.69%,　95.58%)　and　(94.39%,　97.71%,　92.37%,　95.99%),　respectively.　Accuracy　evaluations　and　comparative　studies　prove　that　the　proposed　method　has　the　ability　of　achieving　the　promising　performance　of　roadside　traffic　facilities　detection　in　complex　urban　scenes.