This　paper　proposed　an　novel　algorithm　for　exploring　deep　belief　network　(DBN)　architectures　to　extract　and　recognize　roadside　facilities　(trees,　cars　and　traffic　poles)　from　mobile　laser　scanning　(MLS)　point　cloud.The　proposed　methods　firstly　partitioned　the　raw　MLS　point　cloud　into　blocks　and　then　removed　the　ground　and　building　points.In　order　to　partition　the　off-ground　objects　into　individual　objects,　off-ground　points　were　organized　into　an　Octree　structure　and　clustered　into　candidate　objects　based　on　connected　component.To　improve　segmentation　performance　on　clusters　containing　overlapped　objects,　a　refining　processing　using　a　voxel-based　normalized　cut　was　then　implemented.In　addition,　multi-view　features　descriptor　was　generated　for　each　independent　roadside　facilities　based　on　binary　images.Finally,　a　deep　belief　network　(DBN)　was　trained　to　extract　trees,　cars　and　traffic　pole　objects.Experiments　are　undertaken　to　evaluate　the　validities　of　the　proposed　method　with　two　datasets　acquired　by　Lynx　Mobile　Mapper　System.The　precision　of　trees,　cars　and　traffic　poles　objects　extraction　results　respectively　was　97.31%,　97.79%　and　92.78%.The　recall　was　98.30%,　98.75%　and　96.77%　respectively.The　quality　is　95.70%,　93.81%　and　90.00%.And　the　F1　measure　was　97.80%,　96.81%　and　94.73%.　©　2018,　Surveying　and　Mapping　Press.　All　right　reserved.