Urban　management　and　survey　departments　have　begun　investigating　the　feasibility　of　acquiring　data　from　various　laser　scanning　systems　for　urban　infrastructure　measurements　and　assessments.　Roadside　objects　such　as　cars,　trees,　traffic　poles,　pedestrians,　bicycles　and　e-bicycles　describe　the　static　and　dynamic　urban　information　available　for　acquisition.　Because　of　the　unstructured　nature　of　3D　point　clouds,　the　rich　targets　in　complex　road　scenes,　and　the　varying　scales　of　roadside　objects,　finely　classifying　these　roadside　objects　from　various　point　clouds　is　a　challenging　task.　In　this　paper,　we　integrate　two　representations　of　roadside　objects,　point　clouds　and　multiview　images　to　propose　a　point-group-view　network　named　PGVNet　for　classifying　roadside　objects　into　cars,　trees,　traffic　poles,　and　small　objects　(pedestrians,　bicycles　and　e-bicycles)　from　generalized　point　clouds.　To　utilize　the　topological　information　of　the　point　clouds,　we　propose　a　graph　attention　convolution　operation　called　AtEdgeConv　to　mine　the　relationship　among　the　local　points　and　to　extract　local　geometric　features.　In　addition,　we　employ　a　hierarchical　view-group-object　architecture　to　diminish　the　redundant　information　between　similar　views　and　to　obtain　salient　viewwise　global　features.　To　fuse　the　local　geometric　features　from　the　point　clouds　and　the　global　features　from　multiview　images,　we　stack　an　attention-guided　fusion　network　in　PGVNet.　In　particular,　we　quantify　and　leverage　the　global　features　as　an　attention　mask　to　capture　the　intrinsic　correlation　and　discriminability　of　the　local　geometric　features,　which　contributes　to　recognizing　the　different　roadside　objects　with　similar　shapes.　To　verify　the　effectiveness　and　generalization　of　our　methods,　we　conduct　extensive　experiments　on　six　test　datasets　of　different　urban　scenes,　which　were　captured　by　different　laser　scanning　systems,　including　mobile　laser　scanning　(MLS)　systems,　unmanned　aerial　vehicle　(UAV)-based　laser　scanning　(ULS)　systems　and　backpack　laser　scanning　(BLS)　systems.　Experimental　results,　and　comparisons　with　state-of-the-art　methods,　demonstrate　that　the　PGVNet　model　is　able　to　effectively　identify　various　cars,　trees,　traffic　poles　and　small　objects　from　generalized　point　clouds,　and　achieves　promising　performances　on　roadside　object　classifications,　with　an　overall　accuracy　of　95.76%.　Our　code　is　released　on　https://github.com/flidarcode/　PGVNet.