Classifying　point　clouds　obtained　from　mobile　laser　scanning　of　road　environments　is　a　fundamental　yet　challenging　problem　for　road　asset　management　and　unmanned　vehicle　navigation.　Deep　learning　networks　need　no　prior　knowledge　to　classify　multiple　objects,　but　often　generate　a　certain　amount　of　false　predictions.　However,　traditional　clustering　methods　often　involve　leveraging　a　priori　knowledge,　but　may　lack　generalisability　compared　to　deep　learning　networks.　This　paper　presents　a　classification　method　that　coarsely　classifies　multiple　objects　of　road　infrastructure　with　a　symmetric　ensemble　point　(SEP)　network　and　then　refines　the　results　with　a　Euclidean　cluster　extraction　(ECE)　algorithm.　The　SEP　network　applies　a　symmetric　function　to　capture　relevant　structural　features　at　different　scales　and　select　optimal　sub-samples　using　an　ensemble　method.　The　ECE　subsequently　adjusts　points　that　have　been　predicted　incorrectly　by　the　first　step.　The　experimental　results　indicate　that　this　method　effectively　extracts　six　types　of　road　infrastructure　elements:　road　surfaces,　buildings,　walls,　traffic　signs,　trees　and　streetlights.　The　overall　accuracy　of　the　SEP-ECE　method　improves　by　3.97%　with　respect　to　PointNet.　The　achieved　average　classification　accuracy　is　approximately　99.74%,　which　is　suitable　for　practical　use　in　transportation　network　management.