Semantic　segmentation　is　a　fundamental　task　in　understanding　urban　mobile　laser　scanning　(MLS)　point　clouds.　Recently,　deep　learning-based　methods　have　become　prominent　for　semantic　segmentation　of　MLS　point　clouds,　and　many　recent　works　have　achieved　state-of-the-art　performance　on　open　benchmarks.　However,　due　to　differences　of　objects　across　different　scenes　such　as　different　height　of　buildings　and　different　forms　of　the　same　road-side　objects,　the　existing　open　benchmarks　(namely　source　scenes)　are　often　significantly　different　from　the　actual　application　datasets　(namely　target　scenes).　This　results　in　underperformance　of　semantic　segmentation　networks　trained　using　source　scenes　when　applied　to　target　scenes.　In　this　paper,　we　propose　a　novel　method　to　perform　unsupervised　scene　adaptation　for　semantic　segmentation　of　urban　MLS　point　clouds.　Firstly,　we　show　the　scene　transfer　phenomena　in　urban　MLS　point　clouds.　Then,　we　propose　a　new　pointwise　attentive　transformation　module　(PW-ATM)　to　adaptively　perform　the　data　alignment.　Next,　a　maximum　classifier　discrepancy-based　(MCD-based)　adversarial　learning　framework　is　adopted　to　further　achieve　feature　alignment.　Finally,　an　end-to-end　alignment　deep　network　architecture　is　designed　for　the　unsupervised　scene　adaptation　semantic　segmentation　of　urban　MLS　point　clouds.　To　experimentally　evaluate　the　performance　of　our　proposed　approach,　two　large-scale　labeled　source　scenes　and　two　different　target　scenes　were　used　for　the　training.　Moreover,　four　actual　application　scenes　are　used　for　the　testing.　The　experimental　results　indicated　that　our　approach　can　effectively　achieve　scene　adaptation　for　semantic　segmentation　of　urban　MLS　point　clouds.