Given　the　ecological,　cultural　and　psychological　functions　of　urban　vegetation,　a　growing　number　of　studies　have　focused　on　daily　accessible　greenery　visibility.　Mobile　laser　scanning　(MLS)　can　rapidly　obtain　dense　point　clouds　that　can　be　used　to　extract　vegetation.　To　address　this　issue,　we　propose　a　novel　method　for　calculating　the　green　view　index　(GVI)　based　on　MLS　three-dimensional　(3D)　point　clouds.　In　our　study,　the　GVI　was　specified　as　the　ratio　of　greenery　viewing　angles　to　the　total　number　of　viewing　angles　in　view.　The　GVI　calculation　procedure　was　as　follows.　First,　the　vegetation　points　were　extracted　using　the　density-based　spatial　clustering　of　appli-cations　with　noise　(DBSCAN)　algorithm　and　the　PointNet++　deep　learning　algorithm.　Second,　based　on　the　GVI　specification,　a　virtual　camera　was　constructed　in　a　3D　point　scenario　to　estimate　greenery　viewing　angles　in　view　and　generate　depth　images,　and　then,　the　GVI　value　was　calculated.　This　method　is　flexible　and　can　be　used　to　calculate　the　GVI　at　any　site　with　any　direction　where　3D　point　scene　data　are　available,　and　thus,　it　is　suitable　for　evaluating　various　types　of　urban　greenery.　We　conducted　a　case　human-centered　assessment　of　road　greenery　in　a　partial　area　of　Jinshan　District　in　Fuzhou,　China,　based　on　MLS　point　clouds,　evaluating　visible　greenery　and　analyzing　the　relations　among　the　GVI,　greening　pattern,　and　road　green　belt　mode.　The　results　showed　that　the　overall　visible　greenery　in　the　study　area　was　good　and　that　the　GVI　value　of　most　road　sections　was　more　than　15　%.　The　method　has　potential　for　urban　green　space　planning　and　management.