Three-dimensional　(3D)　point　analysis　and　visualization　is　one　of　the　most　effective　methods　of　point　cluster　detection　and　segmentation　in　geospatial　datasets.　However,　serious　scattering　and　clotting　characteristics　interfere　with　the　visual　detection　of　3D　point　clusters.　To　overcome　this　problem,　this　study　proposes　the　use　of　3D　Voronoi　diagrams　to　analyze　and　visualize　3D　points　instead　of　the　original　data　item.　The　proposed　algorithm　computes　the　cluster　of　3D　points　by　applying　a　set　of　3D　Voronoi　cells　to　describe　and　quantify　3D　points.　The　decompositions　of　point　cloud　of　3D　models　are　guided　by　the　3D　Voronoi　cell　parameters.　The　parameter　values　are　mapped　from　the　Voronoi　cells　to　3D　points　to　show　the　spatial　pattern　and　relationships;　thus,　a　3D　point　cluster　pattern　can　be　highlighted　and　easily　recognized.　To　capture　different　cluster　patterns,　continuous　progressive　clusters　and　segmentations　are　tested.　The　3D　spatial　relationship　is　shown　to　facilitate　cluster　detection.　Furthermore,　the　generated　segmentations　of　real　3D　data　cases　are　exploited　to　demonstrate　the　feasibility　of　our　approach　in　detecting　different　spatial　clusters　for　continuous　point　cloud　segmentation.