Bird＇s-eye-view　(BEV)　building　mapping　from　remote　sensing　images　is　a　studying　hotspot　with　broad　applications.　In　recent　years,　deep　learning　(DL)　has　significantly　advanced　the　development　of　automatic　building　extraction　methods.　However,　most　existing　research　focuses　on　segmenting　buildings　from　a　single　perspective,　such　as　orthophotos,　overlooking　the　rich　information　of　multi-view　images.　In　surveying　and　mapping,　individual　building　instances　need　to　be　separated　even　when　they　are　adjacent　or　touching.　Since　orthophotos　cannot　capture　building　walls　due　to　self-occlusion,　distinguishing　between　closely　connected　buildings　in　densely　built　areas　becomes　challenging.　To　tackle　this　issue,　we　propose　a　multi-view　collaborative　pipeline　for　instance-level　building　segmentation.　This　pipeline　utilizes　a　grouping　optimization　algorithm　to　merge　segmentation　results　from　multiple　views,　which　are　predicted　by　general　instance　segmentation　networks　and　projected　onto　the　BEV,　to　produce　the　final　building　instance　polygons.　Both　qualitative　and　quantitative　results　show　that　the　proposed　multi-view　collaborative　pipeline　significantly　outperforms　the　popular　orthophoto-based　pipeline　on　the　InstanceBuilding　dataset.