Video　based　point　cloud　compression　(V-PCC)　provides　an　efficient　solution　for　compressing　dynamic　point　clouds,　but　the　projection　of　V-PCC　from　3D　to　2D　destroys　the　correlation　of　3D　inter-frame　motion　and　reduces　the　performance　of　inter-frame　coding.　To　solve　this　problem,　we　proposes　an　adaptive　segmentation　based　multi-mode　inter-frame　coding　method　for　video　point　cloud　to　improve　V-PCC,　and　designs　a　new　dynamic　point　cloud　inter-frame　encoding　framework.　Firstly,　in　order　to　achieve　more　accurate　block　prediction,　a　block　matching　method　based　on　adaptive　regional　segmentation　is　proposed　to　find　the　best　matching　block;　Secondly,　in　order　to　further　improve　the　performance　of　inter　coding,　a　multi-mode　inter-frame　coding　method　based　on　joint　attribute　rate　distortion　optimization　(RDO)　is　proposed　to　increase　the　prediction　accuracy　and　reduce　the　bit　rate　consumption.　Experimental　results　show　that　the　improved　algorithm　proposed　in　this　paper　achieves　-22.57%　Bjontegaard　delta　bit　rate　(BD-BR)　gain　compared　with　V-PCC.　The　algorithm　is　especially　suitable　for　dynamic　point　cloud　scenes　with　little　change　between　frames,　such　as　video　surveillance　and　video　conference.　©　2023　Science　Press.　All　rights　reserved.