Compare　to　the　ICP　(Iterative　Closet　Points)　registration　method　and　its　variants,　the　registration　method　based　on　GMM　(Gaussian　Mixture　Models)　is　less　sensitive　to　initial　position,　noise　and　outliers.　For　efficiency　in　a　large-scale　point　sets　alignment,　the　algorithm　involved　with　FGT　(Fast　Gaussian　Transformation)　was　proposed.　However,　due　to　its　accuracy　degeneration,　the　application　of　fast　implementation　is　limited　in　large-scale　point　registration.　Thus　a　modified　GMM　method　is　established　to　improve　its　accuracy　and　efficiency　in　point　sets　registration.　To　improve　the　precision　and　robustness　of　point　density,　noise　and　outliers,　the　corresponding　weight　matrix　consisted　of　bidirectional　gauss　distance　is　proposed　in　this　study.　Instead　of　FGT　(Fast　Gaussian　Transformation),　the　IFGT　(Improved　Fast　Gaussian　Transformation)　and　an　adaptive　adjustment　based　on　axis-angle　is　proposed　to　further　improve　its　efficiency　and　robustness　about　initial　position　simultaneously.　We　test　capabilities　of　methods　in　classical　model　and　symmetric　and　featureless　manufacture　parts.　Compared　to　state-of-the-art　methods　in　experiment,　the　result　demonstrated　applicability　of　proposed　method　in　real　life.　(C)　2019　Published　by　Elsevier　B.V.