Due　to　their　good　approximation　accuracy　and　local　fitting　characteristics,　the　moving　least　squares　(MLS)　and　moving　total　least　squares　(MTLS)　methods　are　widely　used　in　various　engineering　fields.　However,　neither　of　these　two　methods　is　robust　and　they　cannot　effectively　deal　with　outliers　in　measurement　data.　To　eliminate　the　negative　influence　of　outliers　and　achieve　robust　reconstruction,　a　novel　MTLS　method　is　proposed　in　this　paper,　which　introduces　local　Bayesian　estimation　combined　with　clustering　using　representatives　(CURE)　algorithm.　In　the　support　domain,　this　method　adopts　a　two-step　process　to　remove　the　abnormal　points　and　adjust　the　weights　of　discrete　points　through　compound　weighting.　Bayesian　estimation　is　first　performed　on　discrete　points　to　derive　the　reference　model,　and　the　residuals　are　calculated　as　the　input　of　CURE　clustering.　The　points　with　large　residuals　are　classified　into　one　cluster　and　removed.　The　remaining　points　undergo　repeated　processing　until　the　iteration　concludes.　A　gradient　weight　function　based　on　the　residuals　and　a　compact　support　weight　function　are　combined　to　determine　the　final　estimated　value　using　weighted　Bayesian　estimation.　The　simulations　and　experiments　demonstrate　that　the　proposed　reconstruction　method　achieves　excellent　accuracy　and　robustness,　surpassing　several　existing　methods　when　handling　highly　contaminated　datasets.　©　2024　Elsevier　Inc.