When　the　surface　texture　of　a　workpiece　is　extremely　fine,　cameras　struggle　to　accurately　capture　depth　information,　making　conventional　machine　vision　methods　insufficient　for　achieving　micrometer-scale　threedimensional　surface　reconstructions.　To　overcome　this　limitation,　the　study　focuses　on　high-precision　3D　reconstruction　of　the　surface　morphology　of　milled　workpieces.　Given　the　smoothness　of　milled　surfaces,　their　susceptibility　to　overexposure,　and　the　difficulty　in　extracting　depth　information　from　two-dimensional　image　pixels,　the　paper　proposes　a　novel　method　that　combines　phase-shifting　with　complementary　Gray　code　to　achieve　micrometer-level　surface　reconstruction.　The　superiority　of　this　method　over　traditional　phase-shifting　techniques　is　demonstrated　through　comparisons　of　overall　morphology,　two-dimensional　power　spectral　density　(2D　PSD),　and　the　average　deviations　of　sampled　surfaces.　Results　show　that　the　proposed　method　reduces　the　average　relative　error　in　surface　deviations　by　25.89%　compared　to　traditional　techniques.　Furthermore,　cross-sectional　analyses　reveal　that　the　reconstructed　point　cloud　surface　more　closely　mirrors　the　actual　peak-to-　valley　characteristics　of　the　real　surface.　Experimental　results　confirm　that　this　method　effectively　captures　the　surface　features　of　milled　workpieces,　indicating　broad　potential　for　applications　in　precision　manufacturing.