Non-rigid　structure　from　Motion　(NRSfM)　is　an　important　research　task　in　computer　vision,　aiming　to　recover　the　motion　information　of　three-dimensional　non-rigid　structures　from　a　set　of　two-dimensional　images.　An　algorithm　based　on　the　Riemannian　manifold　has　shown　promising　results　in　non-rigid　3D　reconstruction.　However,　this　method　employs　fixed　parameters　for　the　reconstruction　process,　which　makes　it　challenging　to　find　the　optimal　parameter　configuration　for　complex　non-rigid　objects.　So,　a　novel　NRSfM　algorithm　based　on　Bayesian　parameter　optimization　is　proposed　in　this　paper.　By　leveraging　the　Bayesian　optimization　framework,　this　approach　adaptively　adjusts　algorithm　parameters　via　sampling　the　parameter　space　and　evaluating　depth　errors,　enhancing　its　suitability　for　diverse　non-rigid　structure　recovery　scenarios.　A　series　of　experiments　and　comparisons　have　been　conducted　in　this　study.　Through　experiments　on　non-rigid　datasets,　we　have　validated　the　superiority　of　the　Bayesian　parameter　optimization　method　in　NRSfM.　Experimental　results　show　that　the　proposed　Bayesian　parameter　optimization　significantly　improves　recovery　accuracy　compared　to　existing　fixed　parameter　selection　methods.　©　2023　IEEE.