In many traditional non-rigid structure from motion(NRSFM)approaches,the estimation results of part feature points may significantly deviate from their true values because only the overall estimation error is consider...In many traditional non-rigid structure from motion(NRSFM)approaches,the estimation results of part feature points may significantly deviate from their true values because only the overall estimation error is considered in their models.Aimed at solving this issue,a local deviation-constrained-based column-space-fitting approach is proposed in this paper to alleviate estimation deviation.In our work,an effective model is first constructed with two terms:the overall estimation error,which is computed by a linear subspace representation,and a constraint term,which is based on the variance of the reconstruction error for each frame.Furthermore,an augmented Lagrange multipliers(ALM)iterative algorithm is presented to optimize the proposed model.Moreover,a convergence analysis is performed with three steps for the optimization process.As both the overall estimation error and the local deviation are utilized,the proposed method can achieve a good estimation performance and a relatively uniform estimation error distribution for different feature points.Experimental results on several widely used synthetic sequences and real sequences demonstrate the effectiveness and feasibility of the proposed algorithm.展开更多
基金supported by the National NaturalScience Foundation of China(61972002)Open Grant from Anhui Province Key Laboratory of Non-Destructive Evaluation(CGHBMWSJC07)。
文摘In many traditional non-rigid structure from motion(NRSFM)approaches,the estimation results of part feature points may significantly deviate from their true values because only the overall estimation error is considered in their models.Aimed at solving this issue,a local deviation-constrained-based column-space-fitting approach is proposed in this paper to alleviate estimation deviation.In our work,an effective model is first constructed with two terms:the overall estimation error,which is computed by a linear subspace representation,and a constraint term,which is based on the variance of the reconstruction error for each frame.Furthermore,an augmented Lagrange multipliers(ALM)iterative algorithm is presented to optimize the proposed model.Moreover,a convergence analysis is performed with three steps for the optimization process.As both the overall estimation error and the local deviation are utilized,the proposed method can achieve a good estimation performance and a relatively uniform estimation error distribution for different feature points.Experimental results on several widely used synthetic sequences and real sequences demonstrate the effectiveness and feasibility of the proposed algorithm.
