LAUSR

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.