Abstract
Three-dimensional (3D) shape matching finds a wide application in manufacturing industry, such as surface inspection, workpiece localization and reverse engineering. In this paper, a new optimization method is proposed for non-rigid shape matching modeling and solving. The point-tangent distance function is used to construct a nonlinear least-squares model for anisotropic non-rigid shape matching, from which 9 variables with respect to non-rigid matching parameters are computed simultaneously by solving a linear system. In order to strengthen the non-rigid matching robustness to potential outliers, the optimization model is improved by an iteratively reweighted method. The typical characteristic is to weaken the influence of outliers during iterations. Finally, experiments are carried out to evaluate and analyze the proposed method, including matching accuracy, efficiency and robustness. Shape matching is also an important task in computer vision (such as face registration and object recognition), and the proposed method can find its application in this field.
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