Unbiased groupwise registration for shape prediction of foot scans

A graph-based groupwise shape registration algorithm for building statistical shape model (SSM) is proposed, which has been successfully applied to shape prediction of foot scans. Establishing unbiased and effective shape correspondences of large-scale data sets is extremely challenging, for the inappropriate selection of initial mean shape and non-rigid registration of shape with large-scale deformation. To address these issues, first, we use a simplified graph to model the shape distribution in metric space and an edge-guided graph shrinkage to deform the shapes. Then, the groupwise registration is performed by iteratively performing the graph shrinkage until the shape converges. And, the correspondences of training shapes are obtained by propagating the converged shape to the original data along each shrinkage path. Compared with traditional forward and backward models of groupwise registration, the proposed method is data-driven without initial mean shape as input. Moreover, under the constraint of the established graph, the non-rigid registration can perform more accurately by restricting shape register to its neighbors. Based on the shape correspondence, the SSM of foot shapes is constructed and applied to shape prediction by taking the collected anthropometric information as predictor. Experiments demonstrate that the proposed method can obtain robust shape correspondences and SSM capability with respect to model generalization, specificity, and compactness. The application of shape prediction model shows an average prediction error lower than 1% for general foot size. [Figure not available: see fulltext.].