Local coordinate frame (LCF) is a key component deployed in most 3D descriptors for invariant representations of 3D surfaces. This paper addresses the problem of attaching a LCF to non-rigidly deforming objects, in particular humanoid surfaces, with the application of recovering correspondences between the template model and input data for 3D human motion tracking. We facilitate this by extending to the non-rigid case two current LCF paradigms for rigid surface matching. Such an adaptation is motivated by the assumption that interpolating locally rigid movements often amounts to smooth globally non-rigid deformations. Both approaches leverage spatial distributions, based on signed distance and principal component analysis, respectively. Furthermore, we advocate a new strategy that incorporates multiple LCF candidates. This way we relax the requirement of perfectly repeatable LCFs, and yet still achieve improved data-model associations. Ground truth for non-rigid LCFs are synthetically generated by interpolating locally rigidly transformed LCFs. Therefore, the proposed methods can be evaluated extensively in terms of repeatability of LCFs, robustness of obtained correspondences, and accuracy of final tracking results. All the experiments demonstrate the benefits of the proposed methods with respect to the state-of-the-art.     «

Local coordinate frame (LCF) is a key component deployed in most 3D descriptors for invariant representations of 3D surfaces. This paper addresses the problem of attaching a LCF to non-rigidly deforming objects, in particular humanoid surfaces, with the application of recovering correspondences between the template model and input data for 3D human motion tracking. We facilitate this by extending to the non-rigid case two current LCF paradigms for rigid surface matching. Such an adaptation is mo...     »