In this paper, we describe a framework for surface registration. The framework consists of a combination of rigid registration, elasticity modulated registration and the use of a shape model prior. The main goal in this paper is to minimize the geometric surface registration error while maintaining correspondences. Experiments show improved geometric fit, correspondence, and timing compared to the current state of the art. Possible applications of the framework are construction of correspondences for shape models, reconstruction of missing parts, and artifact reduction.
No takes yet. Share an insight, caveat, or question.
Danckaers et al. (2014) studied this question.
Synapse has enriched 4 closely related papers on similar clinical questions. Consider them for comparative context: