3D graphical models for vascular-stent pose simulation
Machine Graphics & Vision International Journal
Bayesian surface reconstruction via iterative scan alignment to an optimized prototype
SGP '07 Proceedings of the fifth Eurographics symposium on Geometry processing
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In computer vision, two complementary approaches have been widely used to perform object reconstruction and registration. The deformable model framework locally applies internal and external forces to fit 3D data. The non-rigid registration framework iteratively computes the best global transformation in order to minimize the distance between a template and the data. In this paper, we first show that applying a global transformation on a surface model, is equivalent to applying an external force on a deformable model without any regularizing force. Second we propose a hybrid framework which combines the registration framework and the deformable models scheme. Our hybrid deformation approach allows to control the scale at which the model is deformed. This is clearly beneficial for performing both reconstruction and registration tasks. We show many examples of this approach on active contours and deformable surfaces. Furthermore, a global transformation based on axial symmetry is introduced.