General Deformable Model Approach for Model-based Reconstruction

In this paper we propose a general approach for performing model-based reconstruction. The task consists in deforming a reference shape in order to extract a geometric model from a 3D dataset. To achieve this goal, two complementary approaches have been widely used. 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 that minimizes the distance between a model and the data. We first show that applying a global transformation on a surface model, is equivalent to applying some global forces on a deformable model. Second we propose a scheme where we combine the registration framework with the deformable model framework. This hybrid deformation model allows us to control the amount of deformation from the reference shape with a single parameter. Finally, we propose a general algorithm for performing model-based reconstruction in a robust and accurate manner. We have applied this approach to the reconstruction from both range data and medical images.