Initialization of Deformable Models from 3D Data

The robustness of shape recovery based on deformable models depends in general on the relative di erence of position and topology of the model with respect to the data a close initialization with correct topology guaranties a proper recovery of the object Furthermore the closeness of the initial model greatly in uences the time of computation needed for the recovery In this paper we propose a method for initializing deformable models from range data or volumetric images The proposed method solves two distinct problems First we use the topological segmentation of volumetric images in order to recover the approximate topology of the object Second we use an e cient mesh sampling algorithm to control the number of vertices of the initial model The method takes into account missing data and outliers