EFFICIENT NONLINEAR FINITE-ELEMENT MODELING OF NONRIGID OBJECTS VIA OPTIMIZATION OF MESH MODELS

In this paper we propose a new general framework for the application o f the nonlinear finite element method (FEM) to nonrigid motion analysi s, We construct the models by integrating image data and prior knowled ge, using well-established techniques from computer vision, structural mechanics, and computer-aided design (CAD). These techniques guide th e process of optimization of mesh models. Linear FEM proved to be a su ccessful physically based modeling tool in solving limited types of no nrigid motion problems, However, linear FEM cannot handle nonlinear ma terials or large deformations. Application of nonlinear FEM to nonrigi d motion analysis has been restricted by difficulties with high comput ational complexity and noise sensitivity. We tackle the problems assoc iated with nonlinear FEM by changing the parametric description of the object to allow easy automatic control of the model, using physically motivated analysis of the possible displacements to address the worst effects of the noise, applying mesh control strategies, and utilizing multiscale methods. The combination of these methods represents a new systematic approach to a class of nonrigid motion applications for wh ich sufficiently precise and flexible FEM models can be built, The res ults from the skin elasticity experiments demonstrate the success of t he proposed method. The model allows us to objectively detect the diff erences in elasticity between normal and abnormal skin, Our work, demo nstrates the possibility of accurate computation of point corresponden ces and force recovery from range image sequences containing nonrigid objects and large motion. (C) 1998 Academic Press.