Center for Human Modeling and Simulation

Document Type

Journal Article

Date of this Version

January 1997

Comments

Postprint version. Published in International Journal of Computer Vision, Volume 25, Issue 1, 1997, pages 49-61. The original publication is available at www.springerlink.com.
Publisher URL: http://portal.acm.org/citation.cfm?id=278528

Abstract

We present a model-based method for the multi-level shape, pose estimation and abstraction of an object’s surface from range data. The surface shape is estimated based on the parameters of a superquadric that is subjected to global deformations (tapering and bending) and a varying number of levels of local deformations. Local deformations are implemented using locally adaptive finite elements whose shape functions are piecewise cubic functions with C1 continuity. The surface pose is estimated based on the model's translational and rotational degrees of freedom. The algorithm first does a coarse fit, solving for a first approximation to the translation, rotation and global deformation parameters and then does several passes of mesh refinement, by locally subdividing triangles based on the distance between the given datapoints and the model. The adaptive finite element algorithm ensures that during subdivision the desirable finite element mesh generation properties of conformity, non-degeneracy and smoothness are maintained. Each pass of the algorithm uses physics-based modeling techniques to iteratively adjust the global and local parameters of the model in response to forces that are computed from approximation errors between the model and the data. We present results demonstrating the multi-level shape representation for both sparse and dense range data.

Keywords

multi-level shape representation, adaptive finite elements, deformable models, physics-based modeling

Share

COinS
 

Date Posted: 19 May 2008