Dynamic 3D Models with Local and Global Deformations: Deformable Superquadrics

The authors present a physically based approach to fitting complex three-dimensional shapes using a novel class of dynamic models that can deform both locally and globally. They formulate the deformable superquadrics which incorporate the global shape parameters of a conventional superellipsoid with the local degrees of freedom of a spline. The model's six global deformational degrees of freedom capture gross shape features from visual data and provide salient part descriptors for efficient indexing into a database of stored models. The local deformation parameters reconstruct the details of complex shapes that the global abstraction misses. The equations of motion which govern the behavior of deformable superquadrics make them responsive to externally applied forces. The authors fit models to visual data by transforming the data into forces and simulating the equations of motion through time to adjust the translational, rotational, and deformational degrees of freedom of the models. Model fitting experiments involving 2D monocular image data and 3D range data are presented. >

[1]  Edward L. Wilson,et al.  Numerical methods in finite element analysis , 1976 .

[2]  D. Marr,et al.  Representation and recognition of the spatial organization of three-dimensional shapes , 1978, Proceedings of the Royal Society of London. Series B. Biological Sciences.

[3]  Barr,et al.  Superquadrics and Angle-Preserving Transformations , 1981, IEEE Computer Graphics and Applications.

[4]  Irving Biederman,et al.  Human image understanding: Recent research and a theory , 1985, Comput. Vis. Graph. Image Process..

[5]  Tomaso Poggio,et al.  Computational vision and regularization theory , 1985, Nature.

[6]  Demetri Terzopoulos,et al.  Regularization of Inverse Visual Problems Involving Discontinuities , 1986, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[7]  Alex Pentland,et al.  Perceptual Organization and the Representation of Natural Form , 1986, Artif. Intell..

[8]  Andrew Blake,et al.  Visual Reconstruction , 1987, Deep Learning for EEG-Based Brain–Computer Interfaces.

[9]  Demetri Terzopoulos,et al.  Constraints on Deformable Models: Recovering 3D Shape and Nonrigid Motion , 1988, Artif. Intell..

[10]  Demetri Terzopoulos,et al.  Physically based models with rigid and deformable components , 1988, IEEE Computer Graphics and Applications.

[11]  Luc Cournoyer,et al.  The NRCC three-dimensional image data files , 1988 .

[12]  Demetri Terzopoulos,et al.  The Computation of Visible-Surface Representations , 1988, IEEE Trans. Pattern Anal. Mach. Intell..

[13]  Terrance E. Boult,et al.  Error Of Fit Measures For Recovering Parametric Solids , 1988, [1988 Proceedings] Second International Conference on Computer Vision.

[14]  Ahmed A. Shabana,et al.  Dynamics of Multibody Systems , 2020 .

[15]  Andrew P. Witkin,et al.  Fast animation and control of nonrigid structures , 1990, SIGGRAPH.

[16]  S. Zucker,et al.  Toward a computational theory of shape: an overview , 1990, eccv 1990.

[17]  Ruzena Bajcsy,et al.  Recovery of Parametric Models from Range Images: The Case for Superquadrics with Global Deformations , 1990, IEEE Trans. Pattern Anal. Mach. Intell..

[18]  Alex Pentland,et al.  Canonical fitting of deformable part models , 1990, Other Conferences.

[19]  Alok Gupta,et al.  Part description and segmentation using contour, surface, and volumetric primitives , 1989, Other Conferences.