Generic deformable implicit mesh models for automated reconstruction

Deformable 3-D models can be represented either as explicit or implicit surfaces. Explicit surfaces, such as triangulations or wire-frame models, are widely accepted in the computer vision and computer graphics communities. However, for automated modeling purposes, they suffer from the fact that fitting to 2-D and 3-D image-data typically involves minimization of the Euclidean distance between observations and their closest facets, which is a non-differentiable distance function. By contrast, implicit surface representations allow fitting by minimizing an algebraic distance where one only needs to evaluate a differentiable field potential function at every data point. However, they have not gained wide acceptance because they are harder to meaningfully deform and render. To combine the strength of both approaches, we propose a method that can turn a completely arbitrary triangulated mesh, such as one taken from the Web, into an implicit surface that closely approximates its shape and can deform in tandem with it. This allows both graphics designers to deform and reshape the implicit surface by manipulating explicit surfaces using standard deformation techniques and automated fitting algorithms to take advantage of the attractive properties of implicit surfaces. We demonstrate the applicability of our technique for upper body-head, neck and shoulders-automated reconstruction.

[1]  William H. Press,et al.  Numerical recipes , 1990 .

[2]  Laurent D. Cohen,et al.  A Parametric Deformable Model to Fit Unstructured 3D Data , 1998, Comput. Vis. Image Underst..

[3]  Pascal Fua,et al.  Tracking and Modeling People in Video Sequences , 2001, Comput. Vis. Image Underst..

[4]  Brian Wyvill,et al.  Warping as a modelling tool for CSG/implicit models , 1997, Proceedings of 1997 International Conference on Shape Modeling and Applications.

[5]  David G. Lowe,et al.  Fitting Parameterized Three-Dimensional Models to Images , 1991, IEEE Trans. Pattern Anal. Mach. Intell..

[6]  W. Press,et al.  Numerical Recipes: The Art of Scientific Computing , 1987 .

[7]  Laurent D. Cohen,et al.  Introducing new deformable surfaces to segment 3D images , 1991, Proceedings. 1991 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[8]  Pascal Fua,et al.  Using Dirichlet Free Form Deformation to Fit Deformable Models to Noisy 3-D Data , 2002, ECCV.

[9]  Ioannis A. Kakadiaris,et al.  Model-Based Estimation of 3D Human Motion , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[10]  R. Plänkers,et al.  Human body modeling from video sequences , 2001 .

[11]  Ingemar J. Cox,et al.  A maximum-flow formulation of the N-camera stereo correspondence problem , 1998, Sixth International Conference on Computer Vision (IEEE Cat. No.98CH36271).

[12]  Thomas W. Sederberg,et al.  Free-form deformation of solid geometric models , 1986, SIGGRAPH.

[13]  James F. Blinn,et al.  A generalization of algebraic surface drawing , 1982, SIGGRAPH.

[14]  Richard Szeliski,et al.  Surface modeling with oriented particle systems , 1992, SIGGRAPH.

[15]  R. Plankers,et al.  Articulated soft objects for video-based body modeling , 2001, Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001.

[16]  Ernest M. Stokely,et al.  Surface Parametrization and Curvature Measurement of Arbitrary 3-D Objects: Five Practical Methods , 1992, IEEE Trans. Pattern Anal. Mach. Intell..

[17]  Jean Ponce,et al.  Using Geometric Distance Fits for 3-D Object Modeling and Recognition , 1994, IEEE Trans. Pattern Anal. Mach. Intell..

[18]  R. Sibson A vector identity for the Dirichlet tessellation , 1980, Mathematical Proceedings of the Cambridge Philosophical Society.

[19]  Alex Pentland,et al.  Closed-form solutions for physically-based shape modeling and recognition , 1991, Proceedings. 1991 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[20]  Alan H. Barr,et al.  Global and local deformations of solid primitives , 1984, SIGGRAPH.

[21]  Pascal Fua,et al.  Regularized Bundle-Adjustment to Model Heads from Image Sequences without Calibration Data , 2000, International Journal of Computer Vision.

[22]  Mathieu Desbrun,et al.  Animating soft substances with implicit surfaces , 1995, SIGGRAPH.

[23]  Frank P. Ferrie,et al.  Recovery of Volumetric Object Descriptions From Laser Rangefinder Images , 1990, ECCV.

[24]  Nadia Magnenat-Thalmann,et al.  Dirichlet free-form deformations and their application to hand simulation , 1997, Proceedings. Computer Animation '97 (Cat. No.97TB100120).

[25]  Demetri Terzopoulos,et al.  Sampling and reconstruction with adaptive meshes , 1991, Proceedings. 1991 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[26]  Thomas Vetter,et al.  A morphable model for the synthesis of 3D faces , 1999, SIGGRAPH.

[27]  Ioannis A. Kakadiaris,et al.  Model-based estimation of 3D human motion with occlusion based on active multi-viewpoint selection , 1996, Proceedings CVPR IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[28]  Sabine Coquillart,et al.  Extended free-form deformation: a sculpturing tool for 3D geometric modeling , 1990, SIGGRAPH.

[29]  Jules Bloomenthal,et al.  Convolution surfaces , 1991, SIGGRAPH.

[30]  James F. O'Brien,et al.  Shape transformation using variational implicit functions , 1999, SIGGRAPH Courses.

[31]  Richard K. Beatson,et al.  Surface interpolation with radial basis functions for medical imaging , 1997, IEEE Transactions on Medical Imaging.

[32]  John F. Hughes,et al.  Direct manipulation of free-form deformations , 1992, SIGGRAPH.

[33]  Gerald E. Farin,et al.  Surfaces over Dirichlet tessellations , 1990, Comput. Aided Geom. Des..

[34]  William H. Press,et al.  Numerical recipes in C. The art of scientific computing , 1987 .

[35]  Richard K. Beatson,et al.  Reconstruction and representation of 3D objects with radial basis functions , 2001, SIGGRAPH.