Modeling surfaces of arbitrary topology with dynamic particles

A new approach to surface modeling and reconstruction is developed which overcomes some important limitations of existing surface representations methods. The approach features two components. The first is a dynamic self-organizing oriented particle system which discovers topological and geometric surface structure implicit in visual data. The oriented particles evolve according to Newtonian mechanics and interact through long-range attraction forces, short-range repulsion forces, and coplanarity, conormality, and cocircularity forces. The second component is an efficient triangulation scheme that connects the particles into a continuous global surface model that is consistent with the inferred structure. A flexible surface reconstruction algorithm is developed that can compute complete, detailed, viewpoint-invariant geometric surface descriptions of objects with arbitrary topology. The algorithms are applied to 3-D medical image segmentation and to surface reconstruction from object silhouettes.<<ETX>>

[1]  Thomas O. Binford,et al.  Computer Description of Curved Objects , 1973, IEEE Transactions on Computers.

[2]  H. Barrow,et al.  RECOVERING INTRINSIC SCENE CHARACTERISTICS FROM IMAGES , 1978 .

[3]  William T. Reeves,et al.  Particle systems—a technique for modeling a class of fuzzy objects , 1983, International Conference on Computer Graphics and Interactive Techniques.

[4]  Andrew W. Appel,et al.  An Efficient Program for Many-Body Simulation , 1983 .

[5]  Michael Ian Shamos,et al.  Computational geometry: an introduction , 1985 .

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

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

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

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

[10]  Hanan Samet,et al.  The Design and Analysis of Spatial Data Structures , 1989 .

[11]  Steven W. Zucker,et al.  Inferring Surface Trace and Differential Structure from 3-D Images , 1990, IEEE Trans. Pattern Anal. Mach. Intell..

[12]  Rachid Deriche,et al.  Recursive Filtering and Edge Closing: two primary tools for 3-D edge detection , 1990, ECCV.

[13]  Richard Szeliski,et al.  Shape from rotation , 1991, Proceedings. 1991 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[14]  Dimitris N. Metaxas,et al.  Dynamic 3D Models with Local and Global Deformations: Deformable Superquadrics , 1991, IEEE Trans. Pattern Anal. Mach. Intell..

[15]  Katsushi Ikeuchi,et al.  Shape representation and image segmentation using deformable surfaces , 1992, Image Vis. Comput..

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

[17]  Demetri Terzopoulos,et al.  A finite element model for 3D shape reconstruction and nonrigid motion tracking , 1993, 1993 (4th) International Conference on Computer Vision.

[18]  Richard Szeliski,et al.  Curvature and continuity control in particle-based surface models , 1993, Optics & Photonics.

[19]  Richard Szeliski,et al.  Rapid octree construction from image sequences , 1993 .