Adaptive shape evolution using blending

We propose a shape representation scheme which allows two shapes to be combined into a single model. The desired regions of the two shapes are selected, and then merged together forming a blended shape. For reconstruction, blending is incorporated into a deformable model framework. The model automatically adapts to the data, blending when necessary. Hierarchical blending allows multiple blends of a shape to occur forming an evolution from the initial shape of a sphere to the final shape. Blending also allows the insertion of a hole between arbitrary locations. The models used are globally defined, making the recovered shape a natural symbolic description. We present reconstruction experiments involving shapes of various topologies.<<ETX>>

[1]  Martin Rutishauser,et al.  Merging range images of arbitrarily shaped objects , 1994, 1994 Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.

[2]  Frank P. Ferrie,et al.  Darboux Frames, Snakes, and Super-Quadrics: Geometry from the Bottom Up , 1993, IEEE Trans. Pattern Anal. Mach. Intell..

[3]  Dimitris N. Metaxas,et al.  Blended deformable models , 1994, 1994 Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.

[4]  Gérard G. Medioni,et al.  Simultaneous segmentation and approximation of complex patterns , 1994, 1994 Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.

[5]  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.

[6]  I. Biederman Recognition-by-components: a theory of human image understanding. , 1987, Psychological review.

[7]  Baba C. Vemuri,et al.  Multiresolution stochastic hybrid shape models with fractal priors , 1994, TOGS.

[8]  Baba C. Vemuri,et al.  Shape Modeling with Front Propagation: A Level Set Approach , 1995, IEEE Trans. Pattern Anal. Mach. Intell..

[9]  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.

[10]  Richard Szeliski,et al.  Modeling surfaces of arbitrary topology with dynamic particles , 1993, Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.

[11]  Franc Solina,et al.  A Direct Recovery of Superquadric Models in Range Images Using Recover-and-Select Paradigm , 1994, ECCV.

[12]  Dimitris N. Metaxas,et al.  Shape and Nonrigid Motion Estimation Through Physics-Based Synthesis , 1993, IEEE Trans. Pattern Anal. Mach. Intell..

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

[14]  Dimitris N. Metaxas,et al.  Dynamic 3D models with local and global deformations: deformable superquadrics , 1990, [1990] Proceedings Third International Conference on Computer Vision.

[15]  Dinesh Manocha,et al.  A new approach for surface intersection , 1991, SMA '91.

[16]  Dimitris N. Metaxas,et al.  Dynamic deformation of solid primitives with constraints , 1992, SIGGRAPH.

[17]  Andrew J. Hanson,et al.  Hyperquadrics: Smoothly deformable shapes with convex polyhedral bounds , 1988, Comput. Vis. Graph. Image Process..

[18]  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..

[19]  Hervé Delingette,et al.  Simplex meshes: a general representation for 3D shape reconstruction , 1994, 1994 Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.