Shape Evolution With Structural and Topological Changes Using Blending

This paper describes a framework for the estimation of shape from sparse or incomplete range data. It uses a shape representation called blending, which allows for the geometric combination of shapes into a unified model - selected regions of the component shapes are cut-out and glued together. Estimation of shape by this representation is realized using a physics-based framework, and it also includes a process for deciding how to adapt the structure and topology of the model to improve the fit. The blending representation helps avoid abrupt changes in model geometry during fitting by allowing the smooth evolution of the shape, which improves the robustness of the technique. We demonstrate this framework with a series of experiments showing its ability to automatically extract structured representations from range data given both structurally and topologically complex objects.

[1]  Dmitry B. Goldgof,et al.  Model based part segmentation of range data-hyperquadrics and dividing planes , 1995, Proceedings of the Workshop on Physics-Based Modeling in Computer Vision.

[2]  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..

[3]  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.

[4]  Douglas DeCarlo,et al.  Topological Evolution of Surfaces , 1996, Graphics Interface.

[5]  Gabriel Taubin,et al.  An improved algorithm for algebraic curve and surface fitting , 1993, 1993 (4th) International Conference on Computer Vision.

[6]  Shigeru Muraki,et al.  Volumetric shape description of range data using “Blobby Model” , 1991, SIGGRAPH.

[7]  Donald D. Hoffman,et al.  Parts of recognition , 1984, Cognition.

[8]  Dimitris N. Metaxas.  Physics-Based Deformable Models: Applications to Computer Vision, Graphics, and Medical Imaging , 1996 .

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

[10]  Satoshi Suzuki,et al.  3D parts decomposition from sparse range data using information criterion , 1993, Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.

[11]  Dimitris N. Metaxas.  Physics-Based Deformable Models , 1996 .

[12]  Ramesh C. Jain,et al.  Segmentation through Variable-Order Surface Fitting , 1988, IEEE Trans. Pattern Anal. Mach. Intell..

[13]  G. Stiny.  Shape , 1999 .

[14]  Tony DeRose,et al.  Surface reconstruction from unorganized points , 1992, SIGGRAPH.

[15]  Franc Solina,et al.  Superquadrics for Segmenting and Modeling Range Data , 1997, IEEE Trans. Pattern Anal. Mach. Intell..

[16]  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.

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

[18]  Frank P. Ferrie,et al.  Partitioning range images using curvature and scale , 1993, Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.

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

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

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

[22]  Ruzena Bajcsy,et al.  Volumetric segmentation of range images of 3D objects using superquadric models , 1993 .

[23]  William S. Massey,et al.  Algebraic Topology: An Introduction , 1977 .

[24]  John F. Hughes,et al.  Modeling surfaces of arbitrary topology using manifolds , 1995, SIGGRAPH.

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

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

[27]  K. Ishii,et al.  Recovery of hierarchical part structure of 3-D shape from range image , 1992, Proceedings 1992 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

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

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

[30]  Chia-Wei Liao,et al.  Surface approximation of complex multipart objects , 1995, Proceedings of the Workshop on Physics-Based Modeling in Computer Vision.

[31]  Dimitris N. Metaxas,et al.  Adaptive shape evolution using blending , 1995, Proceedings of IEEE International Conference on Computer Vision.

[32]  Baba C. Vemuri,et al.  From global to local, a continuum of shape models with fractal priors , 1993, Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.

[33]  Jan J. Koenderink,et al.  Solid shape , 1990 .

[34]  A. Pentland.  Perceptual organization and the representation of natural form , 1987 .

[35]  Fujio Yamaguchi,et al.  Curves and Surfaces in Computer Aided Geometric Design , 1988, Springer Berlin Heidelberg.

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

[37]  Alok Gupta,et al.  The extruded generalized cylinder: a deformable model for object recovery , 1994, 1994 Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.

[38]  Ross T. Whitaker,et al.  Algorithms for implicit deformable models , 1995, Proceedings of IEEE International Conference on Computer Vision.

[39]  Kaleem Siddiqi,et al.  Parts of Visual Form: Computational Aspects , 1995, IEEE Trans. Pattern Anal. Mach. Intell..

[40]  Song Han,et al.  Using hyperquadrics for shape recovery from range data , 1993, ICCV.

[41]  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.

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

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

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