Biharmonic density estimate: a scale-space descriptor for 3-D deformable surfaces

The wide variability in deformable three-dimensional (3-D) shapes calls for the formulation of a multiscale surface signature for effective characterization and analysis of the underlying 3-D intrinsic geometry. To this end, a novel intrinsic geometric scale-space descriptor for 3-D deformable surfaces, termed as the biharmonic density estimate (BDE), is proposed. The BDE, derived from the biharmonic distance measure, is shown to provide an intrinsic geometric scale-space signature for multiscale surface feature-based representation of deformable 3-D shapes that is both effective and useful for practical applications. The proposed BDE signature provides a theoretical framework for the concept of intrinsic geometric scale space, resulting in a highly descriptive characterization of both the local surface structure and the global metric of the underlying 3-D shape. The compactness and robustness of the BDE are experimentally demonstrated on two standard benchmark datasets. The applications of the BDE in the detection of key components on a deformable 3-D surface and determination of sparse point correspondences between two deformable 3-D shapes are also demonstrated.

[1]  Ulrich Pinkall,et al.  Computing Discrete Minimal Surfaces and Their Conjugates , 1993, Exp. Math..

[2]  Leonidas J. Guibas,et al.  A concise and provably informative multi-scale signature based on heat diffusion , 2009 .

[3]  Guoliang Xu Discrete Laplace-Beltrami operators and their convergence , 2004, Comput. Aided Geom. Des..

[4]  Raif M. Rustamov,et al.  Laplace-Beltrami eigenfunctions for deformation invariant shape representation , 2007 .

[5]  Neil A. Dodgson,et al.  Fast Marching farthest point sampling , 2003, Eurographics.

[6]  Iasonas Kokkinos,et al.  SHREC 2010: robust large-scale shape retrieval benchmark , 2010 .

[7]  Mayank Bansal,et al.  Joint Spectral Correspondence for Disparate Image Matching , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.

[8]  Bernard D. Adelstein,et al.  Demand Characteristics in Assessing Motion Sickness in a Virtual Environment: Or Does Taking a Motion Sickness Questionnaire Make You Sick? , 2007 .

[9]  Craig Gotsman,et al.  Spectral compression of mesh geometry , 2000, EuroCG.

[10]  Karthik Ramani,et al.  Temperature distribution descriptor for robust 3D shape retrieval , 2011, CVPR 2011 WORKSHOPS.

[11]  Bo Li,et al.  Hybrid shape descriptor and meta similarity generation for non-rigid and partial 3D model retrieval , 2014, Multimedia Tools and Applications.

[12]  Thomas A. Funkhouser,et al.  Biharmonic distance , 2010, TOGS.

[13]  Hamid R. Arabnia,et al.  Non-rigid shape correspondence and description using geodesic field estimate distribution , 2012, SIGGRAPH '12.

[14]  Bruno Lévy,et al.  Laplace-Beltrami Eigenfunctions Towards an Algorithm That "Understands" Geometry , 2006, IEEE International Conference on Shape Modeling and Applications 2006 (SMI'06).

[15]  François Fouss,et al.  Graph Nodes Clustering Based on the Commute-Time Kernel , 2007, PAKDD.

[16]  Andrew E. Johnson,et al.  Using Spin Images for Efficient Object Recognition in Cluttered 3D Scenes , 1999, IEEE Trans. Pattern Anal. Mach. Intell..

[17]  Bernard Chazelle,et al.  Shape distributions , 2002, TOGS.

[18]  Leonidas J. Guibas,et al.  One Point Isometric Matching with the Heat Kernel , 2010, Comput. Graph. Forum.

[19]  Suchendra M. Bhandarkar,et al.  Joint geometric graph embedding for partial shape matching in images , 2016, 2016 IEEE Winter Conference on Applications of Computer Vision (WACV).

[20]  Suchendra M. Bhandarkar,et al.  Biharmonic density estimate — A scale space signature for deformable surfaces , 2014, 2014 IEEE International Conference on Image Processing (ICIP).

[21]  Niklas Peinecke,et al.  Laplace-Beltrami spectra as 'Shape-DNA' of surfaces and solids , 2006, Comput. Aided Des..

[22]  Jitendra Malik,et al.  Shape Context: A New Descriptor for Shape Matching and Object Recognition , 2000, NIPS.

[23]  Radu Horaud,et al.  Keypoints and Local Descriptors of Scalar Functions on 2D Manifolds , 2012, International Journal of Computer Vision.

[24]  Daniel Cremers,et al.  Integral Invariants for Shape Matching , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[25]  Umberto Castellani,et al.  A sparse coding approach for local-to-global 3D shape description , 2014, The Visual Computer.

[26]  Alexander M. Bronstein,et al.  Shape Recognition with Spectral Distances , 2011, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[27]  Alexander M. Bronstein,et al.  Numerical Geometry of Non-Rigid Shapes , 2009, Monographs in Computer Science.

[28]  Benjamin Bustos,et al.  Key-component Detection on 3D Meshes using Local Features , 2012, 3DOR@Eurographics.

[29]  Jing Hua,et al.  Intrinsic Geometric Scale Space by Shape Diffusion , 2009, IEEE Transactions on Visualization and Computer Graphics.

[30]  B. D. Adelstein,et al.  Calculus of Nonrigid Surfaces for Geometry and Texture Manipulation , 2007 .

[31]  Igor Guskov,et al.  Multi-scale features for approximate alignment of point-based surfaces , 2005, SGP '05.

[32]  Mikhail Belkin,et al.  Discrete laplace operator on meshed surfaces , 2008, SCG '08.

[33]  M. Wardetzky Convergence of the Cotangent Formula: An Overview , 2008 .

[34]  Leonidas J. Guibas,et al.  Shape google: Geometric words and expressions for invariant shape retrieval , 2011, TOGS.

[35]  Haibin Ling,et al.  Diffusion Distance for Histogram Comparison , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).

[36]  Daniel Cremers,et al.  The wave kernel signature: A quantum mechanical approach to shape analysis , 2011, 2011 IEEE International Conference on Computer Vision Workshops (ICCV Workshops).

[37]  Gabriel Taubin,et al.  A signal processing approach to fair surface design , 1995, SIGGRAPH.

[38]  Alexander M. Bronstein,et al.  Calculus of Nonrigid Surfaces for Geometry and Texture Manipulation , 2007, IEEE Transactions on Visualization and Computer Graphics.