Analysis and Visualization of Maps Between Shapes

In this paper we propose a method for analysing and visualizing individual maps between shapes, or collections of such maps. Our method is based on isolating and highlighting areas where the maps induce significant distortion of a given measure in a multi‐scale way. Unlike the majority of prior work, which focuses on discovering maps in the context of shape matching, our main focus is on evaluating, analysing and visualizing a given map, and the distortion(s) it introduces, in an efficient and intuitive way. We are motivated primarily by the fact that most existing metrics for map evaluation are quadratic and expensive to compute in practice, and that current map visualization techniques are suitable primarily for global map understanding, and typically do not highlight areas where the map fails to meet certain quality criteria in a multi‐scale way. We propose to address these challenges in a unified way by considering the functional representation of a map, and performing spectral analysis on this representation. In particular, we propose a simple multi‐scale method for map evaluation and visualization, which provides detailed multi‐scale information about the distortion induced by a map, which can be used alongside existing global visualization techniques.

[1]  Ron Kimmel,et al.  Generalized multidimensional scaling: A framework for isometry-invariant partial surface matching , 2006, Proceedings of the National Academy of Sciences of the United States of America.

[2]  Thomas A. Funkhouser,et al.  Möbius voting for surface correspondence , 2009, ACM Trans. Graph..

[3]  Maks Ovsjanikov,et al.  Functional maps , 2012, ACM Trans. Graph..

[4]  Leonidas J. Guibas,et al.  Non-Rigid Registration Under Isometric Deformations , 2008 .

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

[6]  Ligang Liu,et al.  A Local/Global Approach to Mesh Parameterization , 2008, Comput. Graph. Forum.

[7]  Yücel Yemez,et al.  Eurographics Symposium on Geometry Processing 2011 Coarse-to-fine Combinatorial Matching for Dense Isometric Shape Correspondence , 2022 .

[8]  Mark Meyer,et al.  Discrete Differential-Geometry Operators for Triangulated 2-Manifolds , 2002, VisMath.

[9]  Jovan Popović,et al.  Mesh-based inverse kinematics , 2005, SIGGRAPH 2005.

[10]  Scott Schaefer,et al.  Image deformation using moving least squares , 2006, ACM Trans. Graph..

[11]  Vladimir G. Kim,et al.  Blended intrinsic maps , 2011, ACM Trans. Graph..

[12]  Daniel Cohen-Or,et al.  Electors Voting for Fast Automatic Shape Correspondence , 2010, Comput. Graph. Forum.

[13]  Craig Gotsman,et al.  Variational harmonic maps for space deformation , 2009, ACM Trans. Graph..

[14]  A. Sellent,et al.  A Toolbox to Visualize Dense Image Correspondences ( Stereo Disparities & Optical Flow ) , 2012 .

[15]  Radu Horaud,et al.  Shape matching based on diffusion embedding and on mutual isometric consistency , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Workshops.

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

[17]  Radu Horaud,et al.  Articulated shape matching using Laplacian eigenfunctions and unsupervised point registration , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[18]  Peter Schröder,et al.  Conformal equivalence of triangle meshes , 2008, ACM Trans. Graph..

[19]  Matthias Zwicker,et al.  Mesh-based inverse kinematics , 2005, ACM Trans. Graph..

[20]  K. Hormann,et al.  MIPS: An Efficient Global Parametrization Method , 2000 .

[21]  Vladimir G. Kim,et al.  Blended intrinsic maps , 2011, SIGGRAPH 2011.

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

[23]  Hans-Peter Seidel,et al.  Intrinsic Shape Matching by Planned Landmark Sampling , 2011, Comput. Graph. Forum.