SHREC’16: Partial Matching of Deformable Shapes

Matching deformable 3D shapes under partiality transformations is a challenging problem that has received limited focus in the computer vision and graphics communities. With this benchmark, we explore and thoroughly investigate the robustness of existing matching methods in this challenging task. Participants are asked to provide a point-to-point correspondence (either sparse or dense) between deformable shapes undergoing different kinds of partiality transformations, resulting in a total of 400 matching problems to be solved for each method – making this benchmark the biggest and most challenging of its kind. Five matching algorithms were evaluated in the contest; this paper presents the details of the dataset, the adopted evaluation measures, and shows thorough comparisons among all competing methods.

[1]  David B. Shmoys,et al.  A Best Possible Heuristic for the k-Center Problem , 1985, Math. Oper. Res..

[2]  Michael Garland,et al.  Surface simplification using quadric error metrics , 1997, SIGGRAPH.

[3]  Martial Hebert,et al.  A spectral technique for correspondence problems using pairwise constraints , 2005, Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1.

[4]  H. Zou,et al.  Regularization and variable selection via the elastic net , 2005 .

[5]  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.

[6]  Alfred M. Bruckstein,et al.  Partial Similarity of Objects, or How to Compare a Centaur to a Horse , 2009, International Journal of Computer Vision.

[7]  Daniel Cohen-Or,et al.  4-points congruent sets for robust pairwise surface registration , 2008, ACM Trans. Graph..

[8]  Alexander M. Bronstein,et al.  Not only size matters: Regularized partial matching of nonrigid shapes , 2008, 2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops.

[9]  Hao Li,et al.  Global Correspondence Optimization for Non‐Rigid Registration of Depth Scans , 2008, Comput. Graph. Forum.

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

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

[12]  Vladimir Kolmogorov,et al.  Blossom V: a new implementation of a minimum cost perfect matching algorithm , 2009, Math. Program. Comput..

[13]  Ghassan Hamarneh,et al.  A Survey on Shape Correspondence , 2011, Comput. Graph. Forum.

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

[15]  Radu Horaud,et al.  SHREC '11: Robust Feature Detection and Description Benchmark , 2011, 3DOR@Eurographics.

[16]  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).

[17]  Alexander M. Bronstein,et al.  A game-theoretic approach to deformable shape matching , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

[18]  Yücel Yemez,et al.  Scale Normalization for Isometric Shape Matching , 2012, Comput. Graph. Forum.

[19]  Tobias Schreck,et al.  SHREC'13 Track: Large-Scale Partial Shape Retrieval Using Simulated Range Images , 2013, 3DOR@Eurographics.

[20]  Ghassan Hamarneh,et al.  Bilateral Maps for Partial Matching , 2013, Comput. Graph. Forum.

[21]  Yasuo Kuniyoshi,et al.  Elastic Net Constraints for Shape Matching , 2013, 2013 IEEE International Conference on Computer Vision.

[22]  Daniel Cremers,et al.  Dense Non-rigid Shape Correspondence Using Random Forests , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[23]  Yücel Yemez,et al.  Partial 3‐D Correspondence from Shape Extremities , 2014, Comput. Graph. Forum.

[24]  Leonidas J. Guibas,et al.  Functional map networks for analyzing and exploring large shape collections , 2014, ACM Trans. Graph..

[25]  Hans-Peter Seidel,et al.  A low-dimensional representation for robust partial isometric correspondences computation , 2013, Graph. Model..

[26]  Andrea Torsello,et al.  Fast and accurate surface alignment through an isometry-enforcing game , 2015, Pattern Recognit..

[27]  Pierre Vandergheynst,et al.  Geodesic Convolutional Neural Networks on Riemannian Manifolds , 2015, 2015 IEEE International Conference on Computer Vision Workshop (ICCVW).

[28]  Qi-Xing Huang,et al.  Dense Human Body Correspondences Using Convolutional Networks , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[29]  M. Bronstein,et al.  SHREC’16: Matching of Deformable Shapes with Topological Noise , 2016 .

[30]  Daniel Cremers,et al.  Consistent Partial Matching of Shape Collections via Sparse Modeling , 2017, Comput. Graph. Forum.

[31]  Daniel Cremers,et al.  Partial Functional Correspondence , 2017 .