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Exploration of continuous variability in collections of 3D shapes

Published:25 July 2011Publication History
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Abstract

As large public repositories of 3D shapes continue to grow, the amount of shape variability in such collections also increases, both in terms of the number of different classes of shapes, as well as the geometric variability of shapes within each class. While this gives users more choice for shape selection, it can be difficult to explore large collections and understand the range of variations amongst the shapes. Exploration is particularly challenging for public shape repositories, which are often only loosely tagged and contain neither point-based nor part-based correspondences. In this paper, we present a method for discovering and exploring continuous variability in a collection of 3D shapes without correspondences. Our method is based on a novel navigation interface that allows users to explore a collection of related shapes by deforming a base template shape through a set of intuitive deformation controls. We also help the user to select the most meaningful deformations using a novel technique for learning shape variability in terms of deformations of the template. Our technique assumes that the set of shapes lies near a low-dimensional manifold in a certain descriptor space, which allows us to avoid establishing correspondences between shapes, while being rotation and scaling invariant. We present results on several shape collections taken directly from public repositories.

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References

  1. Allen, B., Curless, B., and Popović, Z. 2003. The space of human body shapes: reconstruction and parameterization from range scans. In Proc. SIGGRAPH, 587--594. Google ScholarGoogle Scholar
  2. Anguelov, D., Srinivasan, P., Koller, D., Thrun, S., Rodgers, J., and Davis, J. 2005. Scape: shape completion and animation of people. ACM SIGGRAPH 24 (July), 408--416. Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. Berner, A., Wand, M., Mitra, N. J., Mewes, D., and Seidel, H.-P. 2011. Shape analysis with subspace symmetries. CGF (Proc. EUROGRAPHICS) 30, 2, 277--286.Google ScholarGoogle ScholarCross RefCross Ref
  4. Blanz, V., and Vetter, T. 1999. A morphable model for the synthesis of 3d faces. In Proc. SIGGRAPH, 187--194. Google ScholarGoogle Scholar
  5. Boutin, M., and Kemper, G. 2004. On reconstructing n-point configurations from the distribution of distances or areas. Advances in Applied Mathematics 32, 4, 709--735.Google ScholarGoogle ScholarDigital LibraryDigital Library
  6. Chaudhuri, S., and Koltun, V. 2010. Data-driven suggestions for creativity support in 3d modeling. In ACM SIGGRAPH Asia, 183:1--183:10. Google ScholarGoogle Scholar
  7. Chazal, F., Cohen Steiner, D., and Mérigot, Q. 2010. Geometric Inference for Measures based on Distance Functions. Research Report RR-6930, INRIA.Google ScholarGoogle Scholar
  8. Cootes, T. F., Taylor, C. J., Cooper, D. H., and Graham, J. 1995. Active shape models -- their training and application. Comput. Vis. Image Underst. 61, 38--59. Google ScholarGoogle ScholarDigital LibraryDigital Library
  9. Dryden, I., and Mardia, K. 1998. Statistical Shape Analysis. John Wiley & Sons.Google ScholarGoogle Scholar
  10. Fisher, M., and Hanrahan, P. 2010. Context-based search for 3d models. In ACM SIGGRAPH Asia, 182:1--182:10. Google ScholarGoogle Scholar
  11. Funkhouser, T., Kazhdan, M., Shilane, P., Min, P., Kiefer, W., Tal, A., Rusinkiewicz, S., and Dobkin, D. 2004. Modeling by example. ACM SIGGRAPH 23, 652--663. Google ScholarGoogle ScholarDigital LibraryDigital Library
  12. Golovinskiy, A., and Funkhouser, T. 2009. Consistent segmentation of 3d models. Comput. Graph. 33 (June), 262--269. Google ScholarGoogle ScholarDigital LibraryDigital Library
  13. Kalogerakis, E., Hertzmann, A., and Singh, K. 2010. Learning 3d mesh segmentation and labeling. In ACM SIGGRAPH, 102:1--102:12. Google ScholarGoogle ScholarDigital LibraryDigital Library
  14. Kazhdan, M., Funkhouser, T., and Rusinkiewicz, S. 2003. Rotation invariant spherical harmonic representation of 3d shape descriptors. In Proc. SGP, 156--164. Google ScholarGoogle ScholarDigital LibraryDigital Library
  15. Kilian, M., Mitra, N. J., and Pottmann, H. 2007. Geometric modeling in shape space. vol. 26, #64, 1--8. Google ScholarGoogle Scholar
  16. Kim, M.-J., Kim, M.-H., and Shen, D. 2008. Learning-based deformation estimation for fast non-rigid registration. In CVPR workshop, 1--6.Google ScholarGoogle Scholar
  17. Kokkinos, I., and Yuille, A. 2007. Unsupervised learning of object deformation models. In IEEE ICCV, 1--8.Google ScholarGoogle Scholar
  18. Laga, H., Takahashi, H., and Nakajima, M. 2006. Spherical wavelet descriptors for content-based 3d model retrieval. In SMI, 15. Google ScholarGoogle Scholar
  19. Mitra, N. J., Guibas, L., and Pauly, M. 2007. Symmetrization. In ACM SIGGRAPH, vol. 26, #63, 1--8. Google ScholarGoogle Scholar
  20. Osada, R., Funkhouser, T., Chazelle, B., and Dobkin, D. 2002. Shape distributions. ACM TOG 21, 807--832. Google ScholarGoogle ScholarDigital LibraryDigital Library
  21. Ovsjanikov, M., Bronstein, A. M., Bronstein, M. M., and Guibas, L. 2009. Shapegoogle: a computer vision approach for invariant shape retrieval. In ICCV workshop, NORDIA.Google ScholarGoogle Scholar
  22. Saltel, E., 2008. INRIA Gamma team research database. http://www-roc.inria.fr/gamma/download/download.php.Google ScholarGoogle Scholar
  23. Shapira, L., Shamir, A., and Cohen-Or, D. 2008. Consistent mesh partitioning and skeletonisation using the shape diameter function. Vis. Comput. 24, 249--259. Google ScholarGoogle ScholarDigital LibraryDigital Library
  24. Sorkine, O., and Alexa, M. 2007. As-rigid-as-possible surface modeling. In Proc. SGP, 109--116. Google ScholarGoogle ScholarDigital LibraryDigital Library
  25. Sumner, R. W., Zwicker, M., Gotsman, C., and Popović, J. 2005. Mesh-based inverse kinematics. ACM SIGGRAPH 24, 488--495. Google ScholarGoogle ScholarDigital LibraryDigital Library
  26. van Kaick, O., Zhang, H., Hamarneh, G., and Cohen-Or, D. 2010. A survey on shape correspondence. Computer Graphics Forum.Google ScholarGoogle Scholar
  27. Xu, K., Li, H., Zhang, H., Cohen-Or, D., Xiong, Y., and Cheng, Z.-Q. 2010. Style-content separation by anisotropic part scales. In ACM SIGGRAPH Asia, 184:1--184:10. Google ScholarGoogle Scholar

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            cover image ACM Transactions on Graphics
            ACM Transactions on Graphics  Volume 30, Issue 4
            July 2011
            829 pages
            ISSN:0730-0301
            EISSN:1557-7368
            DOI:10.1145/2010324
            Issue’s Table of Contents

            Copyright © 2011 ACM

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            Publication History

            • Published: 25 July 2011
            Published in tog Volume 30, Issue 4

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