“Style compatibility for 3D furniture models” by Hertzmann, Li and Funkhouser

  • ©Aaron Hertzmann, Wilmot Li, and Thomas (Tom) A. Funkhouser




    Style compatibility for 3D furniture models

Session/Category Title: Shape Analysis




    This paper presents a method for learning to predict the stylistic compatibility between 3D furniture models from different object classes: e.g., how well does this chair go with that table? To do this, we collect relative assessments of style compatibility using crowdsourcing. We then compute geometric features for each 3D model and learn a mapping of them into a space where Euclidean distances represent style incompatibility. Motivated by the geometric subtleties of style, we introduce part-aware geometric feature vectors that characterize the shapes of different parts of an object separately. Motivated by the need to compute style compatibility between different object classes, we introduce a method to learn object class-specific mappings from geometric features to a shared feature space. During experiments with these methods, we find that they are effective at predicting style compatibility agreed upon by people. We find in user studies that the learned compatibility metric is useful for novel interactive tools that: 1) retrieve stylistically compatible models for a query, 2) suggest a piece of furniture for an existing scene, and 3) help guide an interactive 3D modeler towards scenes with compatible furniture.


    1. Akazawa, Y., Okada, Y., and Niijima, K. 2005. Automatic 3d scene generation based on contact constraints. In Proc. Conf. on Computer Graphics and Artificial Intelligence, 593–598.Google Scholar
    2. Bach, F., Jenatton, R., Mairal, J., and Obozinski, G. 2012. Optimization with sparsity-inducing penalties. Foundations and Trends in Machine Learning 4, 1, 1–106. Google ScholarDigital Library
    3. Chaudhuri, S., Kalogerakis, E., Guibas, L., and Koltun, V. 2011. Probabilistic reasoning for assembly-based 3d modeling. ACM Trans. Graph. 30, 4, 35. Google ScholarDigital Library
    4. Chaudhuri, S., Kalogerakis, E., Giguere, S., and Funkhouser, T. 2013. Attribit: content creation with semantic attributes. In Proc. UIST, ACM, 193–202. Google ScholarDigital Library
    5. Fisher, M., and Hanrahan, P. 2010. Context-based search for 3d models. ACM Trans. Graph. 29, 6, 182. Google ScholarDigital Library
    6. Fisher, M., Savva, M., and Hanrahan, P. 2011. Characterizing structural relationships in scenes using graph kernels. ACM Trans. Graph. 30, 4, 34. Google ScholarDigital Library
    7. Fisher, M., Ritchie, D., Savva, M., Funkhouser, T., and Hanrahan, P. 2012. Example-based synthesis of 3d object arrangements. ACM Trans. Graph. 31, 6, 135. Google ScholarDigital Library
    8. Funkhouser, T., Min, P., Kazhdan, M., Chen, J., Halderman, A., Dobkin, D., and Jacobs, D. 2003. A search engine for 3d models. ACM Trans. Graph. 22, 1, 83–105. Google ScholarDigital Library
    9. Garces, E., Agarwala, A., Gutierrez, D., and Hertzmann, A. 2014. A similarity measure for illustration style. ACM Trans. Graph. 33, 4, 93. Google ScholarDigital Library
    10. Goldberger, J., Roweis, S., Hinton, G., and Salakhutdinov, R. 2004. Neighbourhood components analysis. Advances in Neural Information Processing Systems.Google Scholar
    11. Hotelling, H. 1936. Relations between two sets of variates. Biometrika 28, 3-4, 321–377.Google ScholarCross Ref
    12. Huang, Q.-X., Su, H., and Guibas, L. 2013. Fine-grained semi-supervised labeling of large shape collections. ACM Trans. Graph. 32, 6, 190. Google ScholarDigital Library
    13. Jain, A., Thormählen, T., Ritschel, T., and Seidel, H.-P. 2012. Material memex: Automatic material suggestions for 3d objects. ACM Trans. Graph. 31, 5, 143. Google ScholarDigital Library
    14. Kalogerakis, E., Chaudhuri, S., Koller, D., and Koltun, V. 2012. A probabilistic model for component-based shape synthesis. ACM Trans. Graph. 31, 4, 55. Google ScholarDigital Library
    15. Kazhdan, M., Chazelle, B., Dobkin, D., Funkhouser, T., and Rusinkiewicz, S. 2004. A reflective symmetry descriptor for 3d models. Algorithmica 38, 1, 201–225. Google ScholarDigital Library
    16. Kim, V. G., Li, W., Mitra, N. J., Chaudhuri, S., DiVerdi, S., and Funkhouser, T. 2013. Learning part-based templates from large collections of 3d shapes. ACM Trans. Graph. 32, 4, 70. Google ScholarDigital Library
    17. Kulis, B. 2012. Metric learning: A survey. Foundations & Trends in Machine Learning 5, 4, 287–364.Google ScholarCross Ref
    18. Li, H., Zhang, H., Wang, Y., Cao, J., Shamir, A., and Cohen-Or, D. 2013. Curve style analysis in a set of shapes. Computer Graphics Forum 32, 6, 77–88. Google ScholarDigital Library
    19. Lun, Z., Kalogerakis, E., and Sheffer, A. 2015. Elements of style: Learning structure-transcending perceptual shape style similarity. ACM Trans. Graph. 34, 4. Google ScholarDigital Library
    20. Ma, C., Huang, H., Sheffer, A., Kalogerakis, E., and Wang, R. 2014. Analogy-driven 3D style transfer. Computer Graphics Forum 33, 2, 175–184. Google ScholarDigital Library
    21. Merrell, P., Schkufza, E., Li, Z., Agrawala, M., and Koltun, V. 2011. Interactive furniture layout using interior design guidelines. ACM Trans. Graph. 30, 4, 87. Google ScholarDigital Library
    22. Merriam-Webster. 2004. Merriam-Webster Dictionary. Merriam-Webster Mass Market, July.Google Scholar
    23. Miller, J. 2005. Furniture. Penguin.Google Scholar
    24. O’Donovan, P., Lībeks, J., Agarwala, A., and Hertzmann, A. 2014. Exploratory font selection using crowdsourced attributes. ACM Trans. Graph. 33, 4, 92. Google ScholarDigital Library
    25. Parikh, D., and Grauman, K. 2011. Relative attributes. In Proc. ICCV, 503–510. Google ScholarDigital Library
    26. Shapira, L., Shalom, S., Shamir, A., Cohen-Or, D., and Zhang, H. 2010. Contextual part analogies in 3d objects. International Journal of Computer Vision 89, 2-3, 309–326. Google ScholarDigital Library
    27. Tangelder, J. W., and Veltkamp, R. C. 2008. A survey of content based 3d shape retrieval methods. Multimedia tools and applications 39, 3, 441–471. Google ScholarDigital Library
    28. Umetani, N., Igarashi, T., and Mitra, N. J. 2012. Guided exploration of physically valid shapes for furniture design. ACM Trans. Graph. 31, 4, 86. Google ScholarDigital Library
    29. Wilber, M. J., Kwak, I. S., and Belongie, S. J. 2014. Cost-effective hits for relative similarity comparisons. In Proc. HCOMP.Google Scholar
    30. Xu, K., Li, H., Zhang, H., Cohen-Or, D., Xiong, Y., and Cheng, Z.-Q. 2010. Style-content separation by anisotropic part scales. ACM Trans. Graph. 29, 6, 184. Google ScholarDigital Library
    31. Yu, L.-F., Yeung, S. K., Tang, C.-K., Terzopoulos, D., Chan, T. F., and Osher, S. 2011. Make it home: automatic optimization of furniture arrangement. ACM Trans. Graph. 30, 4, 86. Google ScholarDigital Library
    32. Zheng, Y., Cohen-Or, D., and Mitra, N. J. 2013. Smart variations: Functional substructures for part compatibility. Computer Graphics Forum 32, 2pt2, 195–204.Google Scholar
    33. Zhu, C., Byrd, R. H., Lu, P., and Nocedal, J. 1997. Algorithm 778: L-bfgs-b: Fortran subroutines for large-scale bound-constrained optimization. ACM TOMS 23, 4, 550–560. Google ScholarDigital Library

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