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User guided 3D scene enrichment

Published:03 December 2016Publication History

ABSTRACT

Enriching 3D scenes with small objects is an important step for creating realistic scenes. It becomes tougher to involve user guidance to increase the variety of the scene enrichment results. To resolve this problem, we present a user-guided 3D indoor scene enrichment framework that helps users to effectively apply their rules for small-object arrangements. The enrichment problem can be divided into three parts: what categories of small objects should appear, where the small objects should be placed and how to arrange them on furniture objects. The first two questions are answered by statistical information learned from image datasets and the third question is answered by constructing a cost function considering both constraints proposed by our system and arrangement rules specified by users. Our experiments show that this framework can efficiently generate plausible scene enrichments that conform to the user-specified arrangement rules.

References

  1. Bukowski, R. W., and Séquin, C. H. 1995. Object associations: A simple and practical approach to virtual 3d manipulation. In SI3D, ACM, 131--138, 214. Google ScholarGoogle ScholarDigital LibraryDigital Library
  2. Chang, A., Savva, M., and Manning, C. 2014. Interactive learning of spatial knowledge for text to 3d scene generation. In Proceedings of the Workshop on Interactive Language Learning, Visualization, and Interfaces, Association for Computational Linguistics, Baltimore, Maryland, USA, 14--21.Google ScholarGoogle Scholar
  3. Chang, A. X., Savva, M., and Manning, C. D. 2014. Learning spatial knowledge for text to 3d scene generation. In Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing, EMNLP 2014, October 25--29, 2014, Doha, Qatar, A meeting of SIGDAT, a Special Interest Group of the ACL, 2028--2038.Google ScholarGoogle Scholar
  4. Chang, A. X., Funkhouser, T. A., Guibas, L. J., Hanrahan, P., Huang, Q., Li, Z., Savarese, S., Savva, M., Song, S., Su, H., Xiao, J., Yi, L., and Yu, F. 2015. Shapenet: An information-rich 3d model repository. CoRR abs/1512.03012.Google ScholarGoogle Scholar
  5. Chang, A. X., Monroe, W., Savva, M., Potts, C., and Manning, C. D. 2015. Text to 3d scene generation with rich lexical grounding. CoRR abs/1505.06289.Google ScholarGoogle Scholar
  6. Fisher, M., and Hanrahan, P. 2010. Context-based search for 3d models. ACM Trans. Graph. 29, 6, 182. Google ScholarGoogle ScholarDigital LibraryDigital Library
  7. Fisher, M., Ritchie, D., Savva, M., Funkhouser, T., and Hanrahan, P. 2012. Example-based synthesis of 3d object arrangements. In ACM SIGGRAPH Asia 2012 papers, SIGGRAPH Asia '12. Google ScholarGoogle ScholarDigital LibraryDigital Library
  8. Gilks, W. R., Richardson, S., and Spiegelhalter, D. J. 1996. Markov Chain Monte Carlo in Practice. Chapman and Hall, London.Google ScholarGoogle Scholar
  9. Google. 3d warehouse. https://3dwarehouse.sketchup.com/.Google ScholarGoogle Scholar
  10. Hastings, W. K. 1970. Monte Carlo sampling methods using Markov chains and their applications. Biometrika 57, 1 (Apr.), 97--109.Google ScholarGoogle ScholarCross RefCross Ref
  11. Kim, Y. M., Mitra, N. J., Yan, D.-M., and Guibas, L. 2012. Acquiring 3d indoor environments with variability and repetition. ACM Transactions on Graphics 31, 6, 138:1--138:11. Google ScholarGoogle ScholarDigital LibraryDigital Library
  12. Liu, T., Hertzmann, A., Li, W., and Funkhouser, T. A. 2015. Style compatibility for 3d furniture models. ACM Trans. Graph. 34, 4, 85. Google ScholarGoogle ScholarDigital LibraryDigital Library
  13. Liu, T., McCann, J., Li, W., and Funkhouser, T. A. 2015. Composition-aware scene optimization for product images. Comput. Graph. Forum 34, 2, 13--24. Google ScholarGoogle ScholarDigital LibraryDigital Library
  14. Majerowicz, L., Shamir, A., Sheffer, A., and Hoos, H. H. 2014. Filling your shelves: Synthesizing diverse style-preserving artifact arrangements. IEEE Trans. Vis. Comput. Graph. 20, 11, 1507--1518.Google ScholarGoogle ScholarCross RefCross Ref
  15. Masui, T. 1992. Graphic object layout with interactive genetic algorithms. In Visual Languages, 1992. Proceedings., 1992 IEEE Workshop on, IEEE, 74--80.Google ScholarGoogle ScholarCross RefCross Ref
  16. Mathieu Larive, O. L. R., and GailDrat, V. 2004. Using meta-heuristics for constraint-based 3d objects layout. In Proc. Conf. on Computer Graphics and Artificial Intelligence, ACM, 11--23.Google ScholarGoogle Scholar
  17. Mattausch, O., Panozzo, D., Mura, C., Sorkine-Hornung, O., and Pajarola, R. 2014. Object detection and classification from large-scale cluttered indoor scans. Comput. Graph. Forum 33, 2, 11--21. Google ScholarGoogle ScholarDigital LibraryDigital Library
  18. Merrell, P., Schkufza, E., Li, Z., Agrawala, M., and Koltun, V. 2011. Interactive furniture layout using interior design guidelines. ACM Trans. Graph. 30, 4 (July), 87:1--87:10. Google ScholarGoogle ScholarDigital LibraryDigital Library
  19. Metropolis, N., Rosenbluth, A. W., Rosenbluth, M. N., Teller, A. H., and Teller, E. 1953. Equation of state calculations by fast computing machines. Journal of Chemical Physics 21, 1087--1092.Google ScholarGoogle ScholarCross RefCross Ref
  20. Nan, L., Xie, K., and Sharf, A. 2012. A search-classify approach for cluttered indoor scene understanding. ACM Transactions on Graphics (Proceedings of SIGGRAPH Asia 2012) 31, 6. Google ScholarGoogle ScholarDigital LibraryDigital Library
  21. Sanchez, S., Le Roux, O., Luga, H., and Gaildrat, V. 2003. Constraint-based 3d-object layout using a genetic algorithm. 3IA' 03.Google ScholarGoogle Scholar
  22. Savva, M., Chang, A. X., and Hanrahan, P. 2015. Semantically-Enriched 3D Models for Common-sense Knowledge. CVPR 2015 Workshop on Functionality, Physics, Intentionality and Causality.Google ScholarGoogle Scholar
  23. Shao, T., Xu, W., Zhou, K., Wang, J., Li, D., and Guo, B. 2012. An interactive approach to semantic modeling of indoor scenes with an rgbd camera. ACM Trans. Graph. 31, 6 (Nov.), 136:1--136:11. Google ScholarGoogle ScholarDigital LibraryDigital Library
  24. Song, S., and Xiao, J. 2014. Sliding Shapes for 3D Object Detection in Depth Images. Springer International Publishing, Cham, 634--651.Google ScholarGoogle Scholar
  25. Xie, H., Xu, W., and Wang, B. 2013. Reshuffle-based interior scene synthesis. In VRCAI, ACM, 191--198. Google ScholarGoogle ScholarDigital LibraryDigital Library
  26. Xu, K., Stewart, J., and Fiume, E. Constraint-based automatic placement for scene composition.Google ScholarGoogle Scholar
  27. Xu, K., Chen, K., Fu, H., Sun, W., and Hu, S. 2013. Sketch2scene: sketch-based co-retrieval and co-placement of 3d models. ACM Trans. Graph. 32, 4, 123:1--123:15. Google ScholarGoogle ScholarDigital LibraryDigital Library
  28. 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 Transactions on Graphics 30, 4, 86. Google ScholarGoogle ScholarDigital LibraryDigital Library
  29. Yu, L.-F., Yeung, S. K., and Terzopoulos, D. 2015. The clutterpalette: An interactive tool for detailing indoor scenes. IEEE Transactions on Visualization and Computer Graphics. Google ScholarGoogle ScholarDigital LibraryDigital Library

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  1. User guided 3D scene enrichment

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      • Published in

        cover image ACM Conferences
        VRCAI '16: Proceedings of the 15th ACM SIGGRAPH Conference on Virtual-Reality Continuum and Its Applications in Industry - Volume 1
        December 2016
        381 pages
        ISBN:9781450346924
        DOI:10.1145/3013971
        • Conference Chairs:
        • Yiyu Cai,
        • Daniel Thalmann

        Copyright © 2016 ACM

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        New York, NY, United States

        Publication History

        • Published: 3 December 2016

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