Journal Home > Volume 4 , Issue 2
Computational Visual Media 2018, 4(2): 123-137 https://doi.org/10.1007/s41095-018-0110-3
Research Article | Open Access | Issue | Published: 21 March 2018

Knowledge graph construction with structure and parameter learning for indoor scene design

Show Author's Information Yuan Liang1,  Fei Xu1,  Song-Hai Zhang1,  Yu-Kun Lai2,  Taijiang Mu1( )
TNList, Department of Computer Science, Tsinghua University, Beijing 100084, China.
School of Computer Science and Informatics, Cardiff University, Cardiff, CF24 3AA, UK.
Keywords:
knowledge graph, scene design, structure learning, parameter learning
Cite this article:
Liang Y, Xu F, Zhang S-H, et al. Knowledge graph construction with structure and parameter learning for indoor scene design. Computational Visual Media, 2018, 4(2): 123-137. https://doi.org/10.1007/s41095-018-0110-3
Download citation
EndNote(RIS)
BibTeX

799

Views

38

Downloads

Citations

31

Crossref

N/A

WoS

34

Scopus

2

CSCD

Abstract Full text About this article

We consider the problem of learning a representation of both spatial relations and dependencies between objects for indoor scene design. We propose a novel knowledge graph framework based on the entity-relation model for representation of facts in indoor scene design, and further develop a weakly-supervised algorithm for extracting the knowledge graph representation from a small dataset using both structure and parameter learning. The proposed framework is flexible, transferable, and readable. We present a variety of computer-aided indoor scene design applications using this representation, to show the usefulness and robustness of the proposed framework.


menu
Abstract
Full text
Outline
About this article

Knowledge graph construction with structure and parameter learning for indoor scene design

Show Author's information Yuan Liang1, Fei Xu1, Song-Hai Zhang1, Yu-Kun Lai2, Taijiang Mu1( )
TNList, Department of Computer Science, Tsinghua University, Beijing 100084, China.
School of Computer Science and Informatics, Cardiff University, Cardiff, CF24 3AA, UK.

Abstract

We consider the problem of learning a representation of both spatial relations and dependencies between objects for indoor scene design. We propose a novel knowledge graph framework based on the entity-relation model for representation of facts in indoor scene design, and further develop a weakly-supervised algorithm for extracting the knowledge graph representation from a small dataset using both structure and parameter learning. The proposed framework is flexible, transferable, and readable. We present a variety of computer-aided indoor scene design applications using this representation, to show the usefulness and robustness of the proposed framework.

Keywords: knowledge graph, scene design, structure learning, parameter learning

References(39)

[1]
Autodesk REVIT. Available at https://www.autodesk.com/products/revit-family/overview.
DOI
[2]
SketchUp. Available at https://www.sketchup.com/.
DOI
[3]
ARCHICAD. Available at http://www.graphisoft.com/archicad/.
DOI
[4]
Fisher, M.; Hanrahan, P. Context-based search for 3D models. ACM Transactions on Graphics Vol. 29, No. 6, Article No. 182, 2010.
DOI Google Scholar
[5]
Yeh, Y.-T.; Yang, L.; Watson, M.; Goodman, N. D.; Hanrahan, P. Synthesizing open worlds with constraints using locally annealed reversible jump MCMC. ACM Transactions on Graphics Vol. 31, No. 4, Article No. 56, 2012.
DOI Google Scholar
[6]
Dalton, J.; Dietz, L.; Allan, J. Entity query feature expansion using knowledge base links. In: Proceedings of the 37th International ACM SIGIR Conference on Research & Development in Information Retrieval, 365-374, 2014.
DOI
[7]
Li, F.; Dong, X. L.; Langen, A.; Li, Y. Knowledge verification for long-tail verticals. Proceedings of the VLDB Endowment Vol. 10, No. 11, 1370-1381, 2017.
DOI Google Scholar
[8]
Zhu, X.; Anguelov, D.; Ramanan, D. Capturing long-tail distributions of object subcategories. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 915-922, 2014.
DOI
[9]
Savva, M.; Chang, A. X.; Agrawala, M. Scenesuggest: Context-driven 3D scene design. arXiv preprint arXiv:1703.00061, 2017.
[10]
Yu, L. F.; Yeung, S.-K.; Terzopoulos, D. The clutterpalette: An interactive tool for detailing indoor scenes. IEEE Transactions on Visualization and Computer Graphics Vol. 22, No. 2, 1138-1148, 2016.
DOI Google Scholar
[11]
Fisher, M.; Ritchie, D.; Savva, M.; Funkhouser, T.; Hanrahan, P. Example-based synthesis of 3D object arrangements. ACM Transactions on Graphics Vol. 31, No. 6, Article No. 135, 2012.
DOI Google Scholar
[12]
Xu, K.; Chen, K.; Fu, H.; Sun, W.-L.; Hu, S.-M. Sketch2Scene: Sketch-based co-retrieval and co-placement of 3D models. ACM Transactions on Graphics Vol. 32, No. 4, Article No. 123, 2013.
DOI Google Scholar
[13]
Chang, A. X.; Eric, M.; Savva, M.; Manning, C. D. SceneSeer: 3D scene design with natural language. arXiv preprint arXiv:1703.00050, 2017.
[14]
Chen, K.; Lai, Y.-K.; Hu, S.-M. 3D indoor scene modeling from RGB-D data: a survey. Computational Visual Media Vol. 1, No. 4, 267-278, 2015.
DOI Google Scholar
[15]
Mihalcea, R.; Radev, D. Graph-based Natural Language Processing and Information Retrieval. Cambridge University Press, 2011.
[16]
Yao, X.; Van Durme, B. Information extraction over structured data: Question answering with freebase. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, 956-966, 2014.
DOI
[17]
Socher, R.; Chen, D.; Manning, C. D.; Ng, A. Reasoning with neural tensor networks for knowledge base completion. In: Proceedings of the Advances in Neural Information Processing Systems 26, 926-934, 2013.
[18]
Marino, K.; Salakhutdinov, R.; Gupta, A. The more you know: Using knowledge graphs for image classification. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2673-2681, 2017.
DOI
[19]
Lehmann, J.; Isele, R.; Jakob, M.; Jentzsch, A.; Kontokostas, D.; Mendes, P. N.; Hellmann, S.; Morsey, M.; van Kleef, P.; Auer, S.; Bizer, C. DBpedia—A large-scale, multilingual knowledge base extracted from Wikipedia. Semantic Web Vol. 6, No. 2, 167-195, 2015.
DOI Google Scholar
[20]
Chang, A. X.; Savva, M.; Manning, C. D. Learning spatial knowledge for text to 3D scene generation. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing, 2028-2038, 2014.
DOI
[21]
Dong, X.; Gabrilovich, E.; Heitz, G.; Horn, W.; Lao, N.; Murphy, K.; Strohmann, T.; Sun, S.; Zhang, W. Knowledge vault: A web-scale approach to probabilistic knowledge fusion. In: Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 601-610, 2014.
DOI
[22]
Hoffart, J.; Suchanek, F. M.; Berberich, K.; Lewis-Kelham, E.; de Melo, G.; Weikum, G. YAGO2: Exploring and querying world knowledge in time, space, context, and many languages. In: Proceedings of the 20th International Conference Companion on World Wide Web, 229-232, 2011.
DOI
[23]
Chang, A. X.; Funkhouser, T.; Guibas, L.; Hanrahan, P.; Huang, Q.; Li, Z.; Savarese, S.; Savva, M.; Song, S.; Su, H.; Xiao, J.; Yi, L.; Yu, F. ShapeNet: An information-rich 3D model repository. arXiv preprint arXiv:1512.03012, 2015.
[24]
Miller. G. A. WordNet: A lexical database for English. Communications of the ACM Vol. 38, No. 11, 39-41, 1995.
DOI Google Scholar
[25]
Fisher, M.; Savva, M.; Hanrahan, P. Characterizing structural relationships in scenes using graph kernels. ACM Transactions on Graphics Vol. 30, No. 4, Article No. 34, 2011.
DOI Google Scholar
[26]
Zeng, J.; Cheung, W. K.; Liu, J. Learning topic models by belief propagation. IEEE Transactions on Pattern Analysis and Machine Intelligence Vol. 35, No. 5, 1121-1134, 2013.
DOI Google Scholar
[27]
Kschischang, F. R.; Frey, B. J. Iterative decoding of compound codes by probability propagation in graphical models. IEEE Journal on Selected Areas in Communications Vol. 16, No. 2, 219-230, 1998.
DOI Google Scholar
[28]
Blei, D. M.; Ng, A. Y.; Jordan, M. I. Latent dirichlet allocation. Journal of Machine Learning Research Vol. 3, 993-1022, 2003.
Google Scholar
[29]
Abbeel, P.; Koller, D.; Ng, A. Y. Learning factor graphs in polynomial time and sample complexity. Journal of Machine Learning Research Vol. 7, 1743-1788, 2006.
Google Scholar
[30]
Gelfand, A. E.; Hills, S. E.; Racine-Poon, A.; Smith, A. F. M. Illustration of Bayesian inference in normal data models using Gibbs sampling. Journal of the American Statistical Association Vol. 85, No. 412, 972-985, 1990.
DOI Google Scholar
[31]
Schwarz, G. Estimating the dimension of a model. The Annals of Statistics Vol. 6, No. 2, 461-464, 1978.
DOI Google Scholar
[32]
Soleimani, H.; Miller, D. J. Parsimonious topic models with salient word discovery. IEEE Transactions on Knowledge and Data Engineering Vol. 27, No. 3, 824-837, 2015.
DOI Google Scholar
[33]
Lepage, G. P. A new algorithm for adaptive multidimensional integration. Journal of Com-putational Physics Vol. 27, No. 2, 192-203, 1978.
DOI Google Scholar
[34]
The SIMS 4. Available at https://www.ea.com/games/the-sims?isLocalized=true.
DOI
[35]
Su, H.; Maji, S.; Kalogerakis, E.; Learned-Miller, E. Multi-view convolutional neural networks for 3D shape recognition. In: Proceedings of the IEEE International Conference on Computer Vision, 945-953, 2015.
DOI
[36]
Ma, R.; Li, H.; Zou, C.; Liao, Z.; Tong, X.; Zhang, H. Action-driven 3D indoor scene evolution. ACM Transactions on Graphics Vol. 35, No. 6, Article No. 173, 2016.
DOI Google Scholar
[37]
Merrell, P.; Schkufza, E.; Li, Z.; Agrawala, M.; Koltun, V. Interactive furniture layout using interior design guidelines. ACM Transactions on Graphics Vol. 30, No. 4, Article No. 87, 2011.
DOI Google Scholar
[38]
Lin, Y.; Liu, Z.; Sun, M.; Liu, Y.; Zhu, X. Learning entity and relation embeddings for knowledge graph completion. In: Proceedings of the 29th AAAI Conference on Artificial Intelligence, 2181-2187, 2015.
DOI
[39]
Fisher, M.; Savva, M.; Li, Y.; Hanrahan, P.; Nieβner, M. Activity-centric scene synthesis for functional 3D scene modeling. ACM Transactions on Graphics Vol. 34, No. 6, Article No. 179, 2015.
DOI Google Scholar
Publication history
Copyright
Acknowledgements
Rights and permissions

Publication history

Revised: 08 January 2018
Accepted: 13 January 2018
Published: 21 March 2018
Issue date: June 2018

Copyright

© The Author(s) 2018

Acknowledgements

This work was supported by the National Key R&D Program of China (No. 2017YFB1002604), the National Natural Science Foundation of China (No. 61772298), a Research Grant of Beijing Higher Institution Engineering Research Center, and the Tsinghua–Tencent Joint Laboratory for Internet Innovation Technology.

Rights and permissions

This article is published with open access at Springerlink.com

The articles published in this journal are distributed under the terms of the Creative Commons Attribution 4.0 International License (http:// creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

Other papers from this open access journal are available free of charge from http://www.springer.com/journal/41095. To submit a manuscript, please go to https://www. editorialmanager.com/cvmj.

Return