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 weaklysupervised 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.
Autodesk REVIT. Available at https://www.autodesk. com/products/revit-family/overview.
SketchUp. Available at https://www.sketchup.com/.
ARCHICAD. Available at http://www.graphisoft. com/archicad/.
Fisher, M.; Hanrahan, P. Context-based search for 3D models. ACM Transactions on Graphics Vol. 29, No. 6, Article No. 182, 2010.
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.
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.
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.
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.
Savva, M.; Chang, A. X.; Agrawala, M. Scenesuggest: Context-driven 3D scene design. arXiv preprint arXiv:1703.00061, 2017.
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.
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.
Xu, K.; Chen, K.; Fu, H.; Sun, W.-L.; Hu, S.- M. Sketch2Scene: Sketch-based co-retrieval and coplacement of 3D models. ACM Transactions on Graphics Vol. 32, No. 4, Article No. 123, 2013.
Chang, A. X.; Eric, M.; Savva, M.; Manning, C. D. SceneSeer: 3D scene design with natural language. arXiv preprint arXiv:1703.00050, 2017.
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.
Mihalcea, R.; Radev, D. Graph-based Natural Language Processing and Information Retrieval. Cambridge University Press, 2011.
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.
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.
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.
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.
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.
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.
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.
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.
Miller. G. A. WordNet: A lexical database for English. Communications of the ACM Vol. 38, No. 11, 39–41, 1995.
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.
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.
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.
Blei, D. M.; Ng, A. Y.; Jordan, M. I. Latent dirichlet allocation. Journal of Machine Learning Research Vol. 3, 993–1022, 2003.
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.
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.
Schwarz, G. Estimating the dimension of a model. The Annals of Statistics Vol. 6, No. 2, 461–464, 1978.
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.
Lepage, G. P. A new algorithm for adaptive multidimensional integration. Journal of Computational Physics Vol. 27, No. 2, 192–203, 1978.
The SIMS 4. Available at https://www.ea.com/games/ the-sims?isLocalized=true.
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.
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.
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.
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.
Fisher, M.; Savva, M.; Li, Y.; Hanrahan, P.; Niener, M. Activity-centric scene synthesis for functional 3D scene modeling. ACM Transactions on Graphics Vol. 34, No. 6, Article No. 179, 2015.
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.
This article is published with open access at Springerlink.com
Yuan Liang is a Ph.D. candidate in the Department of Computer Science and Technology, Tsinghua University, China. His research interests include interactive multimedia analysis and geometry processing. He received his B.S. degree from the Department of Computer Science and Technology, Tsinghua University.
Fei Xu received his B.S. degree from Guangdong University of Technology. He is an interdisciplinary master student in the Department of Information Art and Design, Tsinghua University. His interests include interactive multimedia analysis, VR and AR, and human computer interaction.
Song-Hai Zhang received his Ph.D. degree from Tsinghua University, China, in 2007. He is currently an associate professor of computer science in Tsinghua University. His research interests include image and video processing as well as geometric computing.
Yu-Kun Lai received his bachelor and Ph.D. degrees in computer science from Tsinghua University, China, in 2003 and 2008, respectively. He is currently a senior lecturer at the School of Computer Science & Informatics, Cardiff University. His research interests include computer graphics, geometry processing, image processing, and computer vision. He is on the Editorial Board of The Visual Computer.
Taijiang Mu is currently a postdoctoral researcher in the Department of Computer Science and Technology, Tsinghua University, where he received his Ph.D. and B.S. degrees in 2016 and 2011, respectively. His research area is computer graphics, mainly focusing on stereoscopic image and video processing, and stereoscopic perception.
About this article
Cite this article
Liang, Y., Xu, F., Zhang, SH. et al. Knowledge graph construction with structure and parameter learning for indoor scene design. Comp. Visual Media 4, 123–137 (2018). https://doi.org/10.1007/s41095-018-0110-3
- knowledge graph
- scene design
- structure learning
- parameter learning