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 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.
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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.
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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.
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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
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DOI: https://doi.org/10.1007/s41095-018-0110-3
Keywords
- knowledge graph
- scene design
- structure learning
- parameter learning