Abstract
In this paper, we propose an assistive model that supports professional interior designers to produce industrial interior decoration solutions and to meet the personalized preferences of the property owners. The proposed model is able to automatically produce the layout of objects of a particular indoor scene according to property owners’ preferences. In particular, the model consists of the extraction of abstract graph, conditional graph generation, and conditional scene instantiation. We provide an interior layout dataset that contains real-world 11000 designs from professional designers. Our numerical results on the dataset demonstrate the effectiveness of the proposed model compared with the state-of-art methods.
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References
Dai, A., Ritchie, D., Bokeloh, M., Reed, S., Sturm, J., Nießner, M.: ScanComplete: large-scale scene completion and semantic segmentation for 3D scans. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2018
Fisher, M., Ritchie, D., Savva, M., Funkhouser, T., Hanrahan, P.: Example-based synthesis of 3d object arrangements. ACM Trans. Graph. 31(6) (2012). https://doi.org/10.1145/2366145.2366154
Gilmer, J., Schoenholz, S.S., Riley, P.F., Vinyals, O., Dahl, G.E.: Neural message passing for quantum chemistry. CoRR (2017)
Gordon, D., Kembhavi, A., Rastegari, M., Redmon, J., Fox, D., Farhadi, A.: IQA: visual question answering in interactive environments. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2018
Jiang, B., Zhang, Z., Lin, D., Tang, J., Luo, B.: Semi-supervised learning with graph learning-convolutional networks. In: 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 11305–11312 (2019)
Kermani, Z., Liao, Z., Tan, P., Zhang, H.: Learning 3D scene synthesis from annotated RGB-D images. Comput. Graph. Forum 35, 197–206 (2016). https://doi.org/10.1111/cgf.12976
Li, M., et al.: Grains: generative recursive autoencoders for indoor scenes. ACM Trans. Graph. 38(2) (2019). https://doi.org/10.1145/3303766
Merrell, P., Schkufza, E., Li, Z., Agrawala, M., Koltun, V.: Interactive furniture layout using interior design guidelines. In: SIGGRAPH 2011, August (2011, to appear). http://graphics.berkeley.edu/papers/Merrell-IFL-2011-08/
Qi, S., Zhu, Y., Huang, S., Jiang, C., Zhu, S.C.: Human-centric indoor scene synthesis using stochastic grammar. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2018
Wang, K., Lin, Y.A., Weissmann, B., Savva, M., Chang, A.X., Ritchie, D.: Planit: planning and instantiating indoor scenes with relation graph and spatial prior networks. ACM Trans. Graph. 38(4) (2019). https://doi.org/10.1145/3306346.3322941
Xu, K., et al.: Constraint-based automatic placement for scene composition. In: Graphics Interface, pp. 25–34 (2002)
Yu, L.F., Yeung, S.K., Tang, C.K., Terzopoulos, D., Chan, T.F., Osher, S.J.: Make it home: automatic optimization of furniture arrangement. ACM Trans. Graph. 30(4) (2011). https://doi.org/10.1145/2010324.1964981
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Di, X. et al. (2020). Structural Plan of Indoor Scenes with Personalized Preferences. In: Bartoli, A., Fusiello, A. (eds) Computer Vision – ECCV 2020 Workshops. ECCV 2020. Lecture Notes in Computer Science(), vol 12538. Springer, Cham. https://doi.org/10.1007/978-3-030-66823-5_27
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DOI: https://doi.org/10.1007/978-3-030-66823-5_27
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