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Linear kitchen layout design via machine learning

Part of: Perspectives on Data-Enabled Design - Design meets Data Science

Published online by Cambridge University Press:  09 February 2022

Jelena Pejic  and
Petar Pejic Open the ORCID record for Petar Pejic [Opens in a new window]
Jelena Pejic
Affiliation:
Computer Science Department, Faculty of Sciences and Mathematics, University of Nis, Nis, Serbia
Petar Pejic*
Affiliation:
Game Development Department, Faculty of Information Technology, Belgrade Metropolitan University, Belgrade, Serbia
*
Author for correspondence: Petar Pejic, E-mail: petar.pejic@metropolitan.ac.rs
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Abstract

The main objective of this paper is to develop a novel approach for linear kitchen layout design which utilizes information from existing layouts via machine learning algorithms. With the growing popularity of large-scale virtual 3D environments for architectural visualization and the game industry, the manual interior design of virtual scenes becomes prohibitively expensive in terms of time and resources. In our approach, the machine learning model automatically generates layout suggestions. The proposed procedural kitchen generation (PKG) model is a pipeline of six Machine Learning (ML) classifiers that are trained and tested on a kitchen layout dataset created by interior designers. The performances of the model are evaluated for the following classifiers: Random forest, Decision tree, AdaBoost, Naive Bayes, MLP, SVM, and L2 Logistic regression. Random forest, as the best performing classifier is used in the final PKG model, and integrated into Unity Engine for automatic 3D kitchen generation and presentation. The PKG model is evaluated in the quantitative and perceptual study, showing better performance than the prior rule-based method. The perceptual study results demonstrate that our tool can be used to speed up designer's work, improve communication with clients, and educate interior design students.

Keywords

Data-driven modeldata-enabled approachinterior designkitchen layout designmachine learningprocedural content generationrandom forest

Information

Type
Research Article
Information
AI EDAM , Volume 36 , 2022 , e9
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Copyright
Copyright © The Author(s), 2022. Published by Cambridge University Press

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References

Birch, J, Browne, SP, Jennings, VJ, Day, AM and Arnold, DB (2001) Rapid Procedural-Modelling of Architectural Structures. Athens, Greece: ACM, pp. 187–196.Google Scholar
Brenner, C (2000) Towards full automatic generation of city models. In: The International Archives of Photogrammetry. Remote Sensing and Spatial Information Sciences 33, 85–92.Google Scholar
Chaillou, S (2019) AI & Architecture, Towards a New Approach. s.l.: Harvard GSD.Google Scholar
Daniel, R, Wang, K and Lin, Y (2018) Fast and Flexible Indoor Scene Synthesis via Deep Convolutional Generative Models. Computer Science2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 6175–6183.Google Scholar
Di, X and Yu, P (2021) Multi Agent Reinforcement Learning of 3D Furniture Layout Simulation in Indoor Graphics Scenes. arXiv preprint arXiv:2102.09137v1.Google Scholar
Fisher, M, Ritchie, D, Savva, M, Funkhouser, T and Hanrahan, P (2012) Example-based synthesis of 3D object arrangements. ACM Transaction on Graphics 31, 1–11.CrossRefGoogle Scholar
Freiknecht, J and Effelsberg, W (2017) A survey on the procedural generation of virtual worlds. Multimodal Technologies and Interact 1, 27. https://doi.org/10.3390/mti1040027CrossRefGoogle Scholar
Fu, H, Cai, B, Gao, L, Zhang, L, Wang, J, Li, C, Xun, Z, Sun, C, Jia, R, Zhao, B and Zhang, H (2021) 3D-FRONT: 3D Furnished Rooms with layOuts and semaNTics. arXiv preprint arXiv:2107.06149v2.Google Scholar
Henderson, P, Subr, K and Ferrari, V (2019) Automatic generation of constrained furniture layouts.arXiv e-prints, arXiv:1711.10939.Google Scholar
Hepler, D, Wallach, P and Hepler, D (2012) Drafting and Design for Architecture & Construction. s.l.: Cengage Learning.Google Scholar
House, TO (2020) Read This Before Hiring a Kitchen Designer [Online]. Available at https://www.thisoldhouse.com/kitchens/21015837/read-this-before-hiring-a-kitchen-designer (accessed 2020).Google Scholar
Kan, P and Kaufmann, H (2017) Automated Interior Design Using a Genetic Algorithm. Gothenburg, Sweden: s.n.Google Scholar
Kan, P and Kaufmann, H (2018) Automatic Furniture Arrangement Using Greedy Cost Minimization. Tuebingen/Reutlingen, Germany: s.n.CrossRefGoogle Scholar
Katz, N (2011) Algorithmic modelling, parametric thinking. In Kocatürk, T and Medjdoub, B (eds), Distributed Intelligence in Design. Salford: Wiley-Blackwell, pp. 213–231.CrossRefGoogle Scholar
Lane, B (1963) Sunset Ideas for Remodeling Your Home. Birmingham, Alabama: Lane Book Co.Google Scholar
Li, J, Yang, J, Hertzmann, A, Zhang, J and Xu, T (2019 a) LayoutGAN: Generating Graphic Layouts with Wireframe Discriminators. s.l. arXiv:1901.06767v1 [cs.CV].Google Scholar
Li, M, Patil, AG, Xu, K, Chaudhuri, S, Khan, O, Shamir, A, Tu, C, Chen, B, Cohen-Or, D and Zhang, H (2019 b) GRAINS: generative recursive autoencoders for indoor scenes. ACM Transactions on Graphics 38, 1–16.Google Scholar
Made, A (2019) What Makes a GREAT Kitchen Designer? [Online]. Available at https://www.teknikakitchensandbaths.com/what-makes-a-great-kitchen-designer/ (accessed 2020).Google Scholar
Merrell, P, Schkufza, E, Merrell, M, Schkufza, E, Li, Z, Agrawala, M and Koltun, V (2011) Interactive furniture layout using interior design guidelines. ACM Transaction on Graphics 30, 1–10.CrossRefGoogle Scholar
Pejic, P, Jovanovic, D, Marinkovic, J and Krasic, S (2019 a) Parametric 3D modeling of I-shape kitchen. Journal of Industrial Design and Engineering Graphics 14, 155–158.Google Scholar
Pejic, P, Mikic, M and Milovanovic, J (2019 b) Automatic Rule-Based Kitchen Layout Design. Nis, Serbia: s.n.Google Scholar
Powell, C (2005) Architect's Pocket Book of Kitchen Design. Oxford: Architectural Press.Google Scholar
Powell, J and Svendsen, L (2011) Bungalow Kitchens. Utah: Gibbs Smith.Google Scholar
Qi, S, Zhu, Y, Huang, S, Jiang, C and Zhu, S-C (2018) Human-centric Indoor Scene Synthesis Using Stochastic Grammar. s.l.: Computer Vision and Pattern Recognition.CrossRefGoogle Scholar
Ranne, E (1950) Handbook of Kitchen Design. Champaign, IL, United States: Small Homes Council - University of Illinois Urbana-Champaign.Google Scholar
Research, H (2020) 2020 U.S. Houzz Kitchen Trends Study. s.l.: Houzz.Google Scholar
Ritchie, D, Wang, K and Lin, Y (2019) Fast and Flexible Indoor Scene Synthesis via Deep Convolutional Generative Models. Long Beach, CA: s.n.CrossRefGoogle Scholar
Rodrigues, F, Neto, J and Vidal, C (2015) Split Grammar Evolution for Procedural Modeling of Virtual Buildings. Sao Paulo, Brazil: IEEE, pp. 1–10.Google Scholar
Roh, Y, Heo, G and Wang, S (2021) A survey on data collection for machine learning: a big data - aI integration perspective. IEEE Transactionson Knowledge and Data Engineering 33, 1328–1347.CrossRefGoogle Scholar
Saldana, M and Johanson, C (2013) Procedural modeling for rapid-prototyping of multiple building phases. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-5/W1, 205–210.CrossRefGoogle Scholar
Schultz, C, Bhatt, M and Bormann, A (2017) Bridging qualitative spatial constraints and feature-based parametric modelling: expressing visibility and movement constraints. Advanced Engineering Informatics 31, 2–17.CrossRefGoogle Scholar
Stojakovic, V, Budak, I, Obradovic, R, Korolija-Crkvenjakov, D and Santosi, Z (2017) Parametric Modeling Applied to the Virtual Reconstruction of the Damaged Sculpture of St John Nepomuk in Petrovaradin. Rome, Italy: s.n.Google Scholar
Wang, K, Savva, M, Chang, A and Ritchie, D (2018) Deep convolutional priors for indoor scene synthesis. ACM Transactions on Graphics 37, Article No.: 70, 1–14.CrossRefGoogle Scholar
Wang, K, Lin, Y-A, Weissmann, B, Savva, M, Chang, AX and Ritchie, D (2019) PlanIT: planning and instantiating indoor scenes with relation graph and spatial prior networks. ACM Transactions on Graphics 38, Article No.: 132, 1–15.CrossRefGoogle Scholar
Wilson, M (1947) Patterns for Kitchen Cabinets. Oregon: Oregon State System of Higher Education.Google Scholar
Wormer, A (2003) Designing your own kitchen. Log Home Design 10, 41–45.Google Scholar
Wu, W, Fu, X, Tang, R, Wang, Y, Qi, Y and Liu, L (2019) Data-driven interior plan generation for residential buildings. ACM Transactions on Graphics 38, 1–12.Google Scholar
Zhang, Y and Ye, W (2019) Deep learning-based inverse method for layout design. Structural and Multidisciplinary Optimization 60, 527–536.CrossRefGoogle Scholar
Zhang, Z, Yang, Z, Ma, C, Luo, L, Huth, A, Vouga, E and Huang, Q (2018) Deep generative modeling for scene synthesis via hybrid representations. Transactions on Graphics 37, 1–18.Google Scholar
Zhang, S, Zhang, S, Liang, Y and Hall, P (2019) A survey of 3D indoor scene synthesis. Journal of Computer Science and Technology 34, 594–608.CrossRefGoogle Scholar
Zhu, J, Guo, Y and Ma, H (2018) A data-driven approach for furniture and indoor scene colorization. IEEE Transactions on Visualization and Computer Graphics, Vol. 24.CrossRefGoogle ScholarPubMed