Encyclopedia of Computer Graphics and Games

Living Edition
| Editors: Newton Lee

3D Room Layout System Using IEC (Interactive Evaluational Computation)

Living reference work entry
DOI: https://doi.org/10.1007/978-3-319-08234-9_34-1
  • 486 Downloads

Synonyms

Definition

IEC is the interactive optimization system incorporating human tasks. 3D room layout system using IEC is the application of IEC, and it evolves layout according to the user preferences.

Introduction

Designers usually build renderings to create a new layout, and they reorganize it to fit a customer need. Furthermore, customers can understand shapes intuitively if they provide the 3D room layout. Numerical optimization approaches that optimize parameters constructing the 3D room layout can automate these works. However, it is difficult to create a model equation that emulates human thoughts because it is a subjective personal preference. Therefore, optimization systems incorporate the human tasks that evaluate the fitness of solutions manually. These systems usually use interactive evolutionary computation (IEC). This approach is similar to the process of improvement in animal and crop...

This is a preview of subscription content, log in to check access.

References

  1. Akase, R., Nishino, H., Kagawa, T., Utsumiya, K., Okada, Y.: An avatar motion generation method based on inverse kinematics and interactive evolutionary computation. Proc. of the 4th Int. Workshop on Virtual Environment and Network Oriented Applications (VENOA-2012) of CISIS-2012, pp. 741–746. IEEE CS Press (2012)Google Scholar
  2. Akase, R., Okada, Y.: Automatic 3D furniture layout based on interactive evolutionary computation. Proc. of the 5th Int. Workshop on Virtual Environment and Network Oriented Applications of CISIS-2013, pp. 726–731. IEEE CS Press (2013)Google Scholar
  3. Akase, R., Okada, Y.: Web-based multiuser 3D room layout system using inter- active evolutionary computation with conjoint analysis. The 7th Int. Symposium on Visual Information Communication and Interaction (VINCI-2014), pp. 178–187. ACM Press (2014)Google Scholar
  4. Akazawa, Y., Okada, Y., Niijima, K.: Automatic 3D scene generation based on contact constraints. Proc. Conf. on Computer Graphics and Artificial Intelligence, pp. 593–598. (2005)Google Scholar
  5. Akazawa, Y., Okada, Y., Niijima, K.: Interactive learning interface for automatic 3D scene generation. Proc. of 7th Int. Conf. on Intelligent Games and Simulation, pp. 30–35. (2006)Google Scholar
  6. Back, T.: Evolutionary Algorithms in Theory and Practice. Oxford University Press, New York (1996)Google Scholar
  7. Bentley, P.: Evolutionary Design by Computers, pp. 1–73. Morgan Kaufmann, San Francisco (1999)zbMATHGoogle Scholar
  8. Bezdek, J.C., Ehrlich, R., Full, W.: FCM: The fuzzy c-means clustering algorithm. Comput. Geosci. 10, 191–203 (1984)CrossRefGoogle Scholar
  9. Calderon, C., Cavazza, M. Diaz, D.: A new approach to virtual design for spatial configuration problems. Proceedings. Seventh International Conference on Information Visualization, pp. 518–523. (2003)Google Scholar
  10. Coyne, B., Sproat, R.: Words eye: An automatic text-to-scene conversion system, ACM SIGGRAPH 2001. Proceedings of the 28th Annual Conference on Computer Graphics and Interactive Techniques, pp. 487–496. (2001)Google Scholar
  11. Dawkins, R.: The Blind Watchmaker. W.W. Norton, New York (1986)Google Scholar
  12. Funaki, R., Takagi, H.: Application of gravity vectors and moving vectors for the acceleration of both differential evolution and interactive differential evolution. Int. Conf. on Genetic and Evolutionary Computing (ICGEC), pp. 287–290. (2011)Google Scholar
  13. Garcia, H.L., Arauzo, A.A., Salas, M.L., Pierreval, H., Corchado, E.: Facility layout design using a multi-objective interactive genetic algorithm to support the DM, Expert Systems, pp. 1–14. (2013)Google Scholar
  14. Ghannem, A., Ghizlane, B., Marouane, K.: Model Refactoring Using Interactive Genetic Algorithm, Search Based Software Engineering, pp. 96–110. Springer, Berlin (2013)CrossRefGoogle Scholar
  15. Kim, Y., Mitra, N., Yan, D., Guibas, L.: Acquiring 3D indoor environments with variability and repetition. ACM Trans. Graph. 31(6), 138 (2012)CrossRefGoogle Scholar
  16. Koza, J.R.: Genetic Programming: On the Programming of Computers by Means of Natural Selection, vol. 1. MIT press, Cambridge (1992)Google Scholar
  17. Lap-Fai, Y., Sai-Kit, Y., Chi-Keung, T., Demetri, T., Tony, F.C., Stanley, O.: Make it home: Automatic optimization of furniture arrangement. ACM Trans. Graph. 30(4), 86 (2011)Google Scholar
  18. Miki, M., Hiroyasu, T., Tomioka, H.: Parallel distributed interactive genetic algorithm. Proc. Jpn. Soc. Mech. Eng. Des. Syst. Conf. 13, 140–143 (2003)Google Scholar
  19. Miki, M., Yamamoto, Y., Wake, S., Hiroyasu, T.: Global asynchronous distributed interactive genetic algorithm. In: Systems, Man and Cybernetics. IEEE International Conference on, vol. 4, pp. 3481–3485. IEEE, Taipei (2006)Google Scholar
  20. Mok, T.P., Wang, X.X., Xu, J., Kwok, Y.L.: Fashion sketch design by inter- active genetic algorithms. AIP Conference Proceedings, pp. 357–364. (2012)Google Scholar
  21. Nan, L., Xie, K., Sharf, A.: A search-classify approach for cluttered indoor scene understanding. ACM Trans. Graph. 31(6), 137 (2012)CrossRefGoogle Scholar
  22. Ono, I., Kobayashi, S., Yoshida, K.: Optimal lens design by real-coded genetic algorithms using UNDX. Comput. Methods Appl. Mech. Eng. 186(2), 483–497 (2000)CrossRefzbMATHGoogle Scholar
  23. Parish, Y., Muller, P.: Procedural modeling of cities. Proceedings of the 28th Annual Conference on Computer Graphics and Interactive Techniques, pp. 301–308. ACM (2001)Google Scholar
  24. Pei, Y., Takagi, H.: Triple and quadruple comparison-based interactive differential evolution and differential evolution. In: Proceedings of the Twelfth Workshop on Foundations of Genetic Algorithms XII, pp. 173–182. ACM, Australia (2013)Google Scholar
  25. Shao, T., et al.: An interactive approach to semantic modeling of indoor scenes with an RGBD camera. ACM Trans. Graph. 31(6), 136 (2012)CrossRefGoogle Scholar
  26. Sorn, D., Sunisa, R.: Web page template design using interactive genetic algorithm. In: Computer Science and Engineering Conference (ICSEC). 2013 International, IEEE, pp. 206–211. (2013)Google Scholar
  27. Storn, R., Price, K.: Differential evolution – a simple and efficient heuristic for global optimization over continuous spaces. J. Glob. Optim. 11(4), 341–359 (1997)CrossRefMathSciNetzbMATHGoogle Scholar
  28. Takagi, H.: Perspective on interactive evolutionary computing. J. Jpn. Soc. Artif. Intell. 13(5), 692–703 (1998)Google Scholar
  29. Takagi, H.: Interactive evolutionary computation: Fusion of the capabilities of EC optimization and human evaluation. Proc. IEEE 89(9), 1275–1296 (2001)CrossRefGoogle Scholar
  30. Takagi, H., Pallez, D.: Paired Comparison Based Interactive Differential Evolution, Nature and Biologically Inspired Computing. pp. 475–480. India (2009)Google Scholar

Authors and Affiliations

  1. 1.Graduate School of Information Science and Electrical EngineeringKyushu UniversityFukuokaJapan
  2. 2.Innovation Center for Educational ResourceKyushu UniversityFukuokaJapan