JACIII Vol.23 No.3 pp. 390-395
doi: 10.20965/jaciii.2019.p0390


The Discussion on Interior Design Mode Based on 3D Virtual Vision Technology

Yanxing Zhang, Lei Li, and Beibei Liu

Hebei University of Architecture
No.13 Chaoyang West Street, Jingkai District, Zhangjiakou City, Hebei 075000, China

Corresponding author

May 18, 2018
July 24, 2018
May 20, 2019
three-dimensional, vision, interior design, MultiGen Creator

Three dimensional virtual vision technologies improves the realistic effect of interior design, improves the rationality of interior design, and guides interior decoration and graphic design. In this paper, MultiGen Creator 3D modeling technology is used to reconstruct the indoor landscape and form the plane image of interior design. At the same time, the edge matching method is used to divide and decompose the features of the interior design image, and the 3D vision of the interior design is reconstructed with the key points of the interior design. In addition, through the adaptive tracking and rendering technology, the fidelity and space utilization efficiency of the room plane design are improved. The simulation results show that 3D virtual vision technology has better guidance for interior design, visual effect and landscape color fusion.

Interior design effect presentation based on 3D virtual vision technology

Interior design effect presentation based on 3D virtual vision technology

Cite this article as:
Y. Zhang, L. Li, and B. Liu, “The Discussion on Interior Design Mode Based on 3D Virtual Vision Technology,” J. Adv. Comput. Intell. Intell. Inform., Vol.23 No.3, pp. 390-395, 2019.
Data files:
  1. [1] D. Benitez, “Performance of reconfigurable architecture for image-processing applications,” J. of Systems Architecture, Vol.49, Issues 4-6, pp. 193-210, 2013.
  2. [2] M. Yin, W. Liu, X. Zhao, Q.-W. Guo, and R.-F. Bai, “Image denoising using trivariate prior model in nonsubsampled dual-tree complex contourlet transform domain and non-local means filter in spatial domain,” Optik, Vol.124, No.24, pp. 6896-6904, 2013.
  3. [3] J. Cao, H.-S. Li, Q. Cai, and S.-L. Guo, “Research on feature extraction of image targets,” Computer Simulation, Vol.30, No.1, pp. 409-414, 2013.
  4. [4] W.-Q. Zeng, Y.-J. He, and X.-K. Cui, “MRI image segmentation method based on arisotropic diffusion and spatial FCM,” Application Research of Computers, Vol.31, No.1, pp. 316-320, 2014.
  5. [5] Z.-X. Cai, M. Peng, and L.-L. Yu, “Adaptive Incremental Principal Component Analysis Visual Tracking Method Based on Temporal Characteristics,” J. of Electronics & Information, Vol.37, No.11, pp. 2571-2577, 2015.
  6. [6] C. Wang, X.-Y. Tang, and S.-L. Gao, “Infrared image enhancement algorithm based on human vision,” Laser & Infrared, Vol.47, No.1, pp. 114-118, 2017.
  7. [7] J. Xie, Y. Xu, and X. Wang, “Vision measurement method based on Bayesian model and digital image correlation,” Laser Technology, Vol.40, No.6, pp. 866-870, 2016.

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Last updated on May. 19, 2024