Research article Special Issues

Optimization of building model based on 5G virtual reality technology in computer vision software

  • Received: 28 April 2021 Accepted: 11 August 2021 Published: 10 September 2021
  • The 5G virtual reality system needs to interact with the user to draw the scene in real time. The contradiction between the complexity of the scene model and the real-time interaction is the main problem in the operation of the virtual reality system. The model optimization strategy of architectural scene in virtual reality design is studied, and the method of architectural scene model optimization is summarized. This article aims to study the optimization of computer vision software modeling through 5G virtual reality technology. In this paper, the optimization of the architectural model is studied by the method of image gray scale transformation, computer vision detection technology and virtual modeling technology. The four experiments are comprehensive evaluation and quantitative evaluation, comparison of channel estimation performance of different pilot structures, comparison of calculated and true values of external azimuth elements, and the effect of window-to-wall ratio on energy consumption per unit of residential building. The results show that hollow bricks of building materials have a great impact on the environment. The values of the three pixel coordinates X, Y, and Z calculated by the unit quaternion method are 1.27, 1.3, and -6.11, respectively, while the actual coordinate positions are 1.25, 1.37, and -6.22, respectively. It can be seen that the outer orientation element value calculated by the quaternion-based spatial rear intersection method is not much different from the actual value, and the correct result can be accurately calculated.

    Citation: Ziyou Zhuang. Optimization of building model based on 5G virtual reality technology in computer vision software[J]. Mathematical Biosciences and Engineering, 2021, 18(6): 7936-7954. doi: 10.3934/mbe.2021393

    Related Papers:

  • The 5G virtual reality system needs to interact with the user to draw the scene in real time. The contradiction between the complexity of the scene model and the real-time interaction is the main problem in the operation of the virtual reality system. The model optimization strategy of architectural scene in virtual reality design is studied, and the method of architectural scene model optimization is summarized. This article aims to study the optimization of computer vision software modeling through 5G virtual reality technology. In this paper, the optimization of the architectural model is studied by the method of image gray scale transformation, computer vision detection technology and virtual modeling technology. The four experiments are comprehensive evaluation and quantitative evaluation, comparison of channel estimation performance of different pilot structures, comparison of calculated and true values of external azimuth elements, and the effect of window-to-wall ratio on energy consumption per unit of residential building. The results show that hollow bricks of building materials have a great impact on the environment. The values of the three pixel coordinates X, Y, and Z calculated by the unit quaternion method are 1.27, 1.3, and -6.11, respectively, while the actual coordinate positions are 1.25, 1.37, and -6.22, respectively. It can be seen that the outer orientation element value calculated by the quaternion-based spatial rear intersection method is not much different from the actual value, and the correct result can be accurately calculated.



    加载中


    [1] E. A. Johnson, P. G. Voulgaris, L. A. Bergman, Multi-objective optimal structural control of the notre dame building model benchmark, Earthquake Eng. Struct. Dynam., 27 (2015), 1165-1187.
    [2] J. L. Maples-Keller, B. E. Bunnell, S. J. Kim, B. O. Rothbaum, The use of virtual reality technology in the treatment of anxiety and other psychiatric disorders, Harv. Rev. Psych., 25 (2017), 103-113. doi: 10.1097/HRP.0000000000000138
    [3] J. Q. Coburn, I. J. Freeman, J. L. Salmon, A review of the capabilities of current low-cost virtual reality technology and its potential to enhance the design process. J. Comput. Inf. Enc. Eng., 17 (2017), 1-15.
    [4] M. T. Sarıtaş, Chemistry teacher candidates acceptance and opinions about virtual reality technology for molecular geometry, Edu. Res. Rev., 10 (2015), 2745-2757. doi: 10.5897/ERR2015.2525
    [5] A. Williams, Reality check[virtual reality technology], Eng. Technol., 10 (2015), 52-55.
    [6] D. Cao, G. Li, W. Zhu, Q. Liu, X. Li, Virtual reality technology applied in digitalization of cultural heritage, Clust. Comput., 22 (2017), 1-12.
    [7] H. Chen, Research of virtools virtual reality technology to landscape designing, Open Construct. Build. Technol. J., 9 (2015), 164-169. doi: 10.2174/1874836801509010164
    [8] Y, Sang, Y. Zhu, H. Zhao, Study on an interactive truck crane simulation platform based on virtual reality technology, Int. J. Dis. Edu. Technol., 14 (2016), 64-78.
    [9] Y. Huang, Q. Huang, S. Ali, X. Zhai, X. Bi, R. Liu, Rehabilitation using virtual reality technology: A bibliometric analysis, 1996-2015, Scientometrics, 109 (2016), 1547-1559. doi: 10.1007/s11192-016-2117-9
    [10] Z. Liang, R. Shuang, Research on the value identification and protection of traditional village based on virtual reality technology, Boletin Tecn. Bull., 55 (2017), 592-600.
    [11] H. Zhang, H. Zheng, Research on interior design based on virtual reality technology, Boletin Tecn. Bull., 55 (2017), 380-385.
    [12] J. Yao, L. Wang, J. Zhao, H. Yuan, A modeling method for gas station simulation system based on virtual reality technology, J. Comput. Inf. Systems, 11 (2015), 3165-3171.
    [13] Z. Ding, Y. Liu, J. Choi, Q. Sun, M. Elkashlan, I. Chih-Lin, Application of non-orthogonal multiple access in lte and 5g networks, IEEE Commun. Magaz., 55 (2017), 185-191. doi: 10.1109/MCOM.2017.1500657CM
    [14] N. Al-Falahy, O. Y. Alani, Technologies for 5G networks: Challenges and opportunities, It Prof., 19 (2017), 12-20.
    [15] D. Liu, L. Wang, Y. Chen, M. Elkashlan, K. K. Wong, R. Schober, User association in 5g networks: A survey and an outlook, IEEE Commun. Sur. Tutor, 18 (2016), 1018-1044. doi: 10.1109/COMST.2016.2516538
    [16] K. Samdanis, X. Costa-Perez, V. Sciancalepore, From network sharing to multi-tenancy: The 5g network slice broker, IEEE Commun. Magaz., 54 (2016), 32-39.
    [17] S. Buzzi, I. Chih-Lin, T. E. Klein, H. V. Poor, C. Yang, A. Zappone, A survey of energy-efficient techniques for 5g networks and challenges ahead, IEEE J. Selected Areas Commun., 34 (2016), 69-709.
    [18] T. X. Tran, A. Hajisami, P. Pandey, Collaborative mobile edge computing in 5g networks: New paradigms, scenarios, and challenges, IEEE Commun. Magaz., 55 (2017), 54-61.
    [19] Z. Zhang, X. Chai, K. Long, Full duplex techniques for 5G networks: Self-interference cancellation, protocol design, and relay selection, IEEE Commun. Magaz., 53 (2015), 128-137.
    [20] D. J. Lee, P. Recabal, D. D. Sjoberg, Comparative effectiveness of targeted prostate biopsy using magnetic resonance imaging ultrasound fusion software and visual targeting: A prospective study, J. Urol., 196 (2016), 697-702. doi: 10.1016/j.juro.2016.03.149
    [21] A. Gonzalez-Torres, F. J. Garcia-Penalvo, R. Theron-Sanchez, Knowledge discovery in software teams by means of evolutionary visual software analytics, Ence Comp. Program, 121 (2016), 55-74. doi: 10.1016/j.scico.2015.09.005
    [22] D. F. Andersen, G. P. Richardson, Scripts for group model building, System Dynam. Rev., 13 (2015), 107-129.
    [23] J. Hansson, L. G. Mansson. M. Bath, The validity of using roc software for analysing visual grading characteristics data: an investigation based on the novel software VGC analyzer, Radiat. Prot. Dosime., 169 (2016), 54-59. doi: 10.1093/rpd/ncw035
    [24] D. Aerikis, T. Blaauskas, R. Damaeviius, UAREI: A model for formal description and visual representation /software gamification, Dyna (Medellin, Colombia), 84 (2017), 326-334.
    [25] P. Vugteveen, E. Rouwette, H. Stouten, Developing social-ecological system indicators using group model building, Ocean Coast. Manag., 109 (2015), 29-39. doi: 10.1016/j.ocecoaman.2015.02.011
  • Reader Comments
  • © 2021 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)
通讯作者: 陈斌, bchen63@163.com
  • 1. 

    沈阳化工大学材料科学与工程学院 沈阳 110142

  1. 本站搜索
  2. 百度学术搜索
  3. 万方数据库搜索
  4. CNKI搜索

Metrics

Article views(2548) PDF downloads(82) Cited by(3)

Article outline

Figures and Tables

Figures(4)  /  Tables(1)

Other Articles By Authors

/

DownLoad:  Full-Size Img  PowerPoint
Return
Return

Catalog