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Article type: Research Article
Authors: Lv, Dihua
Affiliations: School of Urban Construction, Zhejiang Shuren University, Hangzhou, Zhejiang 310015, China | E-mail: [email protected]
Correspondence: [*] Corresponding author: School of Urban Construction, Zhejiang Shuren University, Hangzhou, Zhejiang 310015, China. E-mail: [email protected].
Abstract: With the development and maturity of virtual reality (VR for short) and artificial intelligence technology, panoramic VR roaming is applied to more and more industries. In the construction engineering industry, due to the complex structure of some key building nodes, the application of panoramic VR roaming technology modeling will play a role in significantly reducing the workload and understanding difficulty of design, construction and management personnel. Therefore, this study improves the parallax map generation method of the belief propagation (BP) algorithm in the panoramic VR roaming to use the form of limit matching and optimizes the energy function and matching primitives in it, so as to propose a spatial model construction method of the panoramic VR roaming based on the improved BP algorithm. The experimental results show that the panoramic VR roaming space model construction method proposed in this study can significantly improve the quality of spatial modeling of building node images, and the normalized values of structural similarity of VR roaming space models designed based on Improved belief propagation (IBP), GC, BP, and DP algorithms are 3.32, 3.23, 2.96, and 2.84, respectively, when the number of iterations is obtained 200 times, is also the highest among the compared methods, for example, when the number of samples is 400, the calculation time of IBP scheme is 14.73 ± 0.85 min, and the highest time of other three schemes is 6.86 ± 0.67 min. Therefore, this virtual space modeling method is designed to be more suitable for application scenarios that require lower computational timeliness.
Keywords: VR, building nodes, BP algorithm, attention mechanism
DOI: 10.3233/JCM-226695
Journal: Journal of Computational Methods in Sciences and Engineering, vol. 23, no. 3, pp. 1349-1361, 2023
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