Abstract
In today's society, information technology is widely used, and virtual reality technology, as one of the emerging frontier technologies, has entered a stage of rapid development. Virtual reality is the use of computer technology to simulate the real-life environment into a virtual simulation environment, with the help of special equipment to realize the natural interaction between users and technical environment, in which the tourism industry is the most widely used. In order to realize 3D virtual reality of tourist attractions and improve users' immersive experience in the process of interaction, the deep belief neural network is introduced to realize the target recognition and reconstruction in virtual reality. The results show that the algorithm has excellent performance in target recognition and target reconstruction, and deep belief networks improve the accuracy by 0.57% and 0.81% and the accuracy by 0.21% and 2.06%, respectively, compared with the current optimal algorithm in target recognition of 12 and 20 view regular projection images. Compared with the current optimal algorithm, deep belief networks are reduced by 0.2%, 3.7%, and 0.6%, respectively. The accuracy index was increased by 2%, 0.1%, and 0.1%, respectively. The above results show that the proposed algorithm based on the deep belief neural network can realize 3D virtual reality of complex scenes such as tourist attractions according to its excellent performance.
Highlights
Fuli SongReceived 8 July 2021; Revised 30 August 2021; Accepted 1 September 2021; Published 13 September 2021
Nowadays, the continuous breakthrough of information technology has brought great impact on people’s production and life. e rapid development of virtual reality technology provides a new idea for the transformation and development of traditional tourism form
Aiming at the 3D target recognition task of multiview of tourist attractions, a multiview feature fusion strategy based on the depth belief neural network is proposed. e diagram is shown in Figure 1. e method is composed of three modules: two-dimensional convolutional neural network (CNN), multilayer residual LSTM subnetwork [23], and multiview feature weighted fusion. e convolutional neural network (CNN) module is responsible for image feature extraction
Summary
Received 8 July 2021; Revised 30 August 2021; Accepted 1 September 2021; Published 13 September 2021. In order to realize 3D virtual reality of tourist attractions and improve users’ immersive experience in the process of interaction, the deep belief neural network is introduced to realize the target recognition and reconstruction in virtual reality. E results show that the algorithm has excellent performance in target recognition and target reconstruction, and deep belief networks improve the accuracy by 0.57% and 0.81% and the accuracy by 0.21% and 2.06%, respectively, compared with the current optimal algorithm in target recognition of 12 and 20 view regular projection images. Compared with the current optimal algorithm, deep belief networks are reduced by 0.2%, 3.7%, and 0.6%, respectively. E above results show that the proposed algorithm based on the deep belief neural network can realize 3D virtual reality of complex scenes such as tourist attractions according to its excellent performance Compared with the current optimal algorithm, deep belief networks are reduced by 0.2%, 3.7%, and 0.6%, respectively. e accuracy index was increased by 2%, 0.1%, and 0.1%, respectively. e above results show that the proposed algorithm based on the deep belief neural network can realize 3D virtual reality of complex scenes such as tourist attractions according to its excellent performance
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