Convolutional Neural Network-Based Virtual Reality Real-Time Interactive System Design for Unity3D

In this paper, the algorithm of the convolutional neural network is used for in-depth research and analysis of real-time interactivity, and a virtual reality real-time interactive system is designed based on Unity3D. Aiming at the problems of high computation and low efficiency of existing high-precision models, based on the lightweight network model, this paper proposes a real-time semantic segmentation method based on an asymmetric codec. The encoder part of the network adopts a newly designed bottleneck residual module based on depth-separable convolution, null convolution, and decomposition convolution to extract local and contextual information without increasing the computational effort. At the same time, channel rearrangement is introduced in the module to facilitate the interaction of information between channels. The approach proposed in this paper can present the architectural system simply and clearly and is more flexible to match various complex business relationships. The system design technology is based on the Spring MVC framework technology, using visualization technology design and implementation. The combination of the Spring MVC framework and Unity3D achieves the separation of the front and back ends of the system, which makes the system stable, real-time, visible, and efficient. Meanwhile, a newly designed global attention guidance module based on the attention mechanism is introduced at the jump connection between codecs to guide the low-level features of encoder structure and high-level features of decoder structure for better integration and accuracy improvement.