Deep Learning-Based Scene Processing and Optimization for Virtual Reality Classroom Environments: A Study

Abstract

With the increasingly widespread application of Virtual Reality (VR) technology in the field of education, VR classroom models, characterized by their unique immersive experience, are considered an important direction for educational innovation. To maximize the educational effects of VR classrooms, efficient processing and optimization of scene images are essential. Currently, although many studies are devoted to the rendering techniques of static scenes, research on real-time processing and personalized layout optimization of dynamic interactive teaching scenes is still insufficient. This paper proposes innovative methods based on deep learning for two core issues in VR classrooms: scene image enhancement and visual layout optimization. First, by constructing an image enhancement generation model based on the U-net network, the clarity and detail richness of scene images are significantly improved. Second, this paper applies an improved Spatial Pyramid Pooling in Fast Regions with Convolutional Neural Networks (SPPF) structure from Yolo5 to scene layout and introduces a novel visual graph attention model (GAM), which can extract colors from input images and effectively apply them to visual interface design. These methods not only enhance the visual effects of scenes but also lay the foundation for building personalized teaching environments that meet the needs of different learners. This research provides a new perspective for the real-time processing and layout optimization of VR classroom scenes, which is of significant importance for advancing the development of educational technology.