Application of Virtual Reality in Biotechnological Processes: Enhancing Training and Simulation for Commercial Outcomes

Abstract

This study explores the enhancement of virtual reality (VR) training and simulation in biotechnological processes through the application of immersive multi-projection CAVE systems, integral to modern machine vision technologies. We detail the architectural design, hardware structure, and functional aspects of the CAVE system, alongside advanced techniques for image splicing correction—including geometric correction, color adjustment, and edge brightness enhancement. Utilizing Level of Detail (LOD) model generation and the triangular edge collapse algorithm, we optimize complex VR scenes for improved human-computer interaction. Our rendering comparison experiments validate the optimization strategy, demonstrating a significant enhancement in image quality with a 13.18 dB increase in the peak signal-to-noise ratio of the R-channel compared to traditional gamma function luminance correction. Moreover, our approach sustains high frame rate performance, maintaining stability between 55 to 61 frames per second despite increasing scene complexity. The integration of these technologies into immersive projection systems not only optimizes VR scene complexity but also significantly enhances the immersive experience for users in biotechnological training applications.