Design of Cloud-Based Complex VR 3D Scene Interaction Based on Attribute Preferences

This paper presents a novel approach to enhancing real-time 3D design collaboration in cloud-rendered virtual reality (VR) environments through adaptive interface personalization and efficient resource management. The study leverages Reinforcement Learning (RL) and Particle Swarm Optimization (PSO) to address critical challenges such as user interface adaptability and cloud resource allocation. Our methodology incorporates an RL framework for dynamic interface personalization, which adjusts in real-time based on user interactions and feedback. The RL agent continuously learns from user behavior to optimize the interface, leading to improved navigation, reduced latency, and higher user satisfaction. Concurrently, PSO is employed to optimize cloud resource allocation, managing CPU, GPU, and bandwidth to minimize latency and enhance performance efficiency. Key findings indicate that the RL-based adaptive interface significantly improved user interaction, with reductions in latency up to 30% and increased task completion rates by 50%. PSO optimization led to a 17% to 23% improvement in resource utilization, ensuring a responsive and efficient VR environment even under high load conditions. The implications of these findings are substantial for real-time 3D design collaboration. By integrating RL for personalization and PSO for resource management, our approach facilitates a more intuitive and seamless collaborative experience, enhancing productivity and satisfaction. This research sets a precedent for future advancements in VR environments, emphasizing the value of combining advanced algorithms to address complex challenges in immersive technology.