Design and Implementation of Point Cloud-Based Space Integration with Quality-Aware Optimization

We present a hybrid-metaverse system that integrates physical and virtual spaces through real-time point cloud processing. To address the challenges of high-bandwidth data transmission and quality management across multiple users, we introduce a ROS-based distributed architecture that combines Input Convex Neural Networks with ADMM optimization. Evaluation using the SUN RGB-D dataset under simulated 6 G conditions demonstrates our system's effectiveness, with PICNN achieving 93-94.6% accuracy in modeling user utility and the distributed approach reducing convergence time by up to 60% compared to centralized methods while maintaining sub-millisecond latency with 30 concurrent users. The results validate our framework's capability to manage real-time point cloud quality in next-generation network environments.