ICNN Based Distributed Optimization of 3D Point Cloud Quality for Real-Time Physical Space Sharing

Abstract

Hybrid style metaverses, integrating physical and virtual spaces, face a critical challenge in managing shared 3D object quality across multiple users with diverse preferences and limited network resources. This paper addresses the problem of allocating limited bandwidth for transmitting point cloud representations while maximizing overall user satisfaction. We propose a distributed optimization method that dynamically adjusts 3D object quality based on contextual importance, available resources, and user preferences. Our approach uses Input Convex Neural Networks (ICNN) to model user utility functions and employs the Alternating Direction Method of Multipliers (ADMM) for distributed optimization. Key advantages include scalability, adaptability, and improved quality of experience. Evaluation using real-world data captured by our team and open datasets demonstrate significant improvements in user satisfaction and resource utilization compared to baseline approaches. Our method achieves 93-94.6% accuracy in modeling user utility and shows up to 60% faster convergence for scenarios with 30 users, contributing to the balance between high-fidelity representation and efficient data management in hybrid-metaverses.