Research on the Development of a Three-dimensional Visualization System for Optimizing Indoor Displays

This study develops a three-dimensional (3D) visualization system for optimizing indoor display layouts. The system integrates high-precision 3D modeling, real-time point cloud acquisition using RGB-D cameras and LiDAR sensors, semantic tagging, and object tracking, combined with metaheuristic optimization algorithms including Particle Swarm Optimization (PSO) and multi-objective Pareto modeling. By applying Euclidean distance-based clearance validation, weighted cost functions, and cognitive load theory, the system enhances layout accuracy, workflow efficiency, and user decision-making while reducing cognitive effort. Experiments demonstrate a 28% reduction in task completion time and a 35% lower subjective cognitive load compared with traditional 2D CAD tools. The framework is particularly suitable for wireless communication-enabled industrial environments and antenna-supported sensor networks, where real-time, reliable, and low-latency data transmission is critical for interactive visualization and optimization. Future integration with AR/VR and AI-based automation is proposed to further improve adaptability, responsiveness, and scalability in engineering applications.