Research on AI 3D Models Analysis Based on Multi-Dimensional

This study presents a comprehensive multidimensional evaluation framework (MQEF) for assessing AI-generated 3D assets, addressing critical gaps in current evaluation methodologies. Our framework systematically analyzes three key dimensions: visual quality (PSNR up to 35.7dB), computational efficiency (generation times 30-120s), and application-specific requirements. Through rigorous benchmarks of leading models including Craftsman3D and MaterialSeg3D, we identify fundamental trade-offs between quality and performance. The framework introduces a dynamic weighting mechanism that adapts to different production scenarios, demonstrating that optimal model selection depends on specific use cases. Experimental results validate the framework's effectiveness across diverse applications, from high-fidelity VFX to real-time game asset generation, providing actionable insights for both researchers and industry practitioners.