Research on Intelligent Optimization Image Recognition Algorithm in Computer-Aided 3D Modeling

How to generate and recognize 3D models efficiently and accurately is a key problem in image recognition. To solve this problem, Cubes Cycle Particle Swarm (CCPS), a new method combining CycleGAN, Marching Cubes (MC) and Particle Swarm Optimization (PSO), is proposed in this paper. CCPS method can capture key features in the image to achieve the conversion from 2D image to 3D model through CycleGAN, and Marching Cubes algorithm is used to generate smooth 3D surface according to voxel data. At the same time, PSO is used for intelligent optimization, and the optimal solution is found through particle swarm collaborative search, so as to ensure high reconstruction accuracy and improve the robustness and recognition efficiency of the model. Finally, through the comparison experiment and analysis with traditional image recognition algorithms, the experimental results show that CCPS method has significant advantages in improving image recognition accuracy, image recognition effect and enhancing system robustness. Especially in complex model reconstruction, CCPS method can generate and identify 3D models more accurately, and reduce the error rate and missing rate. This fully verifies the feasibility and effectiveness of this architecture in intelligent optimization of image recognition algorithm in computer-aided 3D modeling.