Research on Automated Product 3D Modeling Based on Deep Learning

This paper studies the automated product 3D modeling method based on deep learning and proposes an end-to-end deep learning framework for generating high-quality 3D models from raw point cloud data. By designing efficient data preprocessing and feature extraction methods, the geometric and topological features of the point cloud are extracted to provide reliable input for model training. The proposed model combines PointNet++ and variational autoencoder structures, introduces Chamfer distance and regularization loss functions, and significantly improves the geometric accuracy and surface smoothness of the generated mesh. Experimental results show that the model is superior to traditional methods in terms of generation quality and efficiency. The automated modeling process achieves seamless connection from data input to model output, reduces manual intervention, and is suitable for diversified product modeling tasks. This study provides effective technical support for automated 3D modeling in intelligent manufacturing and digital design.