Deep Learning-Based Point Cloud Reconstruction Method for CAD Models

In the field of 3D modeling, point cloud reconstruction technology is a crucial step in generating high-precision 3D models from collected point cloud data. Traditional reconstruction methods often rely on complex geometric calculations and extensive manual adjustments, making them susceptible to noise and point cloud accuracy issues, which can lead to imprecise results. To address these challenges, this paper proposes a point cloud reconstruction method based on the DeepCAD autoencoder and PointNet++ neural network. The process involves calculating normal vectors for the point cloud, constructing a self-attention mechanism module, adding a geometric consistency loss function, using PointNet for feature extraction and conversion into latent vectors for CAD models, decoding these latent vectors into command sequences via an autoencoder, and finally converting the command sequences into STEP files using PythonOCC.The results demonstrate that our method significantly improves reconstruction accuracy and robustness while maintaining geometric precision. Additionally, our method can rapidly generate high-quality 3D models when handling large-scale point cloud data. This provides a new solution for the automated processing of point cloud data, with broad application prospects. Further experimental analysis reveals that our method effectively enhances automation. This offers new insights for the future development of 3D modeling technology.