Attention-Enhanced Feature-Based Point Cloud Completion Network for Precision Parts

Area of Science:

• Computer Vision
• Geometric Deep Learning
• 3D Data Processing

Background:

• 3D scanning of precision parts often yields incomplete point cloud data due to occlusion and equipment limitations.
• This data sparsity leads to distorted or lost critical geometric features, hindering accurate analysis and reconstruction.
• Existing point cloud completion methods struggle with precision parts requiring high geometric fidelity.

Purpose of the Study:

• To develop an attention-enhanced feature-based point cloud completion network specifically for precision parts.
• To address the challenges of data sparsity and feature loss in 3D scanned precision components.
• To improve the accuracy and completeness of reconstructed 3D models from incomplete point cloud data.

Main Methods:

• An encoder-decoder architecture incorporating a curvature-weighted sampling feature extraction module and spatial attention mechanism.
• Multilevel feature fusion and hierarchical, progressive prediction of missing point cloud regions.
• An adversarial generation module with a biased attention mechanism for enhanced geometric sensitivity.

Main Results:

• The proposed network achieved superior performance on the ShapeNet-part dataset, with average errors (CD, Pred → GT, GT → Pred) of 4.663, 2.459, and 2.457, outperforming PF-Net.
• On a custom bearing ring dataset, the network demonstrated significant error reductions (9.3%, 16.3%, 16.2%) compared to PF-Net.
• The network successfully completed point clouds for various missing parts, proving its robustness across different precision components.

Conclusions:

• The attention-enhanced network effectively addresses point cloud completion challenges for precision parts.
• The proposed method achieves state-of-the-art results, outperforming existing networks in accuracy and completeness.
• The network's robustness makes it suitable for diverse precision part reconstruction tasks.