Multi-Scale Dynamic Weighted Fusion for Small-Sample Oil Seal Ring Point Cloud Completion with Transformers

Oil seals are vital components in industrial production, necessitating high-precision 3D reconstruction for automated geometric measurement and quality inspection. High-quality point cloud completion is integral to this process. However, existing methods heavily rely on large datasets and often yield sub-optimal outcomes—such as distorted geometry and uneven point distributions—under limited sample conditions, constraining their industrial applicability. To address this, we propose a point cloud completion network that integrates a dynamic weighted fusion of multi-scale features with Transformer enhancements. Our approach incorporates three key innovations: a multi-layer perceptron fused with EdgeConv to enhance local feature extraction for small-sample oil seal rings, a dynamic weighted fusion strategy to adaptively optimize global feature integration across varying missing rates of oil seal rings, and a Transformer-enhanced multi-layer perceptron to ensure geometric consistency by linking global and local features. These innovations collectively enable high-quality point cloud completion for small-sample oil seal rings, achieving significant improvements at a 25% missing rate, reducing CD by 46%, EMD by 49%, and MMD by 74% compared to PF-Net. Experiments on the ShapeNet-Part dataset further validate the model’s strong generalizability across diverse categories. Experimental results on the industrial oil seal ring dataset and the small-sample ShapeNet sub-dataset show that our approach exhibits highly competitive performance compared to existing models.