Multi-Scale Point Cloud Completion Networks Incorporating Attention Mechanisms

Abstract. Point cloud completion, a critical task in 3D vision, aims to repair incomplete point cloud data caused by sensor limitations or environmental occlusions, thereby providing complete 3D structural information for downstream applications. Most existing methods employ global generation strategies to directly output complete point clouds, but these approaches frequently alter the original geometric structures, resulting in detail loss or increased noise. A novel attention-based multi-scale point cloud completion network is proposed to overcome these limitations. The first enhancement introduces a channel attention mechanism during multi-scale feature fusion, which strengthens the coordinated expression of local details and global semantics through adaptive weight allocation. The second improvement designs a hybrid loss function that combines Wasserstein GAN with gradient penalty and geometric consistency constraints, thereby enhancing both detail authenticity and structural coherence in generated point clouds. Experiments conducted on the ShapeNet-Part dataset demonstrate the effectiveness of the proposed method. The improved approach achieves a reduction in Chamfer Distance compared to PF-Net, with particularly enhanced robustness observed in completing complex structures such as hollow chair backs and thin-walled lampshades. These results validate the superiority of the proposed technical innovations in geometric detail preservation and structural integrity maintenance.