PST-Net: Point Cloud Completion Network Based on Local Geometric Feature Reuse and Neighboring Recovery with Taylor Approximation

Transformer has recently been introduced into point cloud completion and achieved inspirational performance on 3D point cloud generation. However, the low-level local geometric features are ignored in the existing feature extraction network, and this leads to the loss of geometry in the recovery results. Meanwhile, FoldingNet models that estimate neighborhood information from predicted center points cannot effectively recover geometric information. In this paper, we propose a point cloud completion network based on local geometric feature reuse and neighboring recovery with Taylor approximation (PST-Net). Specifically, a feature extraction network named Skip-DGCNN is constructed to integrate local and global geometric features to reduce the geometry loss during the feature extraction. In addition, we propose a computational model through Taylor approximation to recover the geometry information in the neighborhood of the prediction center. Moreover, we design the TSMB module corresponding to Taylor's approximation to maintain the end-to-end training mode. The proposed method is extensively evaluated and compared with previous methods on three datasets including PCN, ShapeNet-55 and ShapeNet-34. The proposed model outperforms the state-of-the-art (SOTA) and PoinTr on ShapeNet-55 and ShapeNet-34. The complexity analysis on the PCN dataset shows that the number of FLOPs of our approach is 60.79% lower than that of the SOTA. Visual comparisons demonstrate that the proposed method can effectively and accurately complete the geometry of missing parts.