A point contextual transformer network for point cloud completion

Point cloud completion is an essential task for recovering a complete point cloud from its partial observation to support downstream applications, such as object detection and reconstruction. Existing point cloud completion networks primarily rely on large-scale datasets to learn the mapping between the partial shapes and the complete shapes. They often adopt a multi-stage strategy to progressively generate complete point clouds with finer details. However, underutilization of shape priors and complex modelling frameworks still plague these networks. To address these issues, we innovatively propose a point contextual transformer (PCoT) for point cloud completion (PCoT-Net). We design the PCoT to adaptively fuse static and dynamic point contextual information. This allows for the effective capture of fine-grained local contextual features. We then propose a one-stage network with a feature completion module to directly generate credible and detailed complete point cloud results. Furthermore, we incorporate External Attention (EA) into the feature completion module, which is lightweight and further improves the performance of learning complete features and reconstructing the complete point cloud. Extensive experiments on various datasets validate the effectiveness of our PCoT-based approach and the EA-enhanced feature completion module, which achieves superior quantitative performance in Chamfer Distance (CD) and F1-Score. In comparison to PMP-Net++ (Wen et al., 2022), our method improves the F1-Score by 0.010, 0.022, and 0.026, and reduces the CD by 0.16, 0.95, and 1.74 on the MVP, CRN, and ScanNet datasets, respectively, while providing visually superior results, capturing more fine-grained details and producing smoother reconstructed surfaces.