SAPCNet: symmetry-aware point cloud completion network

In fields such as autonomous driving and 3D object reconstruction, complete 3D point cloud data is crucial. Existing methods often directly reconstruct complete point clouds from partial ones, overlooking the structural similarities within the point cloud data. To tackle this challenge, we introduce SAPCNet, an innovative network architecture that leverages the symmetry and structural similarities of point clouds to infer missing parts from known parts. We assume that incomplete point clouds share topological similarities with their symmetric counterparts. Through a feature-position pair extractor, we extract the center point and its features, which are then fused into an existing proxy. With our proposed symmetry-aware transformer, we analyze these features to accurately predict the positions of symmetric point proxies. In addition, we introduce a fine-seed generator to bridge the gap between the predicted missing point cloud and the original input point cloud, ensuring that the reconstructed point cloud maintains the geometric structure and visual characteristics consistent with the original data. Through a series of qualitative and quantitative evaluations, SAPCNet demonstrates outstanding performance across multiple datasets.