Self-Supervised Point Cloud Completion With Feature Augmentation For Large-Scale Aerospace Components

Point Cloud Completion aims to reconstruct the complete 3D shapes based on partial 3D point clouds. Existing methods typically require either complete point clouds or multiple partial views of the same object. However, complete 3D point clouds or diverse partial point clouds for large-scale aerospace components are extremely challenging to acquire in practical industrial scenarios due to their complex geometries and substantial sizes. In this paper, we present a novel self-supervised framework with feature augmentation called FASSC for the aerospace field. Unlike traditional methods, FASSC only operates on a single incomplete point cloud per object. Specifically, this method divides the incomplete point cloud into different patches as input and predicts the missing patches by learning prior information from various parts of objects. By incorporating the Region-Aware Chamfer Distance and Normal Consistency Constraint, FASSC eliminates the need for complete point clouds. We also propose an Attention Feature Augmentation Module (AF A) that establishes spatial relationships among points by introducing self-attention mechanism to reveal the detailed geometry of 3D shapes. Experimental results demonstrate that our approach exhibits notable enhancements on multiple datasets.