Abstract: Due to the inherent limitations of single-view scanning and the spatial occlusion effects of complex ship hull structures, existing data collection systems commonly face the technical bottleneck of extensive missing data in back-side point clouds. To address this challenge, this paper proposed a ship point cloud completion method based on symmetry priors. This method operated without the need for labeling data and utilized the symmetrical structural characteristics of ships as prior-driven knowledge to effectively complete the back-side point clouds. First, a feature extraction model of the longitudinal centerplane for various types of ship hull was established based on geometric topology analysis of the ships. Then, a symmetry transformation field generation algorithm was proposed to make a mirror completion for the missing point clouds along the longitudinal centerplane of the ship hull, thereby constructing a candidate point cloud set for completion. Finally, an average nearest neighbor quality assessment function between the candidate point clouds and the original point clouds was designed to robustly select the optimal completion result. Experimental results show that the proposed method effectively completes the back-side point clouds of typical ship types, such as sharp-prowed and flat-bottomed ships, without requiring any training samples, and it meets the requirements of real-time data collection scenarios.

Key words: symmetry-driven completion, point cloud denoising, unsupervised algorithm, feature extraction