Image Inpainting-Based Point Cloud Restoration for Enhancing Tactical Classification of Unmanned Surface Vehicles

The operational effectiveness of Unmanned Surface Vehicles (USVs) in modern naval scenarios depends on robust situational awareness. While LiDAR sensors are integral to 3D perception, their performance is frequently affected by incomplete data resulting from long-range sparsity and target occlusion. This study investigates a framework to restore incomplete point clouds to support improved surface vessel classification. The framework first estimates the target's heading angle using a 2D area projection technique, combined with a descriptor to address orientation ambiguity. Subsequently, the 3D point cloud is converted into a 2D multi-channel image representation to leverage a deep learning-based image inpainting algorithm for data restoration. Finally, a high-density keypoint extraction method is applied to the completed point cloud to generate features for classification. This image-based approach is designed to prioritize computational efficiency and inference speed, facilitating deployment on resource-constrained maritime platforms. Experiments conducted on a simulator dataset reveal that the classification of restored point clouds yields higher accuracy compared to using the original, incomplete LiDAR data, particularly at extended distances (>70 m) and challenging aspect angles (0° and 180°). The results suggest the framework's potential to address perception failures in sparse data scenarios, thereby supporting the operational envelope of USVs in contested environments.