KSG-Net: Key-Sparse and Global-Context Learning for Maritime 3D Ship Detection
Accurate 3D ship detection in maritime environments is critical for autonomous navigation, yet remains challenging due to large-scale vessel variations, sparse point clouds of small vessels, and severe sea-clutter interference. Existing methods, primarily based on 2D features or dense representations, struggle to balance detection accuracy and computational efficiency, while sparse 3D detectors designed for road scenes generalize poorly to maritime scenarios. This paper focuses on two key challenges in maritime LiDAR perception: weak feature representation for small and sparse vessels, and insufficient global structural modeling for large vessels due to the limited receptive field of loca...
| Authors | Zhouyuan Huai, Meiqi Wan, Yan Yang et al. |
|---|---|
| Primary category | cs.CV |
| Published | 2 September 2026 |
| Last updated at source | 2 September 2026 |
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