用深度学习自动清除海图中遮挡物，提升三维地图精度。
Automatic occlusion removal from 3D maps for maritime situational awareness
- 结合实例分割与生成修复，直接修改3D网格的纹理和几何结构。
- 无需重处理即可消除动态船只等遮挡，显著提升模型保真度。
- 适合海事态势感知、实时辅助信息展示等应用场景。
我们提出一种更新3D地理空间模型的新方法，专门针对大规模海上环境中遮挡物的移除问题。传统3D重建技术常因动态物体（如车辆或船只）遮挡真实环境，导致模型失真或需大量人工修正。本方法利用深度学习技术，包括实例分割和生成式修复，直接修改3D网格的纹理与几何结构，无需昂贵的重新处理流程。通过仅处理遮挡物并保留静态要素，该方法同时提升了几何与视觉准确性。该方法不仅保持了地图数据的结构与纹理细节，还兼容现有地理空间标准，在多种数据集上均表现稳健。结果表明，3D模型保真度显著提升，适用于海事态势感知及辅助信息的动态呈现。
原文摘要 · Abstract (English)
We introduce a novel method for updating 3D geospatial models, specifically targeting occlusion removal in large-scale maritime environments. Traditional 3D reconstruction techniques often face problems with dynamic objects, like cars or vessels, that obscure the true environment, leading to inaccurate models or requiring extensive manual editing. Our approach leverages deep learning techniques, including instance segmentation and generative inpainting, to directly modify both the texture and geometry of 3D meshes without the need for costly reprocessing. By selectively targeting occluding objects and preserving static elements, the method enhances both geometric and visual accuracy. This approach not only preserves structural and textural details of map data but also maintains compatibility with current geospatial standards, ensuring robust performance across diverse datasets. The results demonstrate significant improvements in 3D model fidelity, making this method highly applicable for maritime situational awareness and the dynamic display of auxiliary information.
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