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.

翻译：本文提出了一种用于更新三维地理空间模型的新方法，专门针对大规模海事环境中的遮挡移除问题。传统的三维重建技术常面临动态物体（如车辆或船只）遮挡真实环境的问题，导致模型不准确或需要大量人工编辑。我们的方法利用深度学习技术，包括实例分割和生成式修复，直接修改三维网格的纹理和几何结构，无需昂贵的重新处理。通过选择性定位遮挡物体并保留静态元素，该方法提升了几何与视觉精度。此方法不仅保留了地图数据的结构和纹理细节，同时保持了与现有地理空间标准的兼容性，确保在不同数据集上的鲁棒性能。实验结果表明，该方法显著提升了三维模型的保真度，使其在海事态势感知及辅助信息动态显示领域具有高度适用性。