Air-to-Ground Occlusion Reasoning: 3-D Voxel Map Generation for Ground Traversability Analysis

In complex urban environments, traversable spaces beneath bridges and roadside trees are often obscured in UAV aerial imagery. To address this challenge, we propose a novel end-to-end Occlusion-Informed 3D Terrain Mapping (OI-3DTM) framework that enables accurate voxel-level modeling of hidden structures, providing a refined spatial foundation for traversability analysis. Existing methods largely rely on 2D semantic completion or expensive multi-view and LiDAR-based mapping, which either lack sufficient 3D expressiveness or fail to meet real-time and resource constraints. OI-3DTM integrates self-supervised monocular depth estimation with semantic segmentation and 3D reconstruction to explicitly infer traversable voxel structures in occluded scenes. A unified encoder–decoder network fuses RGB imagery with prior depth features, enhanced by a semantic-partitioned hybrid loss and confidence-ranked depth consistency, thereby improving depth accuracy in textureless or geometrically discontinuous regions. Furthermore, we introduce a geometry prior-informed voxel reasoning module that employs parameterized cavity curves to model the underlying spatial geometry of occluded regions, enabling the reconstruction of invisible structures. We evaluate OI-3DTM on three benchmark datasets for depth estimation and conduct 3D inference experiments on the UAV-LADIS dataset. Experimental results demonstrate that the proposed method consistently outperforms existing approaches in both depth estimation and 3D spatial reasoning, achieving a 61% improvement in IoU and reducing the cross-entropy error to 0.116 in occlusion scenarios. These results indicate that OI-3DTM significantly enhances occlusion-aware spatial perception and exhibits strong potential for practical deployment in UAV-based urban navigation applications.