Abstract. With the rapid advancement of Urban Air Mobility (UAM), vision-only UAV object detectors like YOLO often suffer from "context blindness" in complex urban canyons, leading to logical fallacies or missed occluded targets. To address these limitations, this paper proposes an innovative Geo-Visual Fusion (GVF) enhancement strategy. By leveraging high-definition (HD) city maps as deterministic geo-spatial priors, we introduce a Geo-spatial Contextual Reasoning (GCR) module to post-process raw visual outputs. This framework incorporates a Semantic Compatibility Matrix (SCM) to eliminate geographically implausible false positives and a Bayesian enhancement rule to boost the confidence of occluded targets. Experimental validation in the Baibuting Community, Wuhan, demonstrates that the GVF framework significantly outperforms the baseline YOLOv11, achieving perfect recall and precision in the test sequence. Furthermore, the 2D vector-based indexing ensures high computational efficiency for edge computing deployment on platforms like the DJI Dock 3. Finally, a closed-loop "reverse empowerment" mechanism for HD map updates is discussed. This work effectively bridges probabilistic computer vision and deterministic geospatial constraints for reliable UAV perception.
Shenman Zhang, Qing-Shan Peng, Meng-Meng Duan et al.· The International Archives o...· 0 citations
Abstract. To address the issues of geometric distortion and loss of details in 3D modeling for complex cultural heritage scenes, this paper proposes an improved 3D Gaussian Splatting (3DGS) reconstruction method that integrates LiDAR and illumination-awareness. First, high-precision 3D coordinates from LiDAR point clouds are utilized to guide the initialization of Gaussian Primitives, establishing a precise geometric foundation and effectively overcoming deformation on weakly textured surfaces. Second, an illumination-aware network is constructed to dynamically adjust appearance parameters by combining global illumination from images with LiDAR reflectance intensity. This decouples complex lighting from material properties, accurately reproducing the unique textures of artifacts. Finally, a multi-dimensional joint loss function incorporating photometric, scale, and appearance smoothness constraints is introduced to collaboratively optimize scene geometry, appearance, and camera poses. Experimental results on indoor and outdoor cultural heritage preservation scenarios demonstrate that the proposed method significantly outperforms various comparative algorithms in terms of both visual fidelity and geometric accuracy. The quantitative and qualitative evaluations confirm that our approach effectively eliminates geometric distortions and recovers fine texture details, providing an efficient and reliable technical solution for the digital preservation of cultural heritage.
Xiao Liu, Xinying Li, Wan Li et al.· ISPRS Annals of the Photogra...· 0 citations
