Automatic Repair of UAV Photogrammetric Model in Road Environment

Uncrewed aerial vehicle (UAV) photogrammetry has been widely applied in urban reconstruction for acquiring 3-D models. However, during the UAV imaging process, there is occlusion on the ground, leading to severe geometric distortion and texture errors in the road areas of the photogrammetric models. Existing methods are unable to automatically obtain more refined repair effects. Considering this, we have proposed a method for automatically repairing photogrammetric models by removing occlusions to enhance mesh quality. The method first used an improved detection approach tailored for small targets in UAV imagery to identify the target location. Based on this, a local shadow adaptive extraction strategy was used to rapidly extract the target’s shadow, mitigating the impact of shadows on the model’s texture. Subsequently, the extracted target and its shadow were fed into the LaMa repair model to complete the texture completion. By detecting the target’s position in the imagery and using back projection, the 3-D vertices of the mesh target were determined. The surrounding mesh points were then used to flatten it, restoring the 3-D mesh to its state before the target was present. The completed texture was then remapped onto the flattened mesh, achieving the automatic repair of the photogrammetric model. The results indicate that the target detection method proposed in this study, on the VisDrone-DET 2019 dataset, shows a clear superiority over other methods in all metrics. Experiments on two real-world UAV datasets demonstrate that, compared to conventional 3-D reconstruction methods, the technical approach proposed in this study can generate more realistic 3-D meshes after automatically removing unwanted targets in photogrammetric model repair.