In order to address the problems of geometric deformation and texture distortion caused by moving targets on the road 3D Real Scene model based on UAV oblique photography,a restoration method of the road 3D Real Scene model is proposed using deep learning.Firstly,the YOLOv8 net-work,involving the attention mechanism,was employed to detect objects in the image.Secondly,based on the detected object's range,the presence of moving targets was determined by analyzing the catego-ry information of each triangular face in the mesh,according to its projection position in the visual im-age set.Finally,the geometric deformation and texture distortion of the 3D model were restored from the results of moving targets determination to achieve the road 3D Real Scene model reconstruction.The re-sults indicate that the improved network enhances the mAP by an average of 10.82％over YOLOv4,YOLOv5 and YOLOv8.Furthermore,the accuracy of moving targets determination is 97.43％.Addi-tionally,in contrast to commercial software,the proposed method demonstrates a superior restoration effect and a higher level of automation.
关键词
实景三维重建/深度学习/Mesh模型/遮挡剔除/纹理修复
Key words
reality 3D reconstruction of real scene/deep learning/Mesh model/occlusion culling/tex-ture restoration
维普
万方数据