The issue of distortion or misalignment in 3D modeling resulting from moving objects such as people and vehicles during UAV-obtained oblique image acquisition was addressed. An enhanced GAN-based image restoration technique was proposed, which modifies the GAN network by incorporating a U-Net architecture in the generator, fortified with a dual-channel attention mechanism via CBAM in the connecting layers, thereby enhancing the technique's capability for restoring local image details. The discriminator was augmented with the VGG16 model and the SE-Net channel attention mechanism has been undertaken to ensure the high fidelity of generated images in the present approach. Image analysis and processing were carried out using Context Capture software, facilitating automatic 3D modeling. This methodology enables proactive removal of moving entities, such as people and vehicles, from high-resolution, extensive oblique imagery, thus minimizing their detrimental effects on subsequent 3D modeling and enhancing model accuracy. The presented algorithm demonstrates superior performance compared to conventional GAN and WGAN-GP networks, exhibiting increases of 3.329 96 dB and 0.097 9 in PSNR values and 2.288 94 dB and 0.047 8 in SSIM indices, respectively. Moreover, through comparison with generated 3D models, the method effectively reduces geometric deformation and road texture mapping errors, leading to heightened model precision.