Existing unmanned aerial vehicle (UAV) image three-dimensional (3D) reconstruction algorithms suffer from a lack of attention to spatial geometric information and global feature perception. Therefore, the reconstructed point cloud models have holes and their accuracy is low in case of weak textures and complex areas. Herein, a 3D reconstruction algorithm for unmanned aerial vehicle images is proposed. This algorithm combines spatial geometric information and global features to address the abovementioned issues. First, a feature descriptor self-mapping layer is designed. This layer uses multilayer perceptrons to map geometric spatial information to high-dimensional feature vectors, thereby improving feature point matching performance while enhancing 3D reconstruction accuracy. In addition, a lightweight Transformer structure model is proposed based on the characteristics of drone images. This model achieves cross perception of feature descriptors, obtains global contextual feature information, improves the global perception ability and distinguishability of features while maintaining 3D reconstruction efficiency, enhances 3D reconstruction accuracy, and reduces point cloud reprojection errors. Finally, combining the FastAP loss function with the descriptor-enhanced loss function accelerates model convergence and improves the quality of the reconstructed point clouds. To verify the effectiveness of the proposed algorithm, experiments are conducted on three drone highway datasets. Experimental results show that the proposed algorithm improves the reliability and accuracy of the reconstructed point cloud while maintaining 3D reconstruction efficiency.