A parallelizable incremental structure from motion (SFM) recovery reconstruction algorithm is employed to address low efficiency and susceptibility to scene drift when reconstructing large-scale unmanned aerial vehicle image datasets. First, the vocabulary tree image retrieval results are used to constrain the spatial search range and improve the efficiency of image feature matching. Second, by considering the feature matching number and the global positioning system (GPS) information obtained by the drone platform, an undirected weighted scene map is constructed, and a normalized cut algorithm is selected to divide the scene map into multiple overlapping subsets. Further, each subset is distributed on multicore central processing units (CPUs), and the incremental SFM reconstruction algorithm is executed in parallel. Finally, based on the strategy of common reconstruction points between subsets and priority merging of strongly correlated subsets, subset merging is achieved. In addition, combining GPS information to add positional constraints to the beam adjustment (BA) cost function eliminates the errors introduced by each BA optimization execution. To verify the effectiveness of the algorithm, experiments are conducted on three unmanned aerial vehicle datasets. The experimental results show that the proposed algorithm not only significantly improves the efficiency of pose estimation and scene reconstruction compared with the original incremental SFM reconstruction algorithm but also reasonably optimizes the accuracy of the reconstruction results.