Efficient and effective pose estimation and 3D point clouds reconstruction from low-altitude UAV images is crucial for digital twins and geospatial applications. However, conventional divide-and-conquer SfM pipelines remain constrained by the high computational cost of incremental reconstruction. While global SfM, though faster, often suffer from instability due to outlier sensitivity in translation averaging. To address these limitations, we propose a feature track enhanced global SfM within divide-and-conquer framework. To deal with outliers, a cluster consistency constrained outlier filtering method is proposed, which combines distribution-aware scene partition and cluster consistency to remove false matches. Furthermore, we propose a geometry-constrained alignment optimization strategy in sub-models merging process to eliminate misalignments and ghosting artifacts, obtaining complete and accurate 3D models. Extensive experiments on ETH3D and SYSU UAV large-scale datasets verify that the proposed method outperforms established baselines, with comparable accuracy to the incremental SfM but half of the computation time. The reconstructed 3D models demonstrate notable enhancements in both structural integrity and level of detail, proving the method’s high applicability in large-scale 3D reconstruction tasks. The code and data will be released: https://github.com/BunnyanChou/HieGSfM.
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