Work

A geometric consistency constrained hierarchical global SfM for large-scale UAV images

Year: 2026

Type: article

Abstract: • An effective hierarchical global SfM for large-scale 3D reconstruction. • A cluster consistency-constrained outlier filtering to remove ambiguous matches. • A feature-track-enhanced global SfM for parallel sub-model reconstruction. • A geometry-constrained alignment optimization strategy for accurate merging. 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 .

Source: International Journal of Applied Earth Observation and Geoinformation

DOI: 10.1016/j.jag.2025.105075

Authors: Yan Zhou, Xianwei Zheng, Jinding Gao, Qian Shi, Xiaoping Liu

Institutions: Sun Yat-sen University, Wuhan University, State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing

Language: English

Topic: 3D Surveying and Cultural Heritage

Subfield: Geology

Field: Earth and Planetary Sciences

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $3,550

Funder: National Natural Science Foundation of China