Abstract
In this paper, we design an efficient large-scale oblique image matching method. First, to reduce the number of redundant transmissions of match data, we propose a novel three-level buffer data scheduling (TLBDS) algorithm that considers the adjacency between images for match data scheduling from disk to graphics memory. Second, we adopt the epipolar constraint to filter the initial candidate points of cascade hashing matching, thereby significantly increasing the robustness of matching feature points. Comprehensive experiments are conducted on three oblique image datasets to test the efficiency and effectiveness of the proposed method. The experimental results show that our method can complete a match pair within 2.50∼2.64 ms, which not only is much faster than two open benchmark pipelines (i.e., OpenMVG and COLMAP) by 20.4∼97.0 times but also have higher efficiency than two state-of-the-art commercial software (i.e., Agisoft Metashape and Pix4Dmapper) by 10.4∼50.0 times.
Original language | English |
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Article number | 109442 |
Journal | Pattern Recognition |
Volume | 138 |
DOIs | |
Publication status | Published - 30 Jun 2023 |
Bibliographical note
Funding Information:The authors would like to thank Jian Cheng who has made their algorithms of Cascade hashing free and open-source, which is helpful to the research in this paper. Meanwhile, heartfelt thanks to the anonymous reviewers and the editors, whose comments and advice improved the quality of the work. This work was supported by the National Natural Science Foundation of China ( 41671452 ).
Publisher Copyright:
© 2023 Elsevier Ltd
Keywords
- Cascade hashing
- Feature point matching
- Match data scheduling
- Oblique image matching
- SIFT
- Structure from motion