Efficient large-scale oblique image matching based on cascade hashing and match data scheduling

Qiyuan Zhang, Shunyi Zheng*, Ce Zhang, Xiqi Wang, Rui Li

*Corresponding author for this work

Research output: Contribution to journalArticle (Academic Journal)peer-review

4 Citations (Scopus)

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 languageEnglish
Article number109442
JournalPattern Recognition
Volume138
DOIs
Publication statusPublished - 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

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