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

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.