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Learned local features for structure from motion of UAV images: a comparative evaluation
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Unmanned aerial vehicle (UAV) images have become the main remote sensing data sources for varying applications, and Structure from Motion (SfM) is the golden standard for resuming camera poses. Matching local feature descriptors is the prerequisite for the accurate and complete orientation of UAV images. Recently, some newly proposed learned methods have been shown to outperform the hand-crafted methods, such as the SIFT (Scale Invariant Feature Transform) and its variants, and almost alldoi:10.1109/jstars.2021.3119990 fatcat:2mtvs7onwzh4jdt7aqeb4jeuqm