On the new LEVIR-GM benchmark, LoRetta's matchability-aware affine localization plus guided dense registration achieves AUC 83.3%, improving on RoMa v2 by 1.6 points while cutting inference time by 47.8%.
SAR-optical feature matching: A large-scale patch dataset and a deep local descriptor,
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LoRetta: A Foundation Model and Extensive Dataset for Global-Scale Remote Sensing Dense Image Matching
On the new LEVIR-GM benchmark, LoRetta's matchability-aware affine localization plus guided dense registration achieves AUC 83.3%, improving on RoMa v2 by 1.6 points while cutting inference time by 47.8%.