EfficientLoFTR The Efficient LoFTR model was proposed in "Efficient LoFTR: Semi Dense Local Feature Matching with Sparse Like Speed" by Yifan Wang, Xingyi He, Sida Peng, Dongli Tan, and Xiaowei Zhou from Zhejiang University. This model presents a novel method for efficiently producing semi dense matches across images, addressing the limitations of previous detector free matchers like LoFTR, which suffered from low efficiency despite remarkable matching capabilities in challenging scenarios. Efficient LoFTR revisits design choices to improve both efficiency and accuracy. The abstract from the paper is the following: "We present a novel method for efficiently producing semi dense matches across images. Previous detector free matcher LOFTR has shown remarkable matching capability in handling large viewpoint change and texture poor scenarios but suffers from low efficiency. We revisit its design choices and derive multiple improvements for both efficiency and accuracy. One key observation is that performing the transformer over the entire feature map is redundant due to shared local information, therefore we propose an aggregated attention mechanism with adaptive token selection for ef…
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