Efficient LoFTR: Semi-Dense Local Feature Matching with Sparse-Like Speed
CVPR 2024(2024)
摘要
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 efficiency. Furthermore, we find
spatial variance exists in LoFTR's fine correlation module, which is adverse to
matching accuracy. A novel two-stage correlation layer is proposed to achieve
accurate subpixel correspondences for accuracy improvement. Our efficiency
optimized model is ∼ 2.5× faster than LoFTR which can even surpass
state-of-the-art efficient sparse matching pipeline SuperPoint + LightGlue.
Moreover, extensive experiments show that our method can achieve higher
accuracy compared with competitive semi-dense matchers, with considerable
efficiency benefits. This opens up exciting prospects for large-scale or
latency-sensitive applications such as image retrieval and 3D reconstruction.
Project page: https://zju3dv.github.io/efficientloftr.
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