Are Semi-Dense Detector-Free Methods Good at Matching Local Features?
Matthieu Vilain Rémi Giraud Hugo Germain Guillaume Bourmaud
Semi-dense detector-free approaches (SDF), such as LoFTR, are currently among
the most popular image matching methods. While SDF methods are trained to
establish correspondences between two images, their performances are almost
exclusively evaluated using relative pose estimation metrics. Thus, the link
between their ability to establish correspondences and the quality of the
resulting estimated pose has thus far received little attention. This paper is
a first attempt to study this link. We start with proposing a novel structured
attention-based image matching architecture (SAM). It allows us to show a
counter-intuitive result on two datasets (MegaDepth and HPatches): on the one
hand SAM either outperforms or is on par with SDF methods in terms of
pose/homography estimation metrics, but on the other hand SDF approaches are
significantly better than SAM in terms of matching accuracy. We then propose to
limit the computation of the matching accuracy to textured regions, and show
that in this case SAM often surpasses SDF methods. Our findings highlight a
strong correlation between the ability to establish accurate correspondences in
textured regions and the accuracy of the resulting estimated pose/homography.
Our code will be made available.