  We introduce a novel CNN-based feature point detector - GLAMpoints - learned
in a semi-supervised manner. Our detector extracts repeatable, stable interest
points with a dense coverage, specifically designed to maximize the correct
matching in a specific domain, which is in contrast to conventional techniques
that optimize indirect metrics. In this paper, we apply our method on
challenging retinal slitlamp images, for which classical detectors yield
unsatisfactory results due to low image quality and insufficient amount of
low-level features. We show that GLAMpoints significantly outperforms classical
detectors as well as state-of-the-art CNN-based methods in matching and
registration quality for retinal images. Our method can also be extended to
other domains, such as natural images. Training code and model weights are
available at https://github.com/PruneTruong/GLAMpoints_pytorch.
