  A wide range of computer vision algorithms rely on identifying sparse
interest points in images and establishing correspondences between them.
However, only a subset of the initially identified interest points results in
true correspondences (inliers). In this paper, we seek a detector that finds
the minimum number of points that are likely to result in an
application-dependent "sufficient" number of inliers k. To quantify this goal,
we introduce the "k-succinctness" metric. Extracting a minimum number of
interest points is attractive for many applications, because it can reduce
computational load, memory, and data transmission. Alongside succinctness, we
introduce an unsupervised training methodology for interest point detectors
that is based on predicting the probability of a given pixel being an inlier.
In comparison to previous learned detectors, our method requires the least
amount of data pre-processing. Our detector and other state-of-the-art
detectors are extensively evaluated with respect to succinctness on popular
public datasets covering both indoor and outdoor scenes, and both wide and
narrow baselines. In certain cases, our detector is able to obtain an
equivalent amount of inliers with as little as 60% of the amount of points of
other detectors. The code and trained networks are provided at
https://github.com/uzh-rpg/sips2_open .
