  In this paper, we present a novel approach that exploits the information
within the descriptor space to propose keypoint locations. Detect then
describe, or detect and describe jointly are two typical strategies for
extracting local descriptors. In contrast, we propose an approach that inverts
this process by first describing and then detecting the keypoint locations. %
Describe-to-Detect (D2D) leverages successful descriptor models without the
need for any additional training. Our method selects keypoints as salient
locations with high information content which is defined by the descriptors
rather than some independent operators. We perform experiments on multiple
benchmarks including image matching, camera localisation, and 3D
reconstruction. The results indicate that our method improves the matching
performance of various descriptors and that it generalises across methods and
tasks.
