  We present SKD, a novel keypoint detector that uses saliency to determine the
best candidates from a point cloud for tasks such as registration and
reconstruction. The approach can be applied to any differentiable deep learning
descriptor by using the gradients of that descriptor with respect to the 3D
position of the input points as a measure of their saliency. The saliency is
combined with the original descriptor and context information in a neural
network, which is trained to learn robust keypoint candidates. The key
intuition behind this approach is that keypoints are not extracted solely as a
result of the geometry surrounding a point, but also take into account the
descriptor's response. The approach was evaluated on two large LIDAR datasets -
the Oxford RobotCar dataset and the KITTI dataset, where we obtain up to 50%
improvement over the state-of-the-art in both matchability and repeatability.
When performing sparse matching with the keypoints computed by our method we
achieve a higher inlier ratio and faster convergence.
