Shi-NeSS: Detecting Good and Stable Keypoints with a Neural Stability
  Score
Konstantin Pakulev Alexander Vakhitov Gonzalo Ferrer
Learning a feature point detector presents a challenge both due to the
ambiguity of the definition of a keypoint and correspondingly the need for a
specially prepared ground truth labels for such points. In our work, we address
both of these issues by utilizing a combination of a hand-crafted Shi detector
and a neural network. We build on the principled and localized keypoints
provided by the Shi detector and perform their selection using the keypoint
stability score regressed by the neural network - Neural Stability Score
(NeSS). Therefore, our method is named Shi-NeSS since it combines the Shi
detector and the properties of the keypoint stability score, and it only
requires for training sets of images without dataset pre-labeling or the need
for reconstructed correspondence labels. We evaluate Shi-NeSS on HPatches,
ScanNet, MegaDepth and IMC-PT, demonstrating state-of-the-art performance and
good generalization on downstream tasks.