Shi-NeSS: Detecting Good and Stable Keypoints with a Neural Stability Score

• URL: http://arxiv.org/abs/2307.01069v1
• Date: Mon, 3 Jul 2023 14:50:14 GMT
• Title: Shi-NeSS: Detecting Good and Stable Keypoints with a Neural Stability Score
• Authors: Konstantin Pakulev, Alexander Vakhitov, Gonzalo Ferrer
• Abstract summary: 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. We evaluate Shi-NeSS on HPatches, ScanNet, MegaDepth and IMC-PT, demonstrating state-of-the-art performance and good generalization on downstream tasks.
• Score: 73.91231776658375
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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