We introduce a learning-based approach to detect repeatable keypoints under
drastic imaging changes of weather and lighting conditions to which
state-of-the-art keypoint detectors are surprisingly sensitive. We first
identify good keypoint candidates in multiple training images taken from the
same viewpoint. We then train a regressor to predict a score map whose maxima
are those points so that they can be found by simple non-maximum suppression.
As there are no standard datasets to test the influence of these kinds of
changes, we created our own, which we will make publicly available. We will
show that our method significantly outperforms the state-of-the-art methods in
such challenging conditions, while still achieving state-of-the-art performance
on the untrained standard Oxford dataset.

本文介绍了一种基于学习的方法，用于在天气和照明条件的巨大变化下检测可重复的关键点。作者首先从相同视角的多个训练图像中识别出良好的关键点候选对象，然后训练回归器来预测得分图，以便通过简单的非极大值抑制方法找到这些点。实验结果表明，在这种具有挑战性的情况下，我们的方法显著优于现有方法，在未受训练的标准牛津数据集上仍可以实现最先进的性能。