  Local features that are robust to both viewpoint and appearance changes are
crucial for many computer vision tasks. In this work we investigate if
photorealistic image stylization improves robustness of local features to not
only day-night, but also weather and season variations. We show that image
stylization in addition to color augmentation is a powerful method of learning
robust features. We evaluate learned features on visual localization
benchmarks, outperforming state of the art baseline models despite training
without ground-truth 3D correspondences using synthetic homographies only.
  We use trained feature networks to compete in Long-Term Visual Localization
and Map-based Localization for Autonomous Driving challenges achieving
competitive scores.
