We address the challenges of local feature matching under large scale and rotation changes by focusing on keypoint positions.
First, we propose a novel module called similarity normalization (SN).
This module normalizes keypoint positions to remove translation, rotation and scale differences between image pairs.
By performing positional encoding on these normalized positions, a network incorporating with SN can effectively avoid encoding largely different positions into descriptors from the two images.
Second, we apply strong data augmentation (DA) that includes large scale and rotation, whereas existing matchers ignore such DA and are overfitted to upright image pairs.
In our experiments, SN and DA improve the performance for image pairs with large scale and rotation differences.
Additionally, the combination of SN with DA leads to further performance improvements.