  Image matching is still challenging in such scenes with large viewpoints or
illumination changes or with low textures. In this paper, we propose a
Transformer-based pseudo 3D image matching method. It upgrades the 2D features
extracted from the source image to 3D features with the help of a reference
image and matches to the 2D features extracted from the destination image by
the coarse-to-fine 3D matching. Our key discovery is that by introducing the
reference image, the source image's fine points are screened and furtherly
their feature descriptors are enriched from 2D to 3D, which improves the match
performance with the destination image. Experimental results on multiple
datasets show that the proposed method achieves the state-of-the-art on the
tasks of homography estimation, pose estimation and visual localization
especially in challenging scenes.
