  Dense geometric matching determines the dense pixel-wise correspondence
between a source and support image corresponding to the same 3D structure.
Prior works employ an encoder of transformer blocks to correlate the two-frame
features. However, existing monocular pretraining tasks, e.g., image
classification, and masked image modeling (MIM), can not pretrain the
cross-frame module, yielding less optimal performance. To resolve this, we
reformulate the MIM from reconstructing a single masked image to reconstructing
a pair of masked images, enabling the pretraining of transformer module.
Additionally, we incorporate a decoder into pretraining for improved upsampling
results. Further, to be robust to the textureless area, we propose a novel
cross-frame global matching module (CFGM). Since the most textureless area is
planar surfaces, we propose a homography loss to further regularize its
learning. Combined together, we achieve the State-of-The-Art (SoTA) performance
on geometric matching. Codes and models are available at
https://github.com/ShngJZ/PMatch.
