# Dual-Resolution Correspondence Networks

06/16/2020

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by [Xinghui Li, et al.](/content/profile/xinghui-li/index.html)

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15

We tackle the problem of establishing dense pixel-wise correspondences between a pair of images. In this work, we introduce Dual-Resolution Correspondence Networks (DRC-Net), to obtain pixel-wise correspondences in a coarse-to-fine manner. DRC-Net extracts both coarse- and fine- resolution feature maps. The coarse maps are used to produce a full but coarse 4D correlation [tensor](/content/machine-learning-glossary-and-terms/tensor/index.html), which is then refined by a learnable neighbourhood consensus module. The fine-resolution feature maps are used to obtain the final dense correspondences guided by the refined coarse 4D correlation tensor. The selected coarse-resolution matching scores allow the fine-resolution features to focus only on a limited number of possible matches with high confidence. In this way, DRC-Net dramatically increases matching reliability and localisation accuracy, while avoiding to apply the expensive 4D convolution kernels on fine-resolution feature maps. We comprehensively evaluate our method on large-scale public benchmarks including HPatches, InLoc, and Aachen Day-Night. It achieves the state-of-the-art results on all of them.

[READ FULL TEXT](http://arxiv.org/pdf/2006.08844v1)