Dual-Resolution Correspondence Networks
Xinghui Li1Kai Han2Shuda Li1Victor Prisacariu1
1Active Vision Lab & 2Visual Geometry Group
Department of Engineering Science, University of Oxford

Paper [PDF]    Supplementary [PDF]    Code [PyTorch]


We tackle the problem of establishing dense pixel-wise correspondences between a pair of images. In this work, we introduce Dual-Resolution Correspondence Networks (DualRC-Net), to obtain pixel-wise correspondences in a coarse-to-fine manner. DualRC-Net extracts both coarse- and fine- resolution feature maps. The coarse maps are used to produce a full but coarse 4D correlation tensor, 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, DualRC-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.


             author     = {Xinghui Li and Kai Han and Shuda Li and Victor Prisacariu},
             title      = {Dual-Resolution Correspondence Networks},
             booktitle  = {Conference on Neural Information Processing Systems (NeurIPS)},
             year       = {2020},



We gratefully acknowledge the support of the European Commission Project Multiple-actOrs Virtual EmpathicCARegiver for the Elder (MoveCare) and the EPSRC Programme Grant Seebibyte EP/M013774/1.

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