Dual-resolution correspondence networks

Conference item

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 state-of-the-art results on all of them.

Authors: Li, X; Han, K; Li, S; Prisacariu, VA

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: Neural Information Processing Systems Foundation, Inc.
• Pages: 1-12
• Series number: Advances in Neural Information Processing Systems 33 (NeurIPS 2020)
• Publication date: 2020-12-10
• Acceptance date: 2020-09-25
• Event title: 34th Conference on Neural Information Processing Systems (NeurIPS)
• Event location: Virtual
• Event website: https://neurips.cc/
• Event start date: 2020-12-06
• Event end date: 2020-12-12
• Language: English
• Keywords: FFR