GLU-Net: Global-Local Universal Network for Dense Flow and Correspondences
release_kkpuzlrrqvaobelzzv3vmulrzi
by
Prune Truong, Martin Danelljan, Radu Timofte
2021
Abstract
Establishing dense correspondences between a pair of images is an important
and general problem, covering geometric matching, optical flow and semantic
correspondences. While these applications share fundamental challenges, such as
large displacements, pixel-accuracy, and appearance changes, they are currently
addressed with specialized network architectures, designed for only one
particular task. This severely limits the generalization capabilities of such
networks to new scenarios, where e.g. robustness to larger displacements or
higher accuracy is required.
In this work, we propose a universal network architecture that is directly
applicable to all the aforementioned dense correspondence problems. We achieve
both high accuracy and robustness to large displacements by investigating the
combined use of global and local correlation layers. We further propose an
adaptive resolution strategy, allowing our network to operate on virtually any
input image resolution. The proposed GLU-Net achieves state-of-the-art
performance for geometric and semantic matching as well as optical flow, when
using the same network and weights. Code and trained models are available at
https://github.com/PruneTruong/GLU-Net.
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