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
this https URL

我们提出了一种通用的网络架构 GLU-Net，它可以直接应用于所有密集对应的问题，并通过结合全局和局部相关层来同时实现高精度和对大位移的鲁棒性。当使用相同的网络和权重时，此 GLU-Net 在几何和语义匹配以及光流方面都达到了最先进的性能。