  In this paper, we aim at establishing accurate dense correspondences between
a pair of images with overlapping field of view under challenging illumination
variation, viewpoint changes, and style differences. Through an extensive
ablation study of the state-of-the-art correspondence networks, we surprisingly
discovered that the widely adopted 4D correlation tensor and its related
learning and processing modules could be de-parameterised and removed from
training with merely a minor impact over the final matching accuracy. Disabling
these computational expensive modules dramatically speeds up the training
procedure and allows to use 4 times bigger batch size, which in turn
compensates for the accuracy drop. Together with a multi-GPU inference stage,
our method facilitates the systematic investigation of the relationship between
matching accuracy and up-sampling resolution of the native testing images from
1280 to 4K. This leads to discovery of the existence of an optimal resolution
$\mathbb{X}$ that produces accurate matching performance surpassing the
state-of-the-art methods particularly over the lower error band on public
benchmarks for the proposed network.
