  Despite impressive performance for high-level downstream tasks,
self-supervised pre-training methods have not yet fully delivered on dense
geometric vision tasks such as stereo matching or optical flow. The application
of self-supervised concepts, such as instance discrimination or masked image
modeling, to geometric tasks is an active area of research. In this work, we
build on the recent cross-view completion framework, a variation of masked
image modeling that leverages a second view from the same scene which makes it
well suited for binocular downstream tasks. The applicability of this concept
has so far been limited in at least two ways: (a) by the difficulty of
collecting real-world image pairs -- in practice only synthetic data have been
used -- and (b) by the lack of generalization of vanilla transformers to dense
downstream tasks for which relative position is more meaningful than absolute
position. We explore three avenues of improvement. First, we introduce a method
to collect suitable real-world image pairs at large scale. Second, we
experiment with relative positional embeddings and show that they enable vision
transformers to perform substantially better. Third, we scale up vision
transformer based cross-completion architectures, which is made possible by the
use of large amounts of data. With these improvements, we show for the first
time that state-of-the-art results on stereo matching and optical flow can be
reached without using any classical task-specific techniques like correlation
volume, iterative estimation, image warping or multi-scale reasoning, thus
paving the way towards universal vision models.
