  Stereo matching has become a key technique for 3D environment perception in
intelligent vehicles. For a considerable time, convolutional neural networks
(CNNs) have remained the mainstream choice for feature extraction in this
domain. Nonetheless, there is a growing consensus that the existing paradigm
should evolve towards vision foundation models (VFM), particularly those
developed based on vision Transformers (ViTs) and pre-trained through
self-supervision on extensive, unlabeled datasets. While VFMs are adept at
extracting informative, general-purpose visual features, specifically for dense
prediction tasks, their performance often lacks in geometric vision tasks. This
study serves as the first exploration of a viable approach for adapting VFMs to
stereo matching. Our ViT adapter, referred to as ViTAS, is constructed upon
three types of modules: spatial differentiation, patch attention fusion, and
cross-attention. The first module initializes feature pyramids, while the
latter two aggregate stereo and multi-scale contextual information into
fine-grained features, respectively. ViTAStereo, which combines ViTAS with cost
volume-based stereo matching back-end processes, achieves the top rank on the
KITTI Stereo 2012 dataset and outperforms the second-best network StereoBase by
approximately 7.9% in terms of the percentage of error pixels, with a tolerance
of 3 pixels. Additional experiments across diverse scenarios further
demonstrate its superior generalizability compared to all other
state-of-the-art approaches. We believe this new paradigm will pave the way for
the next generation of stereo matching networks.
