  Recent stereo matching networks achieves dramatic performance by introducing
epipolar line constraint to limit the matching range of dual-view. However, in
complicated real-world scenarios, the feature information based on
intra-epipolar line alone is too weak to facilitate stereo matching. In this
paper, we present a simple but highly effective network called Alternated
Attention U-shaped Transformer (AAUformer) to balance the impact of epipolar
line in dual and single view respectively for excellent generalization
performance. Compared to other models, our model has several main designs: 1)
to better liberate the local semantic features of the single-view at pixel
level, we introduce window self-attention to break the limits of intra-row
self-attention and completely replace the convolutional network for denser
features before cross-matching; 2) the multi-scale alternated attention
backbone network was designed to extract invariant features in order to
achieves the coarse-to-fine matching process for hard-to-discriminate regions.
We performed a series of both comparative studies and ablation studies on
several mainstream stereo matching datasets. The results demonstrate that our
model achieves state-of-the-art on the Scene Flow dataset, and the fine-tuning
performance is competitive on the KITTI 2015 dataset. In addition, for cross
generalization experiments on synthetic and real-world datasets, our model
outperforms several state-of-the-art works.
