Multi-scale Alternated Attention Transformer for Generalized Stereo
  Matching
Wei Miao Hong Zhao Tongjia Chen Wei Huang Changyan Xiao
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.