  Stereo depth estimation is of great interest for computer vision research.
However, existing methods struggles to generalize and predict reliably in
hazardous regions, such as large uniform regions. To overcome these
limitations, we propose Context Enhanced Path (CEP). CEP improves the
generalization and robustness against common failure cases in existing
solutions by capturing the long-range global information. We construct our
stereo depth estimation model, Context Enhanced Stereo Transformer (CSTR), by
plugging CEP into the state-of-the-art stereo depth estimation method Stereo
Transformer. CSTR is examined on distinct public datasets, such as Scene Flow,
Middlebury-2014, KITTI-2015, and MPI-Sintel. We find CSTR outperforms prior
approaches by a large margin. For example, in the zero-shot synthetic-to-real
setting, CSTR outperforms the best competing approaches on Middlebury-2014
dataset by 11%. Our extensive experiments demonstrate that the long-range
information is critical for stereo matching task and CEP successfully captures
such information.
