  The self-attention mechanism, successfully employed with the transformer
structure is shown promise in many computer vision tasks including image
recognition, and object detection. Despite the surge, the use of the
transformer for the problem of stereo matching remains relatively unexplored.
In this paper, we comprehensively investigate the use of the transformer for
the problem of stereo matching, especially for laparoscopic videos, and propose
a new hybrid deep stereo matching framework (HybridStereoNet) that combines the
best of the CNN and the transformer in a unified design. To be specific, we
investigate several ways to introduce transformers to volumetric stereo
matching pipelines by analyzing the loss landscape of the designs and
in-domain/cross-domain accuracy. Our analysis suggests that employing
transformers for feature representation learning, while using CNNs for cost
aggregation will lead to faster convergence, higher accuracy and better
generalization than other options. Our extensive experiments on Sceneflow,
SCARED2019 and dVPN datasets demonstrate the superior performance of our
HybridStereoNet.
