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.

本文提出了一种新的混合深度立体匹配框架（HybridStereoNet），使用 Transformer 实现特征表示学习，并结合 CNN 用于代价汇聚，旨在提高立体视觉匹配精度和泛化能力。实验结果表明，与其他选项相比，本文方法收敛更快，准确率更高，能够在 Sceneflow，SCARED2019 和 dVPN 数据集上实现卓越的性能。