  In this paper, we study the problem of stereo matching from a pair of images
with different resolutions, e.g., those acquired with a tele-wide camera
system. Due to the difficulty of obtaining ground-truth disparity labels in
diverse real-world systems, we start from an unsupervised learning perspective.
However, resolution asymmetry caused by unknown degradations between two views
hinders the effectiveness of the generally assumed photometric consistency. To
overcome this challenge, we propose to impose the consistency between two views
in a feature space instead of the image space, named feature-metric
consistency. Interestingly, we find that, although a stereo matching network
trained with the photometric loss is not optimal, its feature extractor can
produce degradation-agnostic and matching-specific features. These features can
then be utilized to formulate a feature-metric loss to avoid the photometric
inconsistency. Moreover, we introduce a self-boosting strategy to optimize the
feature extractor progressively, which further strengthens the feature-metric
consistency. Experiments on both simulated datasets with various degradations
and a self-collected real-world dataset validate the superior performance of
the proposed method over existing solutions.
