  Despite recent stereo matching networks achieving impressive performance
given sufficient training data, they suffer from domain shifts and generalize
poorly to unseen domains. We argue that maintaining feature consistency between
matching pixels is a vital factor for promoting the generalization capability
of stereo matching networks, which has not been adequately considered. Here we
address this issue by proposing a simple pixel-wise contrastive learning across
the viewpoints. The stereo contrastive feature loss function explicitly
constrains the consistency between learned features of matching pixel pairs
which are observations of the same 3D points. A stereo selective whitening loss
is further introduced to better preserve the stereo feature consistency across
domains, which decorrelates stereo features from stereo viewpoint-specific
style information. Counter-intuitively, the generalization of feature
consistency between two viewpoints in the same scene translates to the
generalization of stereo matching performance to unseen domains. Our method is
generic in nature as it can be easily embedded into existing stereo networks
and does not require access to the samples in the target domain. When trained
on synthetic data and generalized to four real-world testing sets, our method
achieves superior performance over several state-of-the-art networks.
