  Online stereo adaptation tackles the domain shift problem, caused by
different environments between synthetic (training) and real (test) datasets,
to promptly adapt stereo models in dynamic real-world applications such as
autonomous driving. However, previous methods often fail to counteract
particular regions related to dynamic objects with more severe environmental
changes. To mitigate this issue, we propose to incorporate an auxiliary
point-selective network into a meta-learning framework, called PointFix, to
provide a robust initialization of stereo models for online stereo adaptation.
In a nutshell, our auxiliary network learns to fix local variants intensively
by effectively back-propagating local information through the meta-gradient for
the robust initialization of the baseline model. This network is
model-agnostic, so can be used in any kind of architectures in a plug-and-play
manner. We conduct extensive experiments to verify the effectiveness of our
method under three adaptation settings such as short-, mid-, and long-term
sequences. Experimental results show that the proper initialization of the base
stereo model by the auxiliary network enables our learning paradigm to achieve
state-of-the-art performance at inference.
