  Despite the recent success of stereo matching with convolutional neural
networks (CNNs), it remains arduous to generalize a pre-trained deep stereo
model to a novel domain. A major difficulty is to collect accurate ground-truth
disparities for stereo pairs in the target domain. In this work, we propose a
self-adaptation approach for CNN training, utilizing both synthetic training
data (with ground-truth disparities) and stereo pairs in the new domain
(without ground-truths). Our method is driven by two empirical observations. By
feeding real stereo pairs of different domains to stereo models pre-trained
with synthetic data, we see that: i) a pre-trained model does not generalize
well to the new domain, producing artifacts at boundaries and ill-posed
regions; however, ii) feeding an up-sampled stereo pair leads to a disparity
map with extra details. To avoid i) while exploiting ii), we formulate an
iterative optimization problem with graph Laplacian regularization. At each
iteration, the CNN adapts itself better to the new domain: we let the CNN learn
its own higher-resolution output; at the meanwhile, a graph Laplacian
regularization is imposed to discriminatively keep the desired edges while
smoothing out the artifacts. We demonstrate the effectiveness of our method in
two domains: daily scenes collected by smartphone cameras, and street views
captured in a driving car.
