  Supervised deep networks are among the best methods for finding
correspondences in stereo image pairs. Like all supervised approaches, these
networks require ground truth data during training. However, collecting large
quantities of accurate dense correspondence data is very challenging. We
propose that it is unnecessary to have such a high reliance on ground truth
depths or even corresponding stereo pairs. Inspired by recent progress in
monocular depth estimation, we generate plausible disparity maps from single
images. In turn, we use those flawed disparity maps in a carefully designed
pipeline to generate stereo training pairs. Training in this manner makes it
possible to convert any collection of single RGB images into stereo training
data. This results in a significant reduction in human effort, with no need to
collect real depths or to hand-design synthetic data. We can consequently train
a stereo matching network from scratch on datasets like COCO, which were
previously hard to exploit for stereo. Through extensive experiments we show
that our approach outperforms stereo networks trained with standard synthetic
datasets, when evaluated on KITTI, ETH3D, and Middlebury.
