  Supervised learning with deep convolutional neural networks (DCNNs) has seen
huge adoption in stereo matching. However, the acquisition of large-scale
datasets with well-labeled ground truth is cumbersome and labor-intensive,
making supervised learning-based approaches often hard to implement in
practice. To overcome this drawback, we propose a robust and effective
self-supervised stereo matching approach, consisting of a pyramid voting module
(PVM) and a novel DCNN architecture, referred to as OptStereo. Specifically,
our OptStereo first builds multi-scale cost volumes, and then adopts a
recurrent unit to iteratively update disparity estimations at high resolution;
while our PVM can generate reliable semi-dense disparity images, which can be
employed to supervise OptStereo training. Furthermore, we publish the
HKUST-Drive dataset, a large-scale synthetic stereo dataset, collected under
different illumination and weather conditions for research purposes. Extensive
experimental results demonstrate the effectiveness and efficiency of our
self-supervised stereo matching approach on the KITTI Stereo benchmarks and our
HKUST-Drive dataset. PVStereo, our best-performing implementation, greatly
outperforms all other state-of-the-art self-supervised stereo matching
approaches. Our project page is available at sites.google.com/view/pvstereo.
