  Stereo image pairs encode 3D scene cues into stereo correspondences between
the left and right images. To exploit 3D cues within stereo images, recent CNN
based methods commonly use cost volume techniques to capture stereo
correspondence over large disparities. However, since disparities can vary
significantly for stereo cameras with different baselines, focal lengths and
resolutions, the fixed maximum disparity used in cost volume techniques hinders
them to handle different stereo image pairs with large disparity variations. In
this paper, we propose a generic parallax-attention mechanism (PAM) to capture
stereo correspondence regardless of disparity variations. Our PAM integrates
epipolar constraints with attention mechanism to calculate feature similarities
along the epipolar line to capture stereo correspondence. Based on our PAM, we
propose a parallax-attention stereo matching network (PASMnet) and a
parallax-attention stereo image super-resolution network (PASSRnet) for stereo
matching and stereo image super-resolution tasks. Moreover, we introduce a new
and large-scale dataset named Flickr1024 for stereo image super-resolution.
Experimental results show that our PAM is generic and can effectively learn
stereo correspondence under large disparity variations in an unsupervised
manner. Comparative results show that our PASMnet and PASSRnet achieve the
state-of-the-art performance.
