  Stereo matching is an essential basis for various applications, but most
stereo matching methods have poor generalization performance and require a
fixed disparity search range. Moreover, current stereo matching methods focus
on the scenes that only have positive disparities, but ignore the scenes that
contain both positive and negative disparities, such as 3D movies. In this
paper, we present a new stereo matching pipeline that first computes semi-dense
disparity maps based on binocular disparity, and then completes the rest
depending on monocular cues. The new stereo matching pipeline have the
following advantages: It 1) has better generalization performance than most of
the current stereo matching methods; 2) relaxes the limitation of a fixed
disparity search range; 3) can handle the scenes that involve both positive and
negative disparities, which has more potential applications, such as view
synthesis in 3D multimedia and VR/AR. Experimental results demonstrate the
effectiveness of our new stereo matching pipeline.
