  In this paper, we present a decomposition model for stereo matching to solve
the problem of excessive growth in computational cost (time and memory cost) as
the resolution increases. In order to reduce the huge cost of stereo matching
at the original resolution, our model only runs dense matching at a very low
resolution and uses sparse matching at different higher resolutions to recover
the disparity of lost details scale-by-scale. After the decomposition of stereo
matching, our model iteratively fuses the sparse and dense disparity maps from
adjacent scales with an occlusion-aware mask. A refinement network is also
applied to improving the fusion result. Compared with high-performance methods
like PSMNet and GANet, our method achieves $10-100\times$ speed increase while
obtaining comparable disparity estimation results.
