  Stereo matching is crucial for binocular stereo vision. Existing methods
mainly focus on simple disparity map fusion to improve stereo matching, which
require multiple dense or sparse disparity maps. In this paper, we propose a
simple yet novel scheme, termed feature disparity propagation, to improve
general stereo matching based on matching cost volume and sparse matching
feature points. Specifically, our scheme first calculates a reliable sparse
disparity map by local feature matching, and then refines the disparity map by
propagating reliable disparities to neighboring pixels in the matching cost
domain. In addition, considering the gradient and multi-scale information of
local disparity regions, we present a $\rho$-Census cost measure based on the
well-known AD-Census, which guarantees the robustness of cost volume even
without the cost aggregation step. Extensive experiments on Middlebury stereo
benchmark V3 demonstrate that our scheme achieves promising performance
comparable to state-of-the-art methods.
