  Stereo matching provides depth estimation from binocular images for
downstream applications. These applications mostly take video streams as input
and require temporally consistent depth maps. However, existing methods mainly
focus on the estimation at the single-frame level. This commonly leads to
temporally inconsistent results, especially in ill-posed regions. In this
paper, we aim to leverage temporal information to improve the temporal
consistency, accuracy, and efficiency of stereo matching. To achieve this, we
formulate video stereo matching as a process of temporal disparity completion
followed by continuous iterative refinements. Specifically, we first project
the disparity of the previous timestamp to the current viewpoint, obtaining a
semi-dense disparity map. Then, we complete this map through a disparity
completion module to obtain a well-initialized disparity map. The state
features from the current completion module and from the past refinement are
fused together, providing a temporally coherent state for subsequent
refinement. Based on this coherent state, we introduce a dual-space refinement
module to iteratively refine the initialized result in both disparity and
disparity gradient spaces, improving estimations in ill-posed regions.
Extensive experiments demonstrate that our method effectively alleviates
temporal inconsistency while enhancing both accuracy and efficiency.
