  A Multistage Full Matching disparity estimation scheme (MFM) is proposed in
this work. We demonstrate that decouple all similarity scores directly from the
low-resolution 4D volume step by step instead of estimating low-resolution 3D
cost volume through focusing on optimizing the low-resolution 4D volume
iteratively leads to more accurate disparity. To this end, we first propose to
decompose the full matching task into multiple stages of the cost aggregation
module. Specifically, we decompose the high-resolution predicted results into
multiple groups, and every stage of the newly designed cost aggregation module
learns only to estimate the results for a group of points. This alleviates the
problem of feature internal competitive when learning similarity scores of all
candidates from one low-resolution 4D volume output from one stage. Then, we
propose the strategy of \emph{Stages Mutual Aid}, which takes advantage of the
relationship of multiple stages to boost similarity scores estimation of each
stage, to solve the unbalanced prediction of multiple stages caused by serial
multistage framework. Experiment results demonstrate that the proposed method
achieves more accurate disparity estimation results and outperforms
state-of-the-art methods on Scene Flow, KITTI 2012 and KITTI 2015 datasets.
