  Deep neural networks have shown excellent performance for stereo matching.
Many efforts focus on the feature extraction and similarity measurement of the
matching cost computation step while less attention is paid on cost aggregation
which is crucial for stereo matching. In this paper, we present a
learning-based cost aggregation method for stereo matching by a novel
sub-architecture in the end-to-end trainable pipeline. We reformulate the cost
aggregation as a learning process of the generation and selection of cost
aggregation proposals which indicate the possible cost aggregation results. The
cost aggregation sub-architecture is realized by a two-stream network: one for
the generation of cost aggregation proposals, the other for the selection of
the proposals. The criterion for the selection is determined by the low-level
structure information obtained from a light convolutional network. The
two-stream network offers a global view guidance for the cost aggregation to
rectify the mismatching value stemming from the limited view of the matching
cost computation. The comprehensive experiments on challenge datasets such as
KITTI and Scene Flow show that our method outperforms the state-of-the-art
methods.
