  Stereo matching is a fundamental building block for many vision and robotics
applications. An informative and concise cost volume representation is vital
for stereo matching of high accuracy and efficiency. In this paper, we present
a novel cost volume construction method which generates attention weights from
correlation clues to suppress redundant information and enhance
matching-related information in the concatenation volume. To generate reliable
attention weights, we propose multi-level adaptive patch matching to improve
the distinctiveness of the matching cost at different disparities even for
textureless regions. The proposed cost volume is named attention concatenation
volume (ACV) which can be seamlessly embedded into most stereo matching
networks, the resulting networks can use a more lightweight aggregation network
and meanwhile achieve higher accuracy, e.g. using only 1/25 parameters of the
aggregation network can achieve higher accuracy for GwcNet. Furthermore, we
design a highly accurate network (ACVNet) based on our ACV, which achieves
state-of-the-art performance on several benchmarks.
