  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, named attention concatenation volume
(ACV), which generates attention weights from correlation clues to suppress
redundant information and enhance matching-related information in the
concatenation volume. The ACV can be seamlessly embedded into most stereo
matching networks, the resulting networks can use a more lightweight
aggregation network and meanwhile achieve higher accuracy. We further design a
fast version of ACV to enable real-time performance, named Fast-ACV, which
generates high likelihood disparity hypotheses and the corresponding attention
weights from low-resolution correlation clues to significantly reduce
computational and memory cost and meanwhile maintain a satisfactory accuracy.
The core idea of our Fast-ACV is volume attention propagation (VAP) which can
automatically select accurate correlation values from an upsampled correlation
volume and propagate these accurate values to the surroundings pixels with
ambiguous correlation clues. Furthermore, we design a highly accurate network
ACVNet and a real-time network Fast-ACVNet based on our ACV and Fast-ACV
respectively, which achieve the state-of-the-art performance on several
benchmarks (i.e., our ACVNet ranks the 2nd on KITTI 2015 and Scene Flow, and
the 3rd on KITTI 2012 and ETH3D among all the published methods; our
Fast-ACVNet outperforms almost all state-of-the-art real-time methods on Scene
Flow, KITTI 2012 and 2015 and meanwhile has better generalization ability)
