  Several leading methods on public benchmarks for depth-from-stereo rely on
memory-demanding 4D cost volumes and computationally intensive 3D convolutions
for feature matching. We suggest a new way to process the 4D cost volume where
we merge two different concepts in one deeply integrated framework to achieve a
symbiotic relationship. A feature matching part is responsible for identifying
matching pixels pairs along the baseline while a concurrent image volume part
is inspired by depth-from-mono CNNs. However, instead of predicting depth
directly from image features, it provides additional context to resolve
ambiguities during pixel matching. More technically, the processing of the 4D
cost volume is separated into a 2D propagation and a 3D propagation part.
Starting from feature maps of the left image, the 2D propagation assists the 3D
propagation part of the cost volume at different layers by adding visual
features to the geometric context. By combining both parts, we can safely
reduce the scale of 3D convolution layers in the matching part without
sacrificing accuracy. Experiments demonstrate that our end-to-end trained CNN
is ranked 2nd on KITTI2012 and ETH3D benchmarks while being significantly
faster than the 1st-ranked method. Furthermore, we notice that the coupling of
image and matching-volume improves fine-scale details as demonstrated by our
qualitative analysis.
