  Convolutional neural network (CNN)-based stereo matching approaches generally
require a dense cost volume (DCV) for disparity estimation. However, generating
such cost volumes is computationally-intensive and memory-consuming, hindering
CNN training and inference efficiency. To address this problem, we propose
SCV-Stereo, a novel CNN architecture, capable of learning dense stereo matching
from sparse cost volume (SCV) representations. Our inspiration is derived from
the fact that DCV representations are somewhat redundant and can be replaced
with SCV representations. Benefiting from these SCV representations, our
SCV-Stereo can update disparity estimations in an iterative fashion for
accurate and efficient stereo matching. Extensive experiments carried out on
the KITTI Stereo benchmarks demonstrate that our SCV-Stereo can significantly
minimize the trade-off between accuracy and efficiency for stereo matching. Our
project page is https://sites.google.com/view/scv-stereo.
