  Volumetric deep learning approach towards stereo matching aggregates a cost
volume computed from input left and right images using 3D convolutions. Recent
works showed that utilization of extracted image features and a spatially
varying cost volume aggregation complements 3D convolutions. However, existing
methods with spatially varying operations are complex, cost considerable
computation time, and cause memory consumption to increase. In this work, we
construct Guided Cost volume Excitation (GCE) and show that simple channel
excitation of cost volume guided by image can improve performance considerably.
Moreover, we propose a novel method of using top-k selection prior to
soft-argmin disparity regression for computing the final disparity estimate.
Combining our novel contributions, we present an end-to-end network that we
call Correlate-and-Excite (CoEx). Extensive experiments of our model on the
SceneFlow, KITTI 2012, and KITTI 2015 datasets demonstrate the effectiveness
and efficiency of our model and show that our model outperforms other
speed-based algorithms while also being competitive to other state-of-the-art
algorithms. Codes will be made available at https://github.com/antabangun/coex.
