  In this paper, we propose CGI-Stereo, a novel neural network architecture
that can concurrently achieve real-time performance, competitive accuracy, and
strong generalization ability. The core of our CGI-Stereo is a Context and
Geometry Fusion (CGF) block which adaptively fuses context and geometry
information for more effective cost aggregation and meanwhile provides feedback
to feature learning to guide more effective contextual feature extraction. The
proposed CGF can be easily embedded into many existing stereo matching
networks, such as PSMNet, GwcNet and ACVNet. The resulting networks show a
significant improvement in accuracy. Specially, the model which incorporates
our CGF with ACVNet ranks $1^{st}$ on the KITTI 2012 and 2015 leaderboards
among all the published methods. We further propose an informative and concise
cost volume, named Attention Feature Volume (AFV), which exploits a correlation
volume as attention weights to filter a feature volume. Based on CGF and AFV,
the proposed CGI-Stereo outperforms all other published real-time methods on
KITTI benchmarks and shows better generalization ability than other real-time
methods. Code is available at https://github.com/gangweiX/CGI-Stereo.
