  Depth estimation based on stereo matching is a classic but popular computer
vision problem, which has a wide range of real-world applications. Current
stereo matching methods generally adopt the deep Siamese neural network
architecture, and have achieved impressing performance by constructing feature
matching cost volumes and using 3D convolutions for cost aggregation. However,
most existing methods suffer from large number of parameters and slow running
time due to the sequential use of 3D convolutions. In this paper, we propose
Ghost-Stereo, a novel end-to-end stereo matching network. The feature
extraction part of the network uses the GhostNet to form a U-shaped structure.
The core of Ghost-Stereo is a GhostNet feature-based cost volume enhancement
(Ghost-CVE) module and a GhostNet-inspired lightweight cost volume aggregation
(Ghost-CVA) module. For the Ghost-CVE part, cost volumes are constructed and
fused by the GhostNet-based features to enhance the spatial context awareness.
For the Ghost-CVA part, a lightweight 3D convolution bottleneck block based on
the GhostNet is proposed to reduce the computational complexity in this module.
By combining with the context and geometry fusion module, a classical
hourglass-shaped cost volume aggregate structure is constructed. Ghost-Stereo
achieves a comparable performance than state-of-the-art real-time methods on
several publicly benchmarks, and shows a better generalization ability.
