  Deep neural networks have shown excellent performance in stereo matching
task. Recently CNN-based methods have shown that stereo matching can be
formulated as a supervised learning task. However, less attention is paid on
the fusion of contextual semantic information and details. To tackle this
problem, we propose a network for disparity estimation based on abundant
contextual details and semantic information, called Multi-scale Features
Network (MSFNet). First, we design a new structure to encode rich semantic
information and fine-grained details by fusing multi-scale features. And we
combine the advantages of element-wise addition and concatenation, which is
conducive to merge semantic information with details. Second, a guidance
mechanism is introduced to guide the network to automatically focus more on the
unreliable regions. Third, we formulate the consistency check as an error map,
obtained by the low stage features with fine-grained details. Finally, we adopt
the consistency checking between the left feature and the synthetic left
feature to refine the initial disparity. Experiments on Scene Flow and KITTI
2015 benchmark demonstrated that the proposed method can achieve the
state-of-the-art performance.
