  Recently, there are emerging many stereo matching methods for autonomous
driving based on unsupervised learning. Most of them take advantage of
reconstruction losses to remove dependency on disparity groundtruth. Occlusion
handling is a challenging problem in stereo matching, especially for
unsupervised methods. Previous unsupervised methods failed to take full
advantage of geometry properties in occlusion handling. In this paper, we
introduce an effective way to detect occlusion regions and propose a novel
unsupervised training strategy to deal with occlusion that only uses the
predicted left disparity map, by making use of its geometry features in an
iterative way. In the training process, we regard the predicted left disparity
map as pseudo groundtruth and infer occluded regions using geometry features.
The resulting occlusion mask is then used in either training, post-processing,
or both of them as guidance. Experiments show that our method could deal with
the occlusion problem effectively and significantly outperforms the other
unsupervised methods for stereo matching. Moreover, our occlusion-aware
strategies can be extended to the other stereo methods conveniently and improve
their performances.
