Self-adaptive Multi-scale Aggregation Network for Stereo Matching

Nowadays stereo matching architectures based on convolutional neural network has achieved remarkable performance. However the existing methods still lack the capability to find the correspondence in ill-posed regions. In this paper we present Self-adaptive Multi-scale Aggregation Network (SMA-Net) for stereo matching. First of all, we construct the cost volume through multi-channels group-wise correlation, which divided the features into groups with different number of channels to enhance the ability of measuring the similarities of features from stereo images. Secondly the self-adaptive cost aggregation is used to regularize the two scale cost volumes from different aggregation branches with intermediate supervise. We conduct comprehensive experiments on SceneFlow, KITTI2012, and KITTI2015 datasets. The competitive results prove that the approach in this paper outperforms many other stereo matching algorithms especially in ill-posed regions.