Stereo matching is one of the low-level visual perception tasks. Currently, two-stage 2D-3D networks and three-stage recurrent networks dominate deep stereo matching. These methods build a cost volume with low-resolution stereo feature maps, which splits the network into a feature net and a matching net. However, the 2D feature map is not uncontrollable, and the low-resolution feature map has lost important matching information. To overcome these problems, we propose the first one-stage 2D-3D deep stereo network, named StereoOne. It has an efficient module that builds a cost volume at image resolution in real-time. The feature extraction and matching are learned in a single 3D network. According to the experiments, the new network can easily surpass the 2D-3D network baseline and it can achieve competitive performance with the state-of-the-art.