Although the current advances on convolutional networks are outstanding, they mainly depend on extensive computational power, limiting the areas of applications. The latter applies for stereo disparity estimation, where current solutions can barely run on embedded devices. This work shows that it is possible to binarize an end-to-end stereo disparity network, which can be considered a step towards lightweight and potentially faster disparity estimation networks. This work shows the validity of the proposed approach through experimentation in two well-known datasets, sceneflow and kitti2012. The results show that a binary disparity model is possible but at the cost of performance. An EPE of 5.14 and 2.09 is achieved in sceneflow and kitti2012 accordingly.