Stereo matching aims to perceive the 3D geometric configuration of scenes and facilitates a variety of computer vision in advanced driver assistance systems (ADAS) applications. Recently, deep convolutional neural networks (CNNs) have shown dramatic performance improvements for computing the matching cost in the stereo matching. However, the performance of CNN-based approaches relies heavily on datasets, requiring a large number of ground truth data which needs tremendous works. To overcome this limitation, we present a novel framework to learn CNNs for matching cost computation in an unsupervised manner. Our method leverages an image domain learning combined with stereo epipolar constraints. By exploiting the correspondence consistency between stereo images, our method selects putative positive samples in each training iteration and utilizes them to train the networks. We further propose a positive sample propagation scheme to leverage additional training samples. Our unsupervised learning method is evaluated with two kinds of network architectures, simple and precise CNNs, and shows comparable performance to that of the state-of-the-art methods including both supervised and unsupervised learning approaches on KITTI, Middlebury, HCI, and Yonsei datasets. This extensive evaluation demonstrates that the proposed learning framework can be applied to deal with various real driving conditions.