  Exiting deep-learning based dense stereo matching methods often rely on
ground-truth disparity maps as the training signals, which are however not
always available in many situations. In this paper, we design a simple
convolutional neural network architecture that is able to learn to compute
dense disparity maps directly from the stereo inputs. Training is performed in
an end-to-end fashion without the need of ground-truth disparity maps. The idea
is to use image warping error (instead of disparity-map residuals) as the loss
function to drive the learning process, aiming to find a depth-map that
minimizes the warping error. While this is a simple concept well-known in
stereo matching, to make it work in a deep-learning framework, many non-trivial
challenges must be overcome, and in this work we provide effective solutions.
Our network is self-adaptive to different unseen imageries as well as to
different camera settings. Experiments on KITTI and Middlebury stereo benchmark
datasets show that our method outperforms many state-of-the-art stereo matching
methods with a margin, and at the same time significantly faster.
