Abstract: A system and method for unsupervised stereo matching with surface normal assistance for indoor applications. According to the disclosure, a deep neural network with a feature extraction module, a normal branch, and a disparity branch is disclosed. The extraction module and the normal branch are trained first in a supervised manner for surface normal prediction. The predicted surface normal is then incorporated into the disparity branch, which is trained later in an unsupervised manner for disparity estimation. The latter unsupervised learning approach can reduce our method's dependence on a large amount of ground truth data that is difficult to collect. Experimental results indicate that our proposed method can predict accurate surface normal at textureless regions. With the help of the surface normal, the predicted disparity at these challenging areas is more accurate, which leads to improved quality of stereo matching in indoor scenarios.